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Z13 update

The document summarizes the IBM z Systems z13 mainframe update. Key points include the status of IBM servers and trends in digital disruption driving increased mainframe requirements. The z13 launch is highlighted as enabling lower costs through improvements like simultaneous multithreading and large memory capabilities. Mainframes are described as the platform for the future, processing a growing number of mobile transactions worldwide and supporting a large portion of critical applications.

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12/10/2015 1 Large Systems Update (LSU) 2015 z Systems z13 update Status, Trends and Directions KMD/JN Data Århus, December 8 The Modern Mainframe Redefining Digital Business, …at the brink of the Cognitive Era. Version 5.9 December 2015 January 14, 2015 Announcement Henrik Thorsen, IBM Technical Director Nordic z Systems Platform Leader With credit for certain charts to fellow IBMers Trademarks Notes: Performance is in Internal Throughput Rate (ITR) ratio based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput that any user will experience will vary depending upon considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve throughput improvements equivalent to the performance ratios stated here. IBM hardware products are manufactured from new parts, or new and serviceable used parts. Regardless, our warranty terms apply. All customer examples cited or described in this presentation are presented as illustrations of the manner in which some customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics will vary depending on individual customer configurations and conditions. This publication was produced in the United States. IBM may not offer the products, services or features discussedin this document in other countries, and the information may be subject to change without notice. Consult your local IBM business contact for information on the product or services available in your area. All statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only. Information about non-IBM products is obtained from the manufacturers of those products or their published announcements. IBM has not tested those products and cannot confirm the performance, compatibility, or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. Prices subject to change without notice. Contact your IBM representative or Business Partner for the most current pricing in your geography. This informationprovides only general descriptions of the types and portions of workloads that are eligible for execution on Specialty Engines (e.g, zIIPs, zAAPs, and IFLs) ("SEs"). IBM authorizes customers to use IBM SE only to execute the processing of Eligible Workloads of specific Programs expressly authorized by IBM as specified in the “Authorized Use Table for IBM Machines” provided at www.ibm.com/systems/support/machine_warranties/machine_code/aut.html (“AUT”). No other workload processing is authorized for execution on an SE. IBM offers SE at a lower price than General Processors/CentralProcessors because customers are authorized to use SEs only to process certain types and/or amounts of workloads as specified by IBM in the AUT. * Registered trademarksof IBM Corporation * Other product and service names might be trademarksof IBM or other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. IT Infrastructure Library is a registered trademark of the Central Computer and Telecommunications Agency which is now part of the Office of Government Commerce. 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12/10/2015 2 • The following people “contributed” to this presentation: • Riaz Ahmad, Matthias Bangert, Uno Bengtson, Mario Bezzi, Maria K Boisen, Nick Clayton, • Donna Dillenberg, Martin Dvorsky, Michael Eggloff, Harv Emery, Cindy Grossman, Ray Jones, • Frank Kyne (Watson & Walker), Parwez Hamid, Gerard Laumay, Helene Lyon, Silvia Melitta, • Marianne Menå Heltborg, Frank Packheiser, Ewerson Palacio, Alain Poquillon, • Per Rosenquist, Jørgen Riis Andersen, Jeff Seidell, Harri Stranden, Peter Sommer • Christopher Spaight, Svenn-Aage Sønderskov (BEC), Henrik Thorsen, Robert Vaupel, • Dan Wardman, Charles Webb • ... and many many more ...Irv Gordon (”5 million km Volvo man”), ....Per Groth (”2 Volvo book writer”) Acknowledgements Thanks! 3 Agenda 4 IT world transforming fast, creating extra MF requirements • Consumer, Economic and IT MF perspective in ww, European, Nordic context • Digital disruption, CAMSS, IoT, Cognitive Era IBM servers holistic view – survival of the fittest • IBM divesting 86-based servers, emphasizing z Systems and POWER brands • What about Moore’s law? Silicon Semiconductor update • Innovation much more than semiconductors and HW IBM z Systems z13 enabling lower cost: • Simultaneous Multithreading (SMT) • Single Instruction Multiple Data (SIMD) • Large Memory and Memory Affinity • Significant I/O, Security and other enhancements IBM z13 and Real Time Transaction/Predictive Analytics IBM client value of HW/SW currency IBM z13 Status: • Obervations, tips and tricks, tools, videos • workshops – ITSO, LinuxOne, GSE,...
12/10/2015 3 5 2014 Key IBM Systems Server Milestones 6 2015 Key IBM Systems Server Milestones Systems HW Ongoing Transformation – 2015 Milestones January z13 launch July IBM launch 7 nm chip, world’s smallest: GlobalFoundries and Samsung March 3$B R&D investment in IoT technology LinuxONE Emperor, world’s most advanced Linux system Welcome to the cognitive era IBM CEO Rometty describes new era in technology August -YE October IBM POWER8
12/10/2015 4 7 The 3rd generation of computing platform, the 3rd phase of the Internet, and the explosion of information are colliding to form a perfect storm of disruption and transformation 2020-2 Amount of Data Collected and Stored Adapted from HorizonWatch: Top Technology Trends To Watch In 2013 Source: Bill Chamberlin 1964 1981 1994 2003 2008 2012 Continuum of Computing Platforms Mainframe Client Server / PC Mobile Devices Internet Web 2.0 Phases of the Internet Web 3.0 (Cloud, Analytics, Mobile, Social) IoT 8 The 3rd generation of computing platform, the 3rd phase of the Internet, and the explosion of information are colliding to form a perfect storm of disruption and transformation The Internet of Things – infographic The Connectivist based on Cisco data -2014 Source: Slide Share: Ammar Sabzwari
12/10/2015 5 Eras of Computing 9 By 2018, 1/3rd of leaders in each industry will be disrupted by 3rd platform competitors. The 3rd Platform allows businesses to: • Create greater operational efficiencies • Build deeper relationships with their customers • Create new revenue streams based on technology-enabled products and services The 3rd Platform is a Business Platform 10
12/10/2015 6 Digital Disruption is Real 11 75% of S&P 500 companies may be replaced by 2027! > 60 years < 20 years 1st Platform 2nd Platform 3rd Platform Digital Disruption is Real 12 Everything in This 1991 Radio Shack Ad Has Been Replaced w/ Apps Running on a Smartphone
12/10/2015 7 What drives Mainframe and z13 Growth? Share 2015 Perspective 13 Systems of Insight drives Future Growth 14
12/10/2015 8 Eagle studies validate that adding workload to Mainframes reduces labor cost per unit of work 15 Mobile, Mobile, Mobile! Transactions generate business, MF load, and they are growing 16 Mainframes in an Worldwide context: • Recent past - 10 years ago • Less than 1 transaction a day • Today – right now • Up to 37 transactions a day • Future – 3 years and in 10 years • 2017 50 trans/day • 2025 1.6 Trillion+ trans/day from 10 Billion devices =1.600.000.000.000 transactions 30.000.000.000 MF business transaction today 50% Compound annual growth rate Sources: Juniper, Gartner, Wall Street Journal Japan, IBM PoV
12/10/2015 9 The Platform for the Future IDC says 60% critical apps run on IBM MF today 17 Mainframes in an European context: • 10.000 mobile transactions happened when you read the heading! • Drive 12% of Europe's economy • Generate more than 110,000 jobs • Creates an ecosystem of 1000 partner organizations • €5.6 billion ecosystem revenue • Open architectures and Linux are sizzling 18 20M+ 100B 6x and 3x apps in the world today apps was downloaded in 2014 Google and Apple respectively have released more major Android and iOS versions than Microsoft has released major Windows PC versions Build and Connect System z mobile web, hybrid, and native app development System z data, service and application integration Lifecycle management Building and connecting System z data to mobile devices to provide a better, more-secure customer experience Building and connecting apps to z Systems
12/10/2015 10 The Platform for the Future IDC says 60% critical apps run on IBM MF today 19 Mainframes in a Nordic context: • ~ 90 Mainframes • Surrounded by ~ 900.000+ other servers • ~ 600.000 MIPs • ~ 50% on latest z13 technology (~ 50% YE) • ~ 2% of Worldwide MF server capacity • Growth rate MIPs (5 YR CAGR) ~ 15% • More for specialty engines • Linux on z MIPs (5 YR) ~ 45%! • ~ 1.000.000.000 MF business transactions/day • ~ 50.000 MF business transactions/sec in peak • MFs are vital to Nordic cooperations, our society, you, IBM and myself • Latest version MF z13 announced January 14, 2015 ...continues to carry the torch The Platform for the Future IDC says 60% critical apps run on IBM MF today 20 Mainframes in a Nordic context: • ~ 90 Mainframes • Surrounded by ~ 900.000+ other servers • ~ 600.000 MIPs • ~ 30% on latest z13 technology (~ 50% YE) • ~ 2% of Worldwide MF server capacity • Growth rate MIPs (5 YR CAGR) ~ 15% • More for specialty engines • Linux on z MIPs (5 YR) ~ 45%! • ~ 1-2.000.000.000 MF business transactions/day • ~ 50.000 MF business transactions/sec in peak • MFs are vital to Nordic cooperations, our society, you, IBM and myself • Latest version MF z13 announced January 14, 2015 Mainframes in a Worldwide context:
12/10/2015 11 The Platform for the Future IDC says 60% critical apps run on IBM MF today 21 Mainframes in a Nordic context: • ~ 90 Mainframes • Surrounded by ~ 900.000+ other servers • ~ 600.000 MIPs • ~ 30% on latest z13 technology (~ 50% YE) • ~ 2% of Worldwide MF server capacity • Growth rate MIPs (5 YR CAGR) ~ 15% • More for specialty engines • Linux on z MIPs (5 YR) ~ 45%! • ~ 1-2.000.000.000 MF business transactions/day • ~ 50.000 MF business transactions/sec in peak • MFs are vital to Nordic cooperations, our society, you, IBM and myself • Latest version MF z13 announced January 14, 2015 Market Forces 2014-2018 22 2018 Devices: • 2x growth • 40 billion • 5.0/person 2018 Mobile Users: • 50% growth • 3.8 billion • 50% penetration … yesterday’s infrastructure won’t cut it… 2018 Datacenter Cores: • 2x growth • 77 billion • 10/person 2018 Data: • 3x growth • 24 zettabytes • 1.5 TBs/person/day
12/10/2015 12 IBM z Systems z13 Platform Positioning • The world’s premier data and transaction engine enabled for the mobile generation • The integrated transaction and analytics system for right-time insights at the point of impact • The world’s most efficient and trusted cloud system that transforms the economics of IT Transaction Processing Data Serving Mixed Workloads Operational Efficiency Trusted and Secure Computing Reliable, Available, Resilient Virtually Limitless Scale 2323 Enable superior Cloud services at up to 32% lower cost than x86 Cloud and up to 60% less than Public Cloud over three years Deliver up to 36% better response time, up to 61% better throughput, and 17 to 37% lower cost per mobile transaction Deliver insights up to 17x faster and with 13x better price performance than closest competitor Accelerate speed of encryption up to 2X over the zEC12 to help protect the privacy of data throughout its life cycle Cloud Analytics Mobile Security The all new IBM z13: Excel in Digital Business Against Competition
12/10/2015 13 Innovation Drives Performance 0% 20% 40% 60% 80% 100% 180 nm 130 nm 90 nm 65 nm 45 nm 32 nm 22 nm Gain by Technology Scaling Gain by Innovation Relative % of Improvement 25 • Future growth also comes from dimensions other than hardware thread speed alone: • Software efficiency – extract full performance value from System z hardware – Compiler technology, dynamic optimization, exploit new architectural facilities • Core density – multi-core processor chips – More efficient use of space, less complex packaging • Cache density – leveraging leadership eDRAM technology – Larger caches closer to more cores Combined result: continued growth in system capacity each generation • Sysplex scale – more systems sharing data and workloads • Workload optimization via specialized capabilities Compute Capability Trends 26
12/10/2015 14 Java Semiconductor Technology Microprocessor Design Systems Design Virtualization & Operating Systems Compilers & Java Virtual Machine Optimized Middleware IBM z Systems z13 27 IBM $3B R&D investment in chip technologies over next 5 years IBM is tackling chip challenges by launching two broad research and development programs: 1. 7nm and beyond silicon technology will address serious physical challenges that are threatening current semiconductor scaling techniques 2. Alternative technologies for post-silicon era chips under development: • Silicon Photonics/Nanowire • Silicium Germanium... • (2012 9nm transistor) • (2015 7nm experimental chip capable of holding up to 20 Billion transistors) • Carbon Nanotubes • Graphene • Neurosynaptic Computing • Quantum Computing 28
12/10/2015 15 IBM $3B R&D investment in chip technologies over next 5 years IBM is tackling chip challenges by launching two broad research and development programs: 1. 7NM and beyond silicon technology will address serious physical challenges that are threatening current semiconductor scaling techniques 2. Alternative technologies for post-silicon era chips under development: • Silicon Photonics/Nanowire • Silicium Germanium... • Carbon Nanotubes • (2012 9NM transistor) • (2015 7NM chip)) • Graphene • Neurosynaptic Computing • Quantum Computing - Qubits 29 IBM is tackling chip challenges by launching two broad research and development programs: 1. 7NM and beyond silicon technology will address serious physical challenges that are threatening current semiconductor scaling techniques 2. Alternative technologies for post-silicon era chips under development: • Silicon Photonics/Nanowire • Silicium Germanium... • Carbon Nanotubes • (2012 9NM transistor) • (2015 7NM chip)) • Graphene • Neurosynaptic Computing • Quantum Computing IBM $3B R&D investment in chip technologies over next 5 years 30 5 year development effort, 1B$ and 500 patents/year involved in launch of z13
12/10/2015 16 31 z13 - Refined z Systems Architecture • Performance: How fast a given piece of work can be done? • Throughput: How much work can be done in a given amount of time? • Performance and throughput are the combination of architecture, design innovation and processor frequency (cycle time) • z13 improvements delivered through: – Design innovations such as increased cache sizes, Out of Order instruction processing, restructured pipelines, increased n-way design, etc. – Increasing Uni-processor performance with a reduction in cycle time when compared to zEC12 – Increasing the scale in the system N-way – Introducing Simultaneous Multi Threading (SMT) – Reintroducing SIMD vector processing – Extending memory from 3TB to 10TB Frequency Scaling x86, POWER, z Systems x86 and POWER already transitioned to a throughput-centric model Frequency peaked for x86 in 2005, POWER in 2008 In System z we have held this off with unique cooling, packaging, technology, and design solutions Frequency(MHz) 0 1000 2000 3000 4000 5000 6000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 x86 Power System z
12/10/2015 17 CPU core speed vs Computer Performance Why the overall CPU frequency approach is changing ? ̶ Consistent frequency growth in the past decade • from hundreds of MHz to GHz ̶ CPU frequency has reduced in the past couple of years Designing chips for better performance ̶ Limits are imposed by physics, technology or economics ̶ Controls the rate of improvements in different dimensions ̶ Different processor architectures have different issues with overclocking Physical limitations ̶ Speed of light limits how fast signals travel form one end to the other on a chip ̶ Power and heat dissipation ̶ Cooling ̶ How many memory elements (caches) can be within a given latency from the CPU Physical limitations force the designers to make trade-offs ̶ “Shrinking” a processor chip • pro: Faster due to the shorter distances • con: Reduced area for dissipation Power dissipation increases as the chip speeds up ̶ Raising the processor voltages would make transistors to switch quicker • pro: Frequency could then be increased • con: current also increases creating more heat Sounds easy.. but… it causes serious problems with heat Emerging technologies allow frequency variation according to processing needs CPU Clock speed versus Computer Performance… GHz is not the only dimension that matters ̶ System z focus is on balanced system design across many factors: • Frequency, pipeline, efficiency, energy efficiency, cache/memory design and I/O design • Greater logic density, power density, wire-ability. All permits more cores per chip, larger cache, additional execution units/circuits, addition of SMT and SIMD on each core. System performance is not linear with frequency ̶ Need to use LSPR and System z capacity planning tools for real client / workload sizing System z leverages technologies to get the most out of chips design ̶ Low latency pipelines ̶ Dense packaging with proper cooling which yields more power-efficient operation ̶ Consistent performance at high utilization The IBM z13 Server is a significant change from zEC12 ̶ Processor speed measured in instructions per second (for a given workload) has increased as compared to the zEC12. • Wider pipeline (up to six per cycle) • Enhanced branch prediction • Optimized resolution of dependencies between instructions.
12/10/2015 18 z Systems - Processor Roadmap z196 9/2010 zEC12 8/2012 z10 2/2008 z13 1/2015 Leadership Single Thread, Enhanced Throughput Improved out-of-order Transactional Memory Dynamic Optimization 2 GB page support Step Function in System Capacity Top Tier Single Thread Performance System Capacity Accelerator Integration Out of Order Execution Water Cooling PCIe I/O Fabric RAIM Enhanced Energy Management Leadership System Capacity and Performance Modularity & Scalability Dynamic SMT Supports two instruction threads SIMD PCIe attached accelerators (XML) Business Analytics Optimized Workload Consolidation and Integration Engine for CPU Intensive Workloads Decimal FP Infiniband 64-CP Image Large Pages Shared Memory 8 double-wide cores per CP chip 2X Instruction pipe width – Improves IPC for all modes – Symmetry simplifies dispatch/issue rules – Required for effective SMT Added FXU and BFU execution units – 4 FXUs (Fixed point units) – 2 BFUs (Binary floating-point units) – DFUs (Decimal floating-point units) – 2 new SIMD units SIMD unit plus additional registers Pipe depth re-optimized for power/performance – Product frequency reduced (5.0 GHz) – Processor performance increased SMT2 support – Wide, symmetric pipeline – Full architected state per thread – SMT-adjusted CPU usage metering z13 Processor Overview Enhancements at a glance
12/10/2015 19 IFB ICM LSU L2D (eDRAM) RU IDU ISU XU TU VFU FXU IFB ICM LSU ISU IDU FXU RU L2D L2I XU PC VFU COP Where is the SIMD Unit? Fixed Point Unit • Counters (Loops) • Adresses Vector & Float Unit • Fixed Point Decimal (BCD) • Floating Point HEX • Floating Point BINARY • Floating Point DECIMAL • Vector string processing • Binary vector integer z13 VFU (Vector and Floating-Point Units) • Enhancements – Two execution pipelines & 4x FPU / VFU registers – SIMD & string (new engine) • String processing & vector execution of binary integer – DFX (decimal FXU) • new engine optimized for BCD add/sub/compare • zEC12: these ops executed on DFU – BFU: Binary & Hex floating-point unit • new: Vector support for Binary 64b float – Leading edge Divide/sqrt engine for binary & hex FP • zEC12: divide & square-root executed in the BFU • Shorter latency and higher throughput than zEC12 – DFU: decimal floating-point unit • 3rd generation, allowing for higher throughput • Executes BCD mul, div, convert, shift – All Units are implemented twice – so in the best case this doubles througput compared to zEC12 BFU BFU Divide Divide DFU DFU DFX SIMD & String DFX SIMD & String RegFiles RegFiles zEC12 FPUs Silvia M Mueller, Jan/2015
12/10/2015 20 New Memory Controller Crypto Express5S FICON Express16S 1U Support Element Standalone zBX Node Hybrid Computing 2.7M lines of firmware changed Radiator Design improvements Expanded operating environment (Rear Doors) 22nm Processor with SIMD, SMT Integrated I/O with PCIe Direct Attach Single Chip Modules Drawer-Based CPC Design Cable-Based SMP Fabric Oscillator Backplane Flexible Service Processor (FSP2) Integrated Sparing On-chip power/thermal monitor / control z13 System Design Changes New Memory Controller Crypto Express5S FICON Express16S 1U Support Element Standalone zBX Node Hybrid Computing 2.7M lines of firmware changed Radiator Design improvements Expanded operating environment (Rear Doors) 22nm Processor with SIMD, SMT Integrated I/O with PCIe Direct Attach Single Chip Modules Drawer-Based CPC Design Cable-Based SMP Fabric Oscillator Backplane Flexible Service Processor (FSP2) Integrated Sparing On-chip power/thermal monitor / control z13 System Design Changes
12/10/2015 21 Physical node: (Two per drawer) Chips – Three PU chips (with up to 8 active cores each) – One SC chip (480 MB L4 cache) RAIM Memory – Three Memory Controllers: One per CP Chip – Five DDR3 DIMM slots per Controller: 15 total per logical node – Populated DIMM slots: 20 or 25 per drawer SC and CP Chip Interconnects – X-bus: SC and CPs to each other (intra node) – S-bus: SC to SC chip in the (intra drawer) – A-bus: SC to SC chips in the remote drawers (intra box) Mem PSI Mem GX Bus 2x PCIe GX Bus 2x PCIePUPU SCSC MemMem DIMMs GX Bus 2x PCIe Node 1 GX Bus 2x PCIe GX Bus 2x PCIe GX Bus 2x PCIe Fully Populated Drawer MemMem A-Bus S-Bus X-Bus Node 0 X-Bus SCSC A-Bus To other drawers To other drawers z13 Drawer Structure and Interconnect Drawer Based SMP Topology Node 1 Node 0 Node 1 Node 0 Node 1 Node 0 Node 1 Node 0 Drawer Drawer Drawer Drawer PU PU PU PU PU z13 versus zEC12 HW Comparison … and z196 zEC12 (z196) – CPU • 5.5 GHz (1514 PCI) • Enhanced Out-Of-Order – Caches • L1 private 64k i, 96k d (Same) • L2 private 1 MB i + 1 MB d (1.5 MB) • L3 shared 48 MB / chip (24 MB) • L4 shared 384 MB / book (192 MB) z13 – CPU • 5.0 GHz (1695 PCI) • Major pipeline enhancements – Caches • L1 private 96k i, 128k d • L2 private 2 MB i + 2 MB d • L3 shared 64 MB / chip • L4 shared 480 MB / node - plus 224 MB NIC ... Memory L4 Cache L2 CPU1 L1 L3 Cache L2 CPU6 L1... L2 CPU1 L1 L3 Cache L2 CPU6 L1... ... Memory L4 Cache L2 PU1 L1 L3 Cache ... L2 PU8 L1 L2 PU1 L1 L3 Cache ...L2 PU8 L1 ... Memory L4 Cache L2 PU1 L1 L3 Cache ... L2 PU8 L1 L2 PU1 L1 L3 Cache ...L2 PU8 L1 Single Book View Single Drawer View
12/10/2015 22 z13 Architecture Extensions Core, SIMD, SMT-2 • Core micro-architecture radically altered to increase parallelism – New branch prediction and instruction fetch front end • Supports SMT-2 and improves branch prediction throughput for all modes – Wider instruction decode, dispatch and completion bandwidth: • Up to six instructions per cycle compared to three on zEC12 – Up to ten instructions issued for execution per cycle compared to seven on zEC12 • 2 branch, 4 FXU, 2 LSU, 2 BFU/DFU/SIMD • Single Instruction Multiple Data (SIMD) instruction set and execution: Business Analytics Vector Processing – 139 new instructions operate on 32 new 128-bit registers – String, vector integer and vector floating point operations • Two 64-bit, four 32-bit, eight 16-bit, or 8-bit operands per register • Two-way simultaneous multithreaded (SMT-2) operation – Up to two active execution threads per core • Dynamically share caches, TLBs, and execution resources. • Significant boost in core and chip throughput on top of core performance increase – SMT-2 operation supported for IFLs and zIIPs – Hardware monitoring support for chargeback and capacity planning 43 Simultaneous Multi-threading (SMT) • Simultaneous Multi-threading (SMT) technology • Multiple programs (software threads) run on same processor core • More efficient use of core hardware • Active threads share core resources • In space: data and instruction caches, TLBs, branch history tables, etc. • In time: pipeline slots, execution units, address translator, etc. • Increases overall throughput per core when SMT active • Amount of increase varies widely with workload – typically 1.2-1.6X • Each thread runs more slowly than on a single-thread core A / B A B B Load/Store (L1 Cache) A A B Execution Units (FXU/FPU) instructions A B A A B A A B Shared Cache A B A A B A BA Cache Thread-A Thread-B Use of Pipeline Stages in SMT-2 Both threads Stage idle 44
12/10/2015 23 Simultaneous Multi Threading (SMT2) • Simultaneous multithreading allows instructions from more than one thread to execute in any given pipeline stage at a time • SMT helps address memory latency, resulting in overall throughput gains • It can increase processing efficiency, and throughput • Currently available on IFLs (Linux and z/VM) and zIIPs (z/OS) • The number of concurrent threads is limited to two and can be turned on or off by an operator command and also set up through parmlib for z/OS 45 Which approach is designed for the highest volume of traffic? Which road is faster? Illustrative numbers only Note: SMT is designed to deliver better overall throughput for many workloads. Performance in some cases may be superior using single threading Why only 45? SMT dependency on Job runtime • SMT increases the efficiency of a core. Instead of lets say running 100 instructions per TIME in a single thread, SMT allows running more than 100 instructions in 2 threads in the same TIME. • However, sharing one ressource amoung 2 threads is „not for free“. There is a „cost“ associated. • SMT Effectivness = (SMT Throughput / Single Thread Throughput). – Lets assume we can run 125 instructions in the same timeintervall with SMT switched on we would end up with SMT Eff. = 125 / 100 • The „cost“ is, that both threads run slower than a single thread. • The elongation is calculated as: Elongation % = (2 / SMT Eff.) - 1 – In our example the single threads would run 60% slower than before. • Lets assume your client has a system zEC12 with 10 ZIIPs. – Because of z13‘s single thread performance improvement you only need 9 ZIIPs now – With SMT switched on and 25% efficiency improvement you end up with 7 ZIIPs. – In that case all workload on your ZIIPs will run 60% slower than without SMT and 46% slower than before on zEC12.
12/10/2015 24 Remarks on SMT • For z/OS – You enable SMT in LOAD xx at IPL – Once done, enable/disable SMT per LPAR by dynamically activating different OPT files. • For native Linux – There is no support (yet) in Redhat or Suse Linux for SMT. – This is expected to be available in Q3 2015. • For z/VM – No dynamic switching of SMT modes – Linux running under z/VM can exploit SMT • Throughput improvements dependent on the workload with SMT: – SAP runs very nicely with SMT switched on (large SMT throughput benefit) – Imagine you are running OLTP transactions and time critical Batch Jobs on a single core with SMT switched on – may be less beneficial (Bad Cache lines) Enterprise Level Multithreading Expands capacity without compromising predictability • z13 delivers significant boost in IFL and zIIP performance (throughput and capacity) via simultaneous multithreading (SMT) • Extends per-processor capacity growth beyond single-thread performance • z13 will support 2 threads per core • Design will preserve unique System z values and attributes • Predictable core capacity with precise utilization measurement – Supports chargeback, billing, and capacity planning • SMT enablement independently controlled by LPAR • Operating systems must be explicitly enabled for SMT • Operating system may opt to run in single-thread mode • SMT control coordinated across hardware, hypervisors, software • Operating system running in LPAR controls use of threads in each core – Transparent to the application • Hypervisors can leverage SMT for image consolidation • Functionally transparent to middleware and applications • Each hardware thread has full z/Architecture processor function • No changes required to run in SMT partition 48
12/10/2015 25 49 z13 and Simultaneous Multi Threading • Simultaneous Multi Threading (SMT2) is available on IFLs and zIIPs only (currently) – 10% to 35%+ throughput improvement on top of Uni-processor performance gains • IFLs: 10% to 32% • zIIPs:10% to 40% – Will affect throughput (amount of work that can be done) versus performance (speeding up the IFL / zIIP) • Be sensitive to workloads that need fastest possible single thread performance – Individual work units may run slower due to each thread running slower on an SMT enabled core • Example: If a core can do 1000 MIPS without SMT (one thread) and with SMT turned on runs 2 threads giving 40% more throughput, then the core will deliver 1400 MIPS with SMT. Since the 1400 MIPS is the sum of the two threads, each thread will be running at 1400/2 = 700 MIPS – Workloads, jobs, transactions that are CPU bound and very response time or elapsed time sensitive many not want to exploit the throughput benefits of SMT • Capacity planning tools for SMT – For IFLs: Assume 20% increase in throughput due to SMT – For zIIPs: Assume 25% increase in throughput due to SMT – Capacity planning tools will initially default to 20% throughput improvement for IFLs and 25% throughput improvement for zIIPs – Tools will be updated with the latest information – Remember that SMT throughput is in addition to benefits seen from the faster uni processor • Overall guidance: Processor speed + SMT benefit – IFL: 32% = 10%+ + 20%+ – zIIP: 40% = 10%+ + 25%+ Note: SMT is designed to deliver better overall throughput for many workloads. Performance in some cases may be superior using single threading A3 B3 C3 A2 B2 C2 Scalar SINGLE INSTRUCTION, SINGLE DATA SIMD SINGLE INSTRUCTION, MULTIPLE DATA Instruction is performed for every data element Perform instructions on every element at once Sum and Store C1 C2 C3 A1 B1 A2 B2 A3 B3 INSTRUCTION A1 B1 C1 Sum and Store SIMD (Single Instruction Multiple Data) Increased parallelism to enable analytics processing • Smaller amount of code helps improve execution efficiency • Process elements in parallel enabling more iterations • Supports analytics, compression, cryptography, video/imaging processing • Exploitation by (partial list): – Java8 and C/C++ for z/OS and Linux on System z; GCC for Linux on System z – Enterprise COBOL for z/OS, PL/1 – MASS and ATLAS math libraries from Rational for z/OS and Linux on System z – ILOG-CPLEX, z/OS XML System Services 50
12/10/2015 26 Single Instruction Multiple Data (SIMD) Introduction and background 51 Motivation / Background – The amount of data is increasing exponentially - IT shops need to respond to the diversity of data – Enterprises use traditional integer, floating point data; also string, XML character-based data – It’s becoming more important for customers to do computations, analytics closer to the data Customer Perception of Analytics and System z – System z handles OLTP and Batch; lots of math and changing data is too compute intensive for z Reality of Analytics and System z – For last 2-3 generations, z has changed its capabilities in compute processing space (analytics) • Superscalar, Out of Order (OoO), compiler improvements, floating point • Legacy Capabilities: quad precision floating point, fuse/multiply/add – SIMD provides next phase of enhancements for analytics competitiveness on z SIMD Objective – Leverage data intensity and be competitive with large data volumes; compete by doing more operations on a given byte of data, extract more interesting insight, and turn that insight into customer inspiration Workloads that may benefit from Data Parallelism (SIMD) – High Data Intensity (i.e. data volume) – High Compute Intensity (i.e. operations on a given byte of data) – Predictive IT analytics, Advanced Security/Crypto, BI reporting, Prescriptive Analytics, Next-Gen Data Warehousing Use Cases – Reporting functions: Querying and populating reports, often in batch fashion to process lots of data quickly – Numerically intensive processing • i.e. time forecasting, simulation; ex: Tivoli based analysis with capacity management – Modelers, matrix intensive computations – ILOG CPLEX: optimizations; i.e. delta cruise scheduling, linear programming Instruction pool Data pool Results Instruction pool Data pool Results Single Instruction Multiple Data (SIMD) Vector Processing • When used with provided libraries and compilers: – creates a platform for numeric and data intensive computing, – minimizing effort on the part of middleware/application developers for exploitation. This capability enhances analytics and big data capability of z13 Key enablers: Libraries: MASS, ATLAS Compliers: XLC, Java.Next String processing: Java / Cobol / PL1 string, XMLSS, Cognos Binary Floating Point ILOG, SPSS, analytics, mobile codes OS/Hypervisor Support: z/OS: 2.1 SPE Linux: SLES12 SP1 and RHEL7.2 Workloads Java.Next C/C++Compiler built-ins for SIMD operations (zOS and zLinux) MASS & ATLAS Math Libraries (zOS and zLinux) SIMD Registers and Instruction Set 52
12/10/2015 27 SIMD - Exploitation New z13 assembler instructions which directly use the vector facility (this is not the full list): • VL Vector Load • VLL Vector Load with Length • VSTL Vector Store with Length • VCEQ Vector Compare • VFAE Vector Find Any Element Equal • VFEE Vector Find Element Equal • Using these instructions promises maximum improvements from SIMD • ILOG CPLEX, COBOL Inspect .. Tallying, JAVA8, self written ASM programs All floating point operations will benefit – without change – from the new VFU design (all units are doubled now compared to zEC12). tor Element Rotate and Insert under Mask 53 Java 8 SIMD Vector Engine Exploitation 54
12/10/2015 28 Java Performance on z13 By Java version, HW generation, and w/w/o SMT 30 % increase in Throughput 55 1999 2009 1998 2001 2003 2005 200 7 SDK1.4 1. 31-bit z/OS and 31-bit and 64-bit Linux on z 2. GA 4Q2002 3. z/OS End of Marketing September, 2008 4. z/OS End of Service September, 2011 31-bit and 64-bit SDK 5 1. IBM J9 2.3 VM and JIT Technology 2. z9 Exploitation 3. GA 4Q2005 4. z/OS and Linux on z 31-bit SDK1.1.8 1. OS/390 GA 1999 2. Out of service 31-bit and 64-bit SDK 6 , V6.0.0 1. Supplies Java SE 6 APIs 2. z10 Exploitation 3. IBM J9 2.4 VM and JIT Technology 4. GA 4Q2007 5. z/OS and Linux on z 31-bit SDK1.3.1 1. z/OS and Linux on z 2. GA 3Q2000 3. End of Service: September, 2007 z/OS 64-bit SDK 1.4.2 1. IBM J9 2.2 VM and JIT Technology (1st product use) 2. GA 4Q2004 3. End of Service September, 2008 31-bit SDK1.1.1, then 1.1.4 and 1.1.6 1. First OS/390 Java product – GA 1997 2. Out of service 2014 31-bit and 64-bit z/OS Java SDK 6 V6.0.1 1. Supplies Java SE 6 APIs 2. z196 Exploitation 3. New IBM J9 2.6 VM and JIT Technology 4. Enhanced JZOS and z/OS Security 5. z/OS Java products, GA March 2011: System z Java Product Timeline 31-bit and 64-bit Java SDK 7.x 1. z/OS and Linux on z 2. Supplies Java SE 7 APIs 3. OpenJDK 4. z196/zEC12 Exploitation 5. New IBM J9 2.6/2.7 VM and JIT Technology 6. GA Oct 2011 Testimonials: http://www.ibm.com/software/os/systemz/testimonials/ IBM continues to invest aggressively in Java for System z, demonstrating a rich history of innovation and performance improvements.
12/10/2015 29 STI z990/z890 STI z9 InfiniBand z10/z196/z114/ zEC12/zBC12 STI: Self-Timed Interconnect 6 GBps 2 GBps PCIe Gen2 zEC12/zBC12/ z196/z114 16 GBps System z I/O Subsystem Internal Bus Interconnect Speeds PCIe Gen3 z13 8 GBps 2.7 GBps 57 New FICON Function on z13 • 16 Gbps Link Speeds – Designed to reduce I/O latency to improve response time for performance-critical middleware and to shrink the batch window required to accommodate I/O bound batch work • FICON Dynamic Routing – September 2015 – Designed to allow ISL sharing by FC and FCP traffic to optimize use of ISL bandwidth in the SAN fabric for both types of traffic • SAN Fabric Priority – September 2015 – Extends z/OS WLM policy into the SAN fabric – Gives important work priority to get through SAN traffic congestion (e.g. after SAN hardware failures) • zHPF Extended Distance II – June 2015 – Up to 50% I/O service time improvement for remote write – Designed to help GDPS HyperSwap configurations with secondary DASD in remote site • 32K devices per FICON channel – Up to 85 Logical Partitions: More flexibility for server consolidation • Preserve Virtual WWPNs for NPIV configured FCP channels – Designed to simplify migration to a new-build z13 • Forward Error Correction Codes – September 2015 – Designed to addresses high bit-error rate on high frequency (>= 8Gb/s) links – Estimated equivalence to doubling optical signal power • 6th Logical Channel Subsystem – Up to 85 Logical Partitions: More flexibility for server consolidation • 4th Subchannel Set – Simplifies I/O configurations for a 2nd synchronous copy of data – With multi-target PPRC, can do HyperSwap and still maintain synchronous copy for 2nd HyperSwap58
12/10/2015 30 DS8870 and z13 Improve performance and resiliency for mainframe environments 59 • January 2015 Preview announcement DS8870 • FICON Dynamic Routing – Reduce costs with improved and persistent performance for supporting I/O devices • 16Gb host adapters – Improve network performance with 2x faster FC and FICON adapters; minimize latency for DB2 log writes with zHyperWrite • Forward Error Correction – Preserve data integrity with more redundancy on the information transmitted via 16Gb adapters • zHPF Extended Distance II – Increase remote data speed with 50% better IO performance for remote mirror • Fabric Priority – Improved resiliency capabilities while enhancing the value of FICON Dynamic Routing 1200 14000 31000 20000 52000 23000 23000 92000 110000 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 I/O driver benchmark I/Os per second 4k block size Channel 100% utilized FICON Express4 and FICON Express2 z H P F FICON Express8 z H P F FICON Express8 FICON Express4 and FICON Express2 ESCON z H P F FICON Express8S FICON Express8S z10 z10 z196 z10 z196 z10 zEC12 zBC12 z196,z114 zEC12 zBC12 z196,z114 350 520 620 770 620 620 1600 2600 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 FICON Express4 4 Gbps I/O driver benchmark MB/second Full-duplex Large sequential read/write mix FICON Express4 4 Gbps FICON Express8 8 Gbps FICON Express8 8 Gbps FICON Express8S 8 Gbps FICON Express8S 8 Gbps z10 z10 z196 z10 z196 z10 zEC12 zBC12 z196,z114 z H P F z H P F z H P F zEC12 zBC12 z196,z114 z13 GA1 z H P F FICON Express 16S z13 GA1 FICON Express 16S z13 GA1z13 GA1 FICON Express 16S 16 Gbps FICON Express 16S 16 Gbps z H P F zHPF and FICON Performance z13 GA1 DS8000 enhanced to support FICON16S 63% increase 60 20% increase (Controlled measurement environment, results may vary)
12/10/2015 31 60000 84000 92000 110000 0 20000 40000 60000 80000 100000 120000 I/Os per second Read/writes/mix 4k block size, channel 100% utilized z10 z196, z10 z13 GA1zEC12 zBC12 z196, z114 520 770 1600 2500 0 500 1000 1500 2000 2500 MegaBytes per second (full-duplex) Large sequential Read/write mix z10 z196, z10 56% increase 20% increase FE4 4 Gbps FE8S 8 Gbps zEC12 zBC12 z196, z114 FE8 8 Gbps FE8S 8 Gbps FE16S 16 Gbps FE16S 16 Gbps z13 GA1 FE8 8 GbpsFE4 4 Gbps FCP Performance for z13 GA1 61 FICON Express 16S • 16 GB/sec link speed –QLogic HBA supports 4G / 8G / 16G –New standard 64b / 66b encoding • Improves link data efficiency –HBA code in IBM memory • Simplifies Qlogic code updates • Reduced latency for large block transfers –z/OS DB2 Log writes 12-14% latency reduction in 128K log writes Up to 5-6% improvement in Transaction Latency –z/OS Managed file transfer DS8000 zDDB feature - exchange data through the SAN –Reduced batch window • Forward Error Correction –Addresses higher error rates at 16GB • Sensitivity to damaged / faulty optical cables –Improved fault isolation for optical links –IBM leading standards to enable FEC • Fabric Priority –Cooperative design across adapter / switch / control unit –Each operation given priority by z/OS / WLM • Priority propagated across SAN fabric –Ensures consistent performance for most important work Z13 GA1 (1Q2015) FL SFP+ SFP+ DRAM PG2 PG2 CNA CNA FL CFAM-S SW Storage Card (LX, SX) PG2 ASIC 4/8/16GFC CNA ASIC Universal FC/FICON spare FICON Express16S Card 62
12/10/2015 32 More Memory Makes a Huge Difference Adding more memory can enable you to… – Cut response time – up to 70% reduction for SAP transactions – Achieve faster decision making by leveraging in memory data – Shrink batch windows with no change to applications – Deploy and support more Linux workloads in the same system footprint – Improve system performance (reduced MLC), minimize constraints, and simplify management of applications with database exploitation of additional memory – Get more work done z13 offers up to a Maximum of 10TB (10,000 GB) Up to 2.5 TB per model increment Leadership reliability and availability with RAIM LPAR support of the full memory configured Elimination of the 1TB limit z/OS 2.1 (with SPEs) to support up to 4TB per image z/VM 6.4 to support up to 1TB per VM guest 63 64 Memory Delivers Additional Benefits Go for mega memory… Enable totally new types of applications − Perform faster table scans with in memory data for faster response time; reduce CPU by avoiding IO Simplify Memory capacity planning − Reduce need to fine tune memory Accommodate growing batch workloads − Run sorts using large memory, improving CPU consumption and elapsed times Now easily support new modern computing languages and architectures − Java and other memory intensive languages Customers can see CPU savings − See up to 5% CPU savings with DB2 tuning enabled by Large Memory − See 5% CPU savings for typical workloads, in some cases up to 20% in certain environments, e.g.; when using SAP with DB2 − Your mileage may vary, and is highly depending on buffer pool hit ratios 70GB buffer pools 1MB frames with Page Fixed is the best performer Candidates benefiting from large memory include: Analytics Java DB2 Cognos Indexing Batch LE CF PR/SM 1.5 1.55 1.6 1.65 4K Pagable 4K Fixed 1M Pagable 1M Fixed milli-seconds Total DB2 CPU time per Transaction
12/10/2015 33 z13 PR/SM Role and Capabilities expanded Dynamic Memory Management! • zEC12 and earlier PR/SM controlled relationship between logical and physical CPs. it pseudo-dedicate physical CPs to vertical high logical CPs, and try to keep logical CPs for an LPAR together in 1 book and one chip, to maximize the value of the L3 and L4 cache. PR/SM can change relationship and transparently move logical CPs between physical CPs. • z13 capabilities of PR/SM expands. Not only assigning logical CPs to physical CPs, now also responsible for controlling where the memory for each LPAR will be allocated. And it is now able to not only dynamically move logical CPs between drawers, it can also (transparently) move memory between drawers. • The complex algorithms PR/SM uses to determine best location for each LPAR include considerations, including: – The number and type of logical CPs in LPAR. – The LPAR's weight as % of total weight of all active LPARs (fair share' of overall capacity). – The number of vertical high, vertical medium, and vertical low (“parked” and “un-parked”) CPs in the LPAR. – The amount of LPAR memory and drawer memory. – The number of assigned and unassigned PUs in each chip, node, and drawer. – Many others. 65 IBM z13 Large Memory Client Value from Large Memory • Response Time: Consistent, fast transactional response time drives a series of bottom line benefits for Clients including higher productivity and sales. • Availability Gains: The culture to hoard memory and tune tightly exists. 3X and mega memory help break this. Availability gains show up in (1) how well the system survives I/O disruptions (2) workload tuning to handle spikes and (3) all of the Flash use cases get better (workload startup and failure scenarios). • Application Productivity and Memory Hungry Workloads: Analytics and other large memory users can be placed closer to data. Changes to charge back allocation reflecting the lower cost of memory enable Application Designers' use of caching software layers and other industry performance and productivity technology. • Long Term Architectural Vision: The 3X memory purchase acts as a shock to the system to move the balance point of workloads towards gaining the advantages of large memory usage. This change in vision in an organization is a very significant Client advantage of the 3X and Mega memory pricing approaches. (Controlled measurement environment, results may vary) http://www.redbooks.ibm.com/abstracts/redp5146.html?Open
12/10/2015 34 IBM z13 Large Memory Enabling optimal middleware efficiency • DB2 Buffer pools – By configuring more memory for DB2 buffer pools • up to 3% CPU time savings • up to 10% transaction response time savings in the DB2 portion of their workload – DB2 synchronous I/O simulation tool now available on DB2 11 • Predict expected reduction in sync I/O resulting from a specific increase in the size of a DB2 buffer pool • Estimate CPU savings based on reduction in sync IOs /sec • IMS – Page fix IMS Full Function database buffers • Improve response time for IMS DB accesses from application • Save up to 3% CPU time of the IMS portion of your workload • MQ – Large memory for IBM MQ V8 can help to cost effectively manage the increasing message volumes generated from today's mobile and cloud applications (Controlled measurement environment, results may vary)http://www.redbooks.ibm.com/abstracts/redp5146.html?Open IBM z13 Large Memory Unleashing the Next Wave of Innovation • MDM (InfoSphere) – Right-sizing your group buffer pools • Configure large enough group buffer pools to cache all MDM Tables and indexes – Internal measurements • Increasing group buffer pool size from 120 GB to 565 GB and retaining all MDM tables and indexes in the group buffer pool • Reduced CPU time by 6.8% • Reduced application elapsed time by 18% • Java / WAS – Allow larger Java heaps and more Java instances • Reduce latency and CPU cost • Cognos Dynamic Cubes on z/OS – Data in memory for faster report generation – Scale up from 1 to 3 Cognos instances with – Increasing one COGNOS Instance to 3 instances using 480GB, we measured 95% scale efficiency (Controlled measurement environment, results may vary)http://www.redbooks.ibm.com/abstracts/redp5146.html?Open
12/10/2015 35 IBM z13 Large Memory Demonstrating Customer Value • Ensure that middleware is optimally tuned – Are DB2 buffer pools optimized? • Are the DB2 buffer pools sized correctly? – Customers with DB2 V11 to use DB2 synchronous I/O simulation tool. • Is the customer using fixed large pages for buffer pools? – Is other middleware taking full use of memory (IMS, MQ, MDM)? • There are other opportunities to exploit mega-memory – Benefits from more JVMs, or larger JVM heaps? • WAS applications, Java applications, Java transactions – Other opportunities? http://www.redbooks.ibm.com/abstracts/redp5146.html?Open Large Memory remarks Rethink memory usage on z Systems • With z13 option to include up to 10 TB in the machine, 2.5 TB per drawer. – “Mega memory“ affordable option 1H 2015 • Large memory expolitation? – Assiging the memory to Linux images, as Linux applications often like that. – Assigning memory to DB2 Bufferpools. Good idea, also consider: • Adding more memory to local buffer pools means you need to add memory in the global bufferpools (CF) • If your customer already has high local bufferpol hit ratios, larger bufferpools offer less value. Consider revamping BP layout. – Other middleware smaller benefits from large memory than DB2 (like IMS, CICS, ...) – Java may use large memory – For MQ Series, Version 8 provides 64 bit support • Application programmers should be involved in planning for large memory exploitaion.
12/10/2015 36 Large Memory Measurement Results DB2 and SAP http://www-03.ibm.com/support/techdocs/atsmastr.nsf/WebIndex/WP102461 z13 Extends Scale, Relieves Constraints Advanced system design optimized for digital business * Servers exploit a subset of its designed I/O capability PCI – Processor Capacity Index Customer Processors PCI for 1-way 1695 Memory 80-way 64-way 54-way 1.5 TB 512 GB 1202920600 288 GB/sec* 172.8 GB/sec* 15033 TB 384 GB/Sec* 101-way z10 EC z9 EC z196 zEC12 z13 10 TB 141-way System I/O Bandwidth 832 GB/Sec* 72
12/10/2015 37 Acceleration & Optimization Integration versus time-to-market, • Embedded accelerators • Computation engines for analytics • Assists for dynamic software optimization • Enablement for integrated function • Close collaboration with compiler and other software teams • PCIe-attached accelerators • FPGAs, Field Programmable Gate Arrays • Leverage flexibility for special functions • In-line processing of data entering or leaving system • Off-load specialized data processing • Crypto, FICON, Compression, ROCE, Flash,... • More may come... • Heterogeneous system optimization • Integrate special-purpose appliances into System z workloads • Enhanced system scale and price/performance 73 IBM z13 Designed for Analytics - Summary Accelerate insight and simplify implementation • IBM DB2 Analytics Accelerator accelerates queries for faster insight – New innovative use cases, such as in- database transformation and advanced predictive analytics • Large memory allows new opportunities for in-memory computing – per system & per LPAR • SMT2 for increased zIIP & IFL cores capacity • SIMD delivers accelerated analytics processing for complex queries – Enable vector processing capabilities to z Systems • More – Optimized math libraries and compilers that will speed up and simplify application development – Faster thread speeds – z Enterprise Data Compression (zEDC) to improve the economics of keeping data on z Systems 74 Which approach is designed for the highest volume of traffic? Which road is faster? Illustrative numbers only A3 B3 C3 A2 B2 C2 Scalar SINGLE INSTRUCTION, SINGLE DATA SIMD SINGLE INSTRUCTION, MULTIPLE DATA Instruction is performed for every data element Perform instructions on every element at once Sum and Store C1 C2 C3 A1 B1 A2 B2 A3 B3 INSTRUCTION A1 B1 C1 Sum and Store
12/10/2015 38 Analytics as part of the flow of business; Insights on every transaction • Purchase made • Resources consumed • Bill paid • Claim submitted • Information updated • Call center contacted • What happened? • How many, how often, where? • What actions are needed? • What will happen if? • What will produce the best outcome? Transactions & Analytics processed together Made possible by using LARGE memory - in addition to SIMD and SMT 75 What’s changing? Data is the new basis of competitive advantage Data is the world’s newest resource Decision-making extends from few to many As data value grows, current systems won’t keep pace 76
12/10/2015 39 77 What is Big data? Google can give you nearly 2 Billion options Vendors have even more definitions Here is how Gartner defines Big Data Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative information processing for enhanced insight and decision making. Big Data Growth is Astounding… The four V’s of Big Data 1 in 2 business leaders don’’’’t have access to data they need 83% of CIO’’’’s cited BI and analytics as part of their visionary plan 54% of companies use analytics for competitive advantage 80% of the world’s data today is unstructured 90% of the world’s data was created in the last two years 20% is the amount of available data traditional systems leverages Source: GigaOM, Software Group, IBM Institute for Business Value“, “Volume, Velocity, Variety, Veracity” 78
12/10/2015 40 Extracting insight from an immense volume, variety and velocity of data, in context, beyond what was previously possible Big Data includes any of the following Characteristics Manage the complexity of data in many different structures, ranging from relational, to logs, to raw text Streaming data and large volume data movement Scale from Terabytes to Petabytes (1K TBs) to Zettabytes (1B TBs) Establish trust as the number of data sources grows Variety: Velocity: Volume: Veracity: Create IT AgilityManage RiskOutperform Why Act Now? Only 1 in 5 organizations allocate more than 50% of IT budget to new projects Of leaders cite growth as the key source of value from analytics Source: 1 - IBM IBV Study: Analytics: A blueprint for value, October 2013 2 - IBM Global Study on the Economic Impact of IT Risk, 2013 3 - IBM Global Data Center Study, 2012 Of respondents were impacted by a cyber security breach over the past 24 months 46%75% 1in5
12/10/2015 41 Analytics is becoming the Keystone of every organization … • Analytics derive insight from data – To help optimize business performance – To build new innovative services – To fight against fraud – To make all customer interaction personal! … • Analytics become Business Critical! – Analytics services are tightly integrated with business critical applications and data • Often hosted in z/OS transaction and batch systems • Often relying on copies or aggregation of transaction and application data – Analytics is part of the flow of the business. – Decision processes have to be improved with new business insight derived from real time or near real time data. – Failure of these applications for any length of time can result in lost business or reputation. – Analytics solutions need to support a large concurrent user population with high volumes of requests. • Analytics are only as good as the underlying data foundation – Data governance & Security & Performance 81 … but IT remains aligned to the old way of doing business analytics. • Some reluctances from the past – Core business is primary, analytics is secondary! • On core business side: High volume transactions and batch processing running concurrently, shared everything DB • On analytics side: Low volume complex queries and batch reporting, shared nothing DB – Cost of running analytics on z … without looking at all hidden costs concerning data movement – latency, data governance, IT complexity – Impact on operational performance & security • Key drivers to change IT perception – Awareness of z position as primary Systems of Records – Technology availability to build a fully-integrated, end-to-end system that executes intelligent business processes – Recognized business value of advanced real time analytics – Business leaders brainstorm to identify to rethink business process with HTAP influence • Gartner Research Note G00259033 28 January 2014: Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation Insights on every transaction Analytics as part of the flow of business Analytics close to the data z Systems Transactions Analytics 8282
12/10/2015 42 Data Moved Analytics Approaches for Mainframe Data DATA Transaction Data Customer Data Account Data Payment Data Claims Data System z Host System z/OS Systems of Record Orchestrate Processing Predictive Scoring Business Rules Move the data to the analytics Performance of critical transactions may not meet SLAs due to data movement Customer needs to create security infrastructures across multiple servers Customer needs to create audit infrastructure across multiple servers to ensure governance Customer needs to create availability and DR function for multi-server transaction flows and in- transit data Move the analytics to the data, and within the transaction Unparalleled, proven performance execution for models and rules, with NO or seamless data movement Leverage existing best of class security with System z infrastructure Leverage existing transaction level auditing and logging for governance Leverage existing, tested HA / DR capabilities already configured with System z Network Network Data Moved Data Moved Network DATA Transaction Data Customer Data Account Data Payment Data Claims Data Orchestrate Processing Predictive Scoring Business Rules System z/OS Systems of Record System z Host 83 System z Mainframe CP(s) z/OS IFL … z/VM S MF Linux for System z IBM InfoSphere BigInsights HDFS MapReduce, Hbase, Hive IFL IFL DB2 VSAM IMS Logs System z Connector For Hadoop System z Connector For Hadoop Simplified data transfer from z/OS to Hadoop IBM InfoSphere System z Connector for Hadoop Easily extract data from mainframe sources Purpose built for Hadoop Complementary to other IBM tools: IBM MQ FTE, DataStage Fast, secure data transfer Interactive or batch Supports popular Hadoop distributions IBM BigInsights Cloudera CDH Hortonworks HDP
12/10/2015 43 Operational Data Store Enterprise Data Warehouse Analytics Accelerator What is happening? What happened? What is likely to happen and what do I do about it? OLTP Transactions Operational analytics Real time data ingestion High concurrency Advanced analytics Standard reports Complex queries Historical queries OLAP Integrated Transformation/Warehousing Single DB2 z/OS Data Sharing Group Big Data Accelerator Accelerated Reporting Real-Time Predictive and Prescriptive Analytics Customer Interaction Data In Business Insight Out z Systems Hybrid Transaction and Analytic Processing Everything is online – Analytics in the Right Place 85 Why did it happen? Hybrid transaction/Analytical processing The hybrid computing platform on z Systems Supports transaction processing and analytics workloads concurrently, efficiently and cost-effectively Delivers industry leading performance for mixed workloads The unique heterogeneous scale- out platform in the industry Superior availability, reliability and security Transaction Processing Analytics Workload
12/10/2015 44 • Business agility through simplified architecture with in-database transformation and multi-step processing • Real-time, actionable business processes through in-database analytics • Insight into now to maximize business opportunities through enterprise Incremental Update enhancements Announcing DB2 Analytics Accelerator Version 5.1 Enabling real-time analytic solutions on a single, integrated system combining transactional data, historical data and predictive analytics • Extended security through encryption of data at rest and in motion while taking advantage of the renowned built-in security of z Systems • Enriched systems management capabilities and improved serviceability through IBM Call Home Strategy Enable DB2 transition into a truly universal DBMS that provides best characteristics for both OLTP and analytical workloads. Complement DB2's industry leading transactional processing capabilities Provide specialized access path for data intensive queries Enable real and near-real time analytics processing Execute transparency to the applications Operate as an integral part of DB2 and z Systems Reusing industry leading PDA's query and analytics capabilities and take advantage of future enhancements Extend query acceleration to new, innovative usage cases, such as: – in-database transformations – advanced analytical capabilities – multi-temperature and storage saving solutions Ultimately allow consolidation and unification of transactional and analytical data stores DB2 for z/OS In-database Transformation Query Accelerator Storage Saver OLTP Advanced Analytics
12/10/2015 45 IBM DB2 Analytics Accelerator Do things you could never do before! • What is it? – The IBM DB2 Analytics Accelerator is a workload optimized, appliance add- on to DB2 for z/OS, that enables the integration of analytic insights into operational processes to drive business critical analytics and exceptional business value • What does it do? – Accelerates complex queries, up to 2000x faster – Lowers the cost of storing, managing and processing historical data – Minimizes latency – Reduces z Systems capacity requirements – Improves security and governance – Reduces operational costs and risk – Complements existing investments 89 IBM DB2 Analytics Accelerator Product Components OSA- Express4 10 GbE CLIENT Data Studio Foundation DB2 Analytics Accelerator Admin Plug-in z Systems Data Warehouse application DB2 for z/OS enabled for IBM DB2 Analytics Accelerator IBM DB2 Analytics Accelerator Netezza Technology (PDA) Users/ Applications Note: There are several connection options using switches to increase redundancy Dedicated highly available network connection 90
12/10/2015 46 Customer Table ~ 5 Billion Rows 300 Mixed Workload Queries Times Faster Query Total Rows Reviewed Total Rows Returned Hours Sec(s) Hours Sec(s) Query 1 2,813,571 853,320 2:39 9,540 0.0 5 1,908 Query 2 2,813,571 585,780 2:16 8,220 0.0 5 1,644 Query 3 8,260,214 274 1:16 4,560 0.0 6 760 Query 4 2,813,571 601,197 1:08 4,080 0.0 5 816 Query 5 3,422,765 508 0:57 4,080 0.0 70 58 Query 6 4,290,648 165 0:53 3,180 0.0 6 530 Query 7 361,521 58,236 0:51 3,120 0.0 4 780 Query 8 3,425.29 724 0:44 2,640 0.0 2 1,320 Query 9 4,130,107 137 0:42 2,520 0.1 193 13 DB2 Only DB2 with IDAA 270 of the Mixed Workload Queries Executes in DB2 returning results in seconds or sub- seconds 30 of the Mixed Workload Queries took minutes to hours Successfully accelerated the problem queries without affecting the rest Customer “A” Example 91 Without accelerator With accelerator Customer Example – LPAR CPU utilization comparison with and without IDAA 92
12/10/2015 47 Analytic needs are expanding from enterprise data to big data 5 Facts to consider to ensure success 1 Organizations are using analytics to outperform their competition 2 More users across the organization want access to analytics, woven into the fabric of the business 3 Analytics is business critical and demands low latency, high qualities of service and performance 4 Spreading analytic components across multiple departments can increase data latency, cost, complexity and governance risk 5 Bringing analytic components to where data originates improves data governance while minimizing data latency, cost and complexity 93 Five Business Critical Analytics Use Cases Big Data Exploration Find, visualize, understand all big data to improve business knowledge Enhanced 360o View of the Customer Achieve a true unified view, incorporating internal and external sources Operations Insight Analyze a variety of machine data for improved business results. Data Warehouse Augmentation Integrate big data and data warehouse capabilities to increase operational efficiency Security/Intelligence Extension Risk, compliance and counter fraud detection. z13 can deliver real time analytics and scale up to meet future demand 94
12/10/2015 48 More Users across the Organization want access to Business Critical Analytics 95 The “Big Data” Starting Point Where are organizations getting the most return on Big Data projects? 0 10 20 30 40 50 60 70 80 Audio Video Others Images Geospatial data Free-form text Social media data E-mails or documents Machine or sensor data Log data Transactions Percentage of respondents Gartner research note “Survey Analysis - Big Data Adoption in 2013 Shows Substance Behind the Hype“ Sept 12 2013 Analyst(s): Lisa Kart, Nick Heudecker, Frank Buytendijk N=465, multiple responses allowed Most big data initiatives involve transactional or log-file data 96
12/10/2015 49 Unfortunately for most of our clients, their data lifecycle is too fragmented to gain advantage from that data • Significant complexity – Data is move from operational databases to separated data warehouses/data marts to support analytics • Analytics latency – Transactional data is not readily or easily available for analytics when created • Lack of synchronization – Data is not easily aggregated and users are not assured they have access to “fresh” data • Data duplication – Multiple copies of the same data is proliferated throughout the organization • Excessive costs – An IT infrastructure that was not designed nor can support real-time analytics Historical Data Predictive Data Transaction Data 97 Business Critical Analytics Systems with IBM z Systems An Hybrid Vision Bring analytics to the data Reduced latency Reduced complexity Reduced cost Deliver business critical analytics Timely, accurate, secure data Availability, scalability, performance Rapid deployment & expansion Evolve with the business Start with your top analytic requirement(s) Grow without changing customer existing IT environment Business System OLTP & Batch Business Critical Analytics Improved business performance out Transactions in Data Transformati on Data Warehousing Minimize latency. Improve performance. Drive innovation. • Purchase made • Resources consumed • Bill paid • Claim submitted • Information updated • Call center contacted • What happened? • How many, how often, where? • What actions are needed? • What will happen if? • What will produce the best outcome? 98
12/10/2015 50 On average, 70% of the data that feeds data warehousing and business analytics solutions originates on the System z platform (financial information, customer lists, personal records, manufacturing…) DB2 Analytics Accelerator – Four Usage Scenarios Understand your workload and data: Where transaction source data is being analyzed today Use Case Benefits If the data is analyzed on the mainframe Rapid Acceleration of Business Critical Queries Performance improvements and cost reduction while retaining System z security and reliability If the data is offloaded to a distributed data warehouse or data mart Reduce IT Sprawl for analytics Simplify and consolidate complex infrastructures, low latency, reliability, security and TCO If the data is not being analyzed yet Derive business insight from z/OS transaction systems One integrated, hybrid platform, optimized to run mixed workload. Simplicity and time to value If the analysis is based on a lot of historical data Improve access to historical data and lower storage costs Performance improvements and cost reduction 1 2 3 4 99 System z Point of View Building a foundation to grow with business needs 10 0 Why z13? 3X larger memory enables in-memory analytics for faster insight MASS libraries can see a 2X to 10X improvement making it advantageous to port x86 analytic workloads CPLEX on z/OS exploitation of SIMD instructions provides up to 80% improvement complex modeling Add real-time scoring to your OLTP workload with minimal impact on CPU consumption zIIP exploitation of SMT2 Linux exploitation of SMT2 Why System z Currency of data Reduce complexity Bring analytic function to the data Improve synchronization Eliminate data duplication
12/10/2015 51 101 Systems of Record Systems of Record are well integrated and mostly complete Order Fulfillment Corporate Data Ware- house Accounting Finance z/OS Systems of Engagement Systems of Engagement are disconnected, piece parts Internet of Things (e.g., RFID) Mobile Apps Social Channels Siloed Dept. Apps SaaS/ Cloud Apps Linux on System z Campaign Mgmt App Other Dept. Apps System z Bridging Systems of Record with Systems of Engagement 10 2 z13 Potential Cost Reduction Use Cases: HW/SW “currency” Summary Workload Options Performance Improvement (estimation) Potential Cost Reduction (estimation) SAP with DB2 on z/OS zHW+zSW currency + more memory 10%-18% +20% 10% 30% CICS/DB2 banking zHW+zSW currency + more memory 10%-15% +5% 10% 15% WAS on z/OS e.g. internet banking zHW+zSW currency + SMT + more memory 10%-12% +25% +5% 5% 5% 15% SPSS predictive analytics zHW+zSW currency + more memory + SIMD 10%-18% +5% tbd 10% 15% tbd Traditional Batch processing zHW+zSW currency + more memory + SIMD 10% +5% tbd 10% 15% tbd Linux consolidation on IFL e.g. Private Cloud zHW+zSW currency + SMT + more memory + GDPS 15% +20% tbd D/R improvement 15% 35% tbd tbd Mobile zHW+zSW currency + more memory 10% +5% 5% 15% All performance information was determined in a controlled environment. Results may vary. Sample output from new version of z13 Benefits Estimator tool
12/10/2015 52 L1 miss Instrs 1 2 3 4 5 6 7 Time In-order core execution L1 miss Time z196 Out-of-order core execution L1 miss zEC12 Out-of-order core execution Time Improved overlapping opportunities Execution Storage access Dependency Out of Order Execution z10, z196 vs zEC12/zBC12 12/10/2015 zBC12/zEC12 Cache Topology L1 64KI + 96KD 8w (D) / 4w (I) Set Associative L2 Private 1+1MB Inclusive of L1s; new split 2nd level cache design L3 Shared 48MB Inclusive of L2s 12w Set Associative L4 384/192MB Inclusive 24w Set Associative zBC12/zEC12 4 L4 Caches 384/192MB Shared eDRAM L4 L1 64KI + 128KD 8w (D) / 4w (I) Set Associative L2 Private 1.5MB Inclusive of L1s 12w Set Associative L3 Shared 24MB Inclusive of L2s 12w Set Associative L4 192MB Inclusive 24w Set Associativez114/z196 4 L4 Caches 192MB Shared eDRAM L4 6 L3s, 24 L1 / L2s L2 L1 24MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 24MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 48MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 48MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 6 L3s, 36 L1 / L2s
12/10/2015 53 Data Compression Acceleration High Speed Communication Fabric Proactive Systems Health Analytics SSD Flash Exploitation Hybrid Computing Enhancements Reduce CP consumption, free up storage & speed cross platform data exchange Optimize server to server networking with reduced latency and lower CPU overhead Increase availability by detecting unusual system behavior for faster problem determination and resolution Improve availability at critical times like market open or during abnormal situations; New coupling & Linux exploitation x86 blade resource optimization; New alert & notification for blade virtual servers; Expanding future roadmap zEDC Express 10GbE RoCE Express IBM zAware IBM Flash Express zBX Mod 003; zManager Automate; EAM zEnterprise compilers (COBOL, PL/I, C/C++), Run time monitoring and Transactional Memory provide an optimized application infrastructure for increased software performance Innovations available on zBC12 and zEC12 w zEC12/ zBC12 w zEC12 zBC12 w zEC12 zBC12 w zEC12 zBC12 General Performance Disclaimers MSU Ratings MIPS Tables IBM System z Capacity Planning in a nutshell Please do not use “single-number tables” for capacity comparisons
12/10/2015 54 General Performance Disclaimers IBM System z Capacity Planning in a nutshell Please do not use “single-number tables” for capacity comparisons Preparing for z13: Why CPU MF Important? Collect SMF 113, Calculate RNI, use zPCR • z13 provides lower single thread improvements than previous processor changes, e.g. zEC12 versus z196 • z13 provides more variability in capacity improvement – Capacity projections and expectations should be reasonably accurate – Relative Nest Intensity (RNI) is a metric describing access to various cache levels of the processor architecture
12/10/2015 55 Although System z servers are famous for their scalability, CPU consumed/Trans is a function of total load on system as this real life Nordic client case illustrates Approx. 3-4% growth in CPU/transaction for each 10% growth in CPU busy Increasing Data and Compute Requirements 11 0 Mathematical Optimization Next Best Action What will happen next? Business Intelligence- Reporting Big Sort Small Sort Small String Ops Large String Ops Big Scan Traditional IT Analytics Predictive IT Analytics Data Intensity Small Matrix Math Large Matrix Math ComputeIntensity Integer Ops Floating- Point Ops String Ops FP, String, Int String, Int Traditional Algorithms Competitive Algorithms Data Warehousing (DW) Next- Generation DW Small Compress Large Compress Security/ Crypto Adv Security/ Crypto
12/10/2015 56 11 1 PerformanceGain HW upgrade SIMD and other instructions Large Memory SW currency z13 Performance and relevance in marketplace Transparent Transformational Cobol 5 HLASM New SIMD Instructions 2 GB Pages Java 8 DB2 V11 + Large Memory Reduce Paging with larger Memory MQ Series V8 + Large Memory z13 vs. zEC12 Performance XMLSS exploiting SIMD SMT Throughput gain (zIIPs and IFLs) New Floating Point Unit exploitation Processor Performance Single engine performance function Drawer layout zEC12 z/OS 2.1 z13 z/OS 2.1 MIPS MSU MIPS MSU Mips Delta Percent 2827-701 1514 188 2964-701 1695 210 181 12 2827-702 2853 352 2964-702 3196 394 343 12 2827-703 4151 511 2964-703 4644 571 493 12 2827-704 5409 664 2964-704 6041 740 632 12 2827-705 6628 813 2964-705 7392 905 764 12 2827-706 7809 957 2964-706 8700 1062 891 11 2827-707 8954 1092 2964-707 9964 1212 1010 11 2827-708 10063 1224 2964-708 11188 1356 1125 11 2827-709 11137 1350 2964-709 12371 1496 1234 11 2827-710 12179 1473 2964-710 13515 1632 1336 11 2827-711 13188 1593 2964-711 14622 1764 1434 11 2827-712 14166 1709 2964-712 15693 1891 1527 11 2827-713 15126 1822 2964-713 16729 2011 1603 11 2827-714 16069 1934 2964-714 17731 2129 1662 10 2827-715 16994 2043 2964-715 18700 2244 1706 10 2827-716 17904 2149 2964-716 19665 2358 1761 10 2827-717 18797 2254 2964-717 20624 2472 1827 10 2827-718 19673 2359 2964-718 21579 2584 1906 10 2827-719 20534 2462 2964-719 22529 2695 1995 10 2827-720 21380 2564 2964-720 23475 2801 2095 10 2827-721 22222 2661 2964-721 24415 2905 2193 10 2827-722 23061 2755 2964-722 25351 3009 2290 10 2827-723 23896 2848 2964-723 26282 3111 2386 10 2827-724 24728 2940 2964-724 27209 3212 2481 10 2827-725 25557 3032 2964-725 28130 3313 2573 10 2827-726 26382 3122 2964-726 29048 3414 2666 10 2827-727 27205 3212 2964-727 29960 3516 2755 10 2827-728 28023 3301 2964-728 30868 3619 2845 10 2827-729 28839 3389 2964-729 31772 3725 2933 10 2827-730 29651 3480 2964-730 32671 3830 3020 10 Same CHIP Same NODE, different Chip Different NODE or different Drawer
12/10/2015 57 To MIPS or not to MIPS that is z question • You can not/should not just convert MIPS from zEC12 (or older machines) to z13. Hollistic“ view performance needed to make a prediction on what machine you need. • Collecting data from existing CEC is „strongly recommended“ – HW Instrumentation Services (HIS) is support built into the z/OS operating system to allow customers to capture CPU MF information, both counter data and sample data. – Please use the tool from Patrice Megard to analyse the data. – Use the HIS data also as „feed“ for zPCR. – This is also important to have a detailed documentation about the status quo before migrating to a new machine. • Native Linux does neither support SMT2 nor SIMD until 3Q 2015. Please keep that in mind when sizing machines for Linux for system z workloads. • You need to involve Software People in the performance discussions. Their job is to do an assessment for (list is incomplete): – Java 8 improvements due to SIMD. – Cobol 5.2 Compiler improvements and project evaluation with the client. – Large memory analysis and exploitation activity definition with the client. – Check that SMT can be exploited as planned. z13 Sizing Methodology 1. Collect HIS data from the peak hours of the last month (Software AND Hardware) Please use the IBM supplied tool to analyse the data. Use the HIS data also as „feed“ for zPCR. Collect the SMF70 records from the same time intervall 2. Collect CP3000 data from one full week (incl. WE). It must be the week where the 4HRA is also included (you get this from the SCRT report) -> Ask your SW rep to provide it to you. Once you have the data you need, analyse what workload is running in the 4HRA and what effect a migration to z13 would have to this particular workload. 3. Use zPCR to get a first proposal of the new HW size. This is dependent on the HW PEAK you have – not the SW peak (the SW peak is a 4hour rolling AVERAGE). For a first iteration on sizing, leave out SMT benefits. 4. After you analyzed the HIS and zCP3000 data do a workshop with IBM about z13 and its capabilities and dependencies. Make sure you involve Software IBMers as they need to explain Java, Cobol, PL1 etc. Improvements of z13.
12/10/2015 58 2001 20052000 2008 2010 20152003 Capacity comparison based on LSPR & IBM zPCR tool * Average SMT benefit on z13 Linux Performance Evolution TCO improvement through server generations, and SODs +58% +10-12% +33% +61% +36% 20152012 +20-32%* +26% 1 IFL on all Systems = 120 PVUs KVM support for Linux on System z SODGDPS support for Linux on System z SOD (1H 2015) Source: 2014 IBM Market Intelligence, Percentage of survey respondents Recommended Linux Workloads for Linux on z Systems • Data services: Cognos®, SPSS®, DB2®, InfoSphere™, Informix®, Oracle Database, IBI WebFOCUS, … • Business applications: WebSphere Application Server, WebSphere Process Server, Oracle Application Server, … • Development and test: WebSphere®/Java applications – Rational® Asset Manager, Build Forge®, ClearCase®, Quality Manager • Email and collaboration: IBM Domino®, IBM Connections, IBM SameTime, WebSphere Portal, … • Enterprise Content Management: FileNet® Content Manager, Content Manager, Content Manager On Demand • Business Process Management: Business Process Manager, WebSphere Business Monitor, FileNet Business Process Manager, WebSphere Operational Decision Management, … • Infrastructure services: WebSphere MQ, WebSphere Message Broker, WebSphere Enterprise Service Bus, DB2 Connect™, FTP, NFS, DNS, Firewall, Proxy, … • Cloud management: Infrastructure (IaaS), Platform (PaaS), Software (SaaS), Business Process as a Service – Tivoli® System Automation Manager, Tivoli Provisioning Manager, Integrated Service Management for System z, Maximo® Asset Management, … • Print (Ubiquitech)
12/10/2015 59 Before Enterprise Linux Servers 2*15 IFL = 30 Oracle RAC EE Licenses 380 cores = 285 Oracle RAC EE Licensesfrom to Nordic customer - Oracle consolidation SOD: zKVM – An Open Hypervisor for zEnterprise Open • Open Source based Virtualization • Open Source with Enterprise scale capabilities • Accelerate adoption Linux on System z Cloud • Standards-based Cloud enablement • OpenStack Efficient • KVM already used by existing users by FIEs and MSPs Use the tools you know and use today • Puppet, Chef, Heat, Knife, Moab • Home Grown Scripts in Perl , Ruby , Java…. KVM – Optimized for System z System z Host z CPU, Memory and IO Support Element PR/SM™ . . . z/TFP z/OSz/OS LinuxonSysz z/OSz/OS LinuxonSysz LinuxonSysz LinuxonSys z KVMz/VM Modern Open Standard Simple LinuxonSysz A new non disruptive hypervisor choice for the mainframe z13, zEC12, zBC12 supported KVM as an additional choice to run existing and new Linux centric workload on zEnterprise in parallel to your existing z/VM virtualization environment and z/OS 11 8
12/10/2015 60 Introducing the z13 Removing constraints on growth through innovation Greater Workload Performance, Capacity and Scale in the same energy footprint 40% more total capacity 40% more configurable cores (up to 141 vs 101) New vector facility (SIMD) for faster mathematical computation Up to 6 instructions per cycle (double that of zEC12) 3+ x more memory to reduce latency (10 TB vs 3 TB) New multithreading (SMT2) to expand IFL (Linux) and zIIP capacity 40% more LPARs to securely host more cloud tenants (85 vs 60) z13: An innovative, intelligent and integrated system that provides a trusted foundation for sustainable growth today and in the future Better Economics, Flexibility and Efficiency Almost 50% increase in granularity to fine tune system usage and cost 4x data access with zEDC Standalone zBX support for more flexibility New resilient IO Infrastructure addresses Skills, Complexity, Cost and Availability Price Performance gains for Linux, zIIP, mobile and new SW workloads Investment protection with full upgradeability from z196 and zEC12 Resilient and Secure Growth Highest level of Security (PR/SM EAL5+) Next Generation Hardware Cryptography Best System z RAS with integrated sparing Much enhanced hypervisor 11 9 Focused on Enterprise Linux Extending Linux to wider audience with LinuxONE, Linux/KVM (SOD)* Continuous data availability for z/OS and Linux guests under z/VM with new GDPS Appliance (SOD)* Faster diagnosis with IBM zAware – now extended to Linux on z Systems IBM zAware Support for Linux on System z 12 0 • Linux on System z system logs can now be analyzed by IBM zAware • Upgraded analytics engine for better results on z/OS analysis • Upgraded internal database for improved RAS • Completely rewritten UI, including heat map views HiperSockets ™ OSA (for data from other servers) LPAR z13 Host 1 IBM zAware Partition Web Server Analytics z/OS operlog LOGGER Data Transport Linux on System z HiperSockets ™ OSA (for data from other servers) LPAR zServer Host 2 z/OS operlog LOGGER Data Transport z/OS operlog LOGGER Data Transport syslog Results Models Data Retrieval Manage zAware Firmware partition (similar to CF) File System IBM zAware GUI Persistent Storage Control IBM zAware-specific knobs View IBM zAware results SE zAware Partition Shipped as firmware with z13 zVM
12/10/2015 61 z Aware Heat Map – All systems in a group enhanced zAware interface z Systems z13 Model Overview Processors CP, IFL, zIIP, ICF, optional SAP Specialty engines: ‒ IFL and zIIP exploitation of SMT-2 ‒ 2:1 zIIP to CP ratio on a core basis ‒ zAAP eliminated as per SOD 3 Sub capacity levels for up to a 30 way Full support of Capacity on Demand Features 85 LPARs, increased from 60 Memory Maximum 10 TB / 2.5 TB per drawer RAIM LPAR support of the full memory enabled I/O Up to 5 PCIe I/O drawers ‒ FICON Exp8S, OSA-Exp4S, OSA Exp5S, 10Gb RoCE, ‒ FICON 16S Up to 2 Legacy I/O Drawers ‒ Up to16 FICON 8 features ‒ Carry forward only Crypto Exp4S, Crypto Exp5S, zEDC, zFlash Express Environmental Enhanced integrated sparing Directional setting of rear exhaust airflow 1 U rack mountable HMC option zEC12 z196 Model # PUs Max Memory NE1 141 10 TB NC9 129 10 TB N96 96 7.5 TB N63 63 5 TB N30 30 2.5 TB Investment protection strategy supported by: Announced upgrade paths from z196 and zEC12 N-2 Sysplex co-existence (backwards and forwards) Ensemble co-existence Node based zBX mod 004 eliminates tight coupling to CEC 12 2
12/10/2015 62 Prior Servers z9 EC Z10 EC z196 zEC12 z13 Unscheduled Outages Scheduled Outages Planned Outages Preplanning requirements Power/Thermal Management Application Availability zAware Flash IT Analytics System z overall RAS Strategy: Never Rest Design objective: Continuous end-to-end availability 12 3 © 2015 IBM Corporation 124 Operating Systems focused on exploiting Hardware innovation z/OS V2.1 and 2.2 • Improved price performance for zIIP workloads with SMT • Support new analytics workloads with SIMD and large memory • Digitally sign audit records to reduce risk • Improved operations agility with entitled z/OSMF z/VM® V6.3 • Improved price performance with SMT– support for twice as many processors • Improved systems management and economics • Embracing Open Standards and Open Source Interoperability • Supports more virtual servers than any other platform in a single footprint (up to 8000) Linux on z Systems • Multithreading allows for per core software savings • Starting with z/VM support today • Linux distributions expected shortly • Ability to host and manage more workloads efficiently and cost-effectively • Automatic identification of unusual messages (zAware) • Integrated continuous availability & disaster recovery solution (GDPS SOD) • KVM Support - SOD
12/10/2015 63 Supported Operating Systems Operation System Supported Levels z/OS • z/OS V2.1 with PTFs (exploitation) • z/OS V1R13 with PTFs (coexistance) • z/OS V1R12 with PTFs (Toleration. Life cycle extension required) z/VM • z/VM 6.3 (exploitation) • z/VM 6.2 (toleration) z/VSE • z/VSE 5.1 and 5.2 will support z13 GA1 in compatibility mode with PTFs • z/VSE 5.2 (with PTFs) will exploit Crypto Express5s zTPF • z/TPF 1.1 Linux1 • SLES 11 and SLES 12 • RHEL 6 and RHEL 7 1The intention is to support n and n-1 Linux distribution releases depending on how their product cycle rolls out. The majority of z13 exploitation will be made available with the major distribution releases following System zNext general availability. IBM intends to make selected exploitation items available on selected distributions and work with its Linux distribution partners to make these available A Complete Workload-Optimized System Integration of operations and business-critical analytics into one streamlined, end-to- end data lifecycle 126 Transact Transform Analyze Report Better business response Reduced data movement, reduced complexity, reduced configuration resources More accurate, more secure, more available CUSTOMER INTERACTION DATA IN BUSINESS INSIGHTS OUT
12/10/2015 64 Sources of Information IBM DB2 Analytics Accelerator – Primary Product Page – Prerequisites and Maintenance – Guides and manuals – Knowledge Center Customer Testimonials – https://engage.vevent.com/index.jsp?eid=556&seid=68284&code=brand Redbooks and Redpapers – Reliability and Performance with IBM DB2 Analytics Accelerator Version 4.1 – Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS – Hybrid Analytics Solutions using IBM DB2 Analytics Accelerator for z/OS V3.1 – IBM DB2 Analytics Accelerator: High Availability and Disaster Recovery – SAP Integration with IBM DB2 Analytics Accelerator for z/OS All TechDocs available at the following link. 127
12/10/2015 65 Eight major trends that will affect the industry in coming years Business Services Hardware Software Technology Services Security Through 2016, the financial impact of cybercrime will grow 10% per year, due to the continuing discovery of new vulnerabilities Growth Markets By 2015, IDC expects emerging markets to generate over 33% of all IT spending Big Data Through 2015, more than 85% of Fortune 500 organizations will fail to effectively exploit big data for competitive advantage Analytics Through 2015, more than 90% of business leaders contend information is a strategic asset, yet fewer than 10% will quantify its economic value Social Business By 2014, 20% of business users will replace email as the primary interpersonal communications with social networking Smarter Planet Over $100 billion: Global investment in technology to support smart city development by 2020 Mobile Enterprise 66% of CIOs ranked mobility as a top investment priority in 2012 Cloud Economic benefits of cloud will continue to be the #1 driver of adoption through 2016 for most companies.* Strategic Market Trends Spring 2012 Customer Sets *Source: IDC, IDC's CloudTrack 2012 Summer Survey, Part 1: Cost Savings in the Cloud, November 14, 2012 Business Initiatives % 10% 20% 30% 40% 50% 60% 130 Q. In 2015, which of the following business initiatives will be significant in driving IT investments at your organization? N=242 Increase Productivity Reduce Costs Improve Business Processes Increase Revenue Introduce new Products & Services Increase Agility Improve Customer Retention Source: IDC IT Experience Survey, January 2015
12/10/2015 66 Power & Cooling Mgmt & Administration New Server Spending Why OPEX Matters IT Efficiency? IT Effectiveness? 131 1995 2000 2005 2010 2015 OPEX/CAPEX Ratio 0.48 0.77 1.51 2.83 3.91 $94B $123B $138B $206B $272B WW Server, P&C and Administration Spending 3x 8x 2009 800,000 petabytes 2020 35 zettabytes as much Data and Content Over Coming Decade 44x Business leaders frequently make decisions based on information they don’t trust, or don’t have 1in3 83% of CIOs cited “Business intelligence and analytics” as part of their visionary plans to enhance competitiveness Business leaders say they don’’’’t have access to the information they need to do their jobs 1in2 of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions 60% Of world’s data is unstructured 80% Big Data Big Value to enterprise and society The resulting explosion of information (plus intermediate data) creates a need for a new kind of intelligence Kilobyte (kB) 1,000 Bytes Megabyte (MB) 1,000 Kilobytes Gigabyte (GB) 1,000 Megabytes Terabyte (TB) 1,000 Gigabytes Petabyte (PB) 1,000 Terabytes Exabyte (EB) 1,000 Petabytes Zettabyte (ZB) 1,000 Exabytes
12/10/2015 67 Conclusion • There is no shortage of Big Problems that require Big Data • The Nature of Data in IT is changing. • Volume – Data doubling every two years • Variety – Heading to a trillion devices; Unstructured data; • Velocity – Sometimes all you have is milliseconds to respond • Veracity – My business, finances, safety, health, life depend on • Not all Big Data systems are created equal even if the datasheet says they are! 13 4 Big Data Big Data Big Data allows to bring together all kinds of data on one single platform
12/10/2015 68 135 Unstructured Data Documents (Word, Excel, PDF, TXT, etc.) Social Media Data Facebook, Twitter, YouTube, Internet, etc. Multimedia Data Films, Music, Pictures, etc. Events / Streams Live-Cam, Exchange-Trigger, Microphone, etc. Operational Data Policy, Claim, Underwriting, General Ledger, CRM, etc. Big Data on z Big Data allows to bring together all kinds of data on one single z13 platform Most of the structured data used today resides there already, and extracting golden nuggets from unstructructured data has never been easier 136 Big Data Analytics allows various kinds of Analytics on one single z13 platform Real time, predictive and in-transaction. Bring analytics to the data. Analytics on Databases BigSheets, reports, dashboards, etc. on unstructured data Operational Analytics Reports, dashbords, etc. on operational data (i.e. legacy systems) Business Analytics Reports, dashbords, etc. on dispositive Data (i.e. Data Warehouse) Predictive Analysis What-if-Analysis, Cluster-Analysis, etc. Visualization/Discovery Search, connection and visualization of data of different datasources and –types with one application Realtime Analytics Dashboards, reports of events and streamed data
12/10/2015 69 Top 5 Use Cases for z13 Risk, compliance and counter fraud detection z13 can deliver real time analytics and scale up to meet future demand Operational Insight z13 can deliver customer insight and next-best-action with a competitive TCO Payment z13 is the industry-proven platform for realtime payment and transaction management Cloud, API management and Linux z13 can deliver the required scaleability for private clouds, API management platforms and critical Linux-based workload Mobile z13 can scale up to meet the high-volume transactions demand from mobile YouTube z13 Nordic Videos 13 8 https://www.youtube.com/watch?v=YuytuCEYIUg https://www.youtube.com/watch?v=sl8VKXiatjM https://www.youtube.com/watch?v=Bi4-0guBbOI
12/10/2015 70 How many times have you used a Mainframe today? IBM Mainframe: Make the Extraordinary Possible http://www.youtube.com/watch?v=0tgt4VSrPso Mainframe 50 IBM Mainframe: Make the Extraordinary Possible http://www.youtube.com/watch?v=x6MpJL9XBlU
12/10/2015 71 Server Consolidation on System z http://www.youtube.com/watch?v=mMsvTR0x334 z13: Installations Status per October 2015 500+ thus far worldwide (15+ Nordic) – Most installations, migrations and upgrades went smooth w/o problems – What we see: • Most installation are within -1% to +8% of the zPCR modelled expectation • Expectation is ~10% better ITR than zEC12 – Plan upgrade carefully • So far few installations experience some problems with the migration: – Less compared to previous significant processor design changes (for example z10) – But enough to warrant planning considerations
12/10/2015 72 Matthias R. Bangert, Executive IT- Specialist IBM Europe, z Systems November 14, 2015 Exploiting IBM z13 to the utmost (these results were NOT measured in an “IBM controlled environment” – it is CUSTOMER PRODUCTION data) • Java 7 to Java 8 migration: - 25% CPU consumption • DB2 V10 to V11 migration: - 15% CPU consumption • SIMD usage in Assembler programs: - 30% CPU consumption • Cobol 4 to 5.2 migration (project started): - 20% CPU consumption (expected) • MQ Series V7 to V8 migration: up to 50% less batch elapsed time • SMT exploitation for ZIIPs: + 20% capacity • Large memory exploitation: VSAM/RLS, DB2 Buffer Pools, MQ Series V8 The benefits this particular client got out of their z13 clearly shows, that a holistic approach to gain the most out of z13 is not optional – it is mandatory. It also confirms the results the z13 Benefit Estimator tool is calculating. IBM LinuxONE In Action… IBM LinuxOne In Action: Scalable Financial Trading https://www.youtube.com/watch?v=VWBNoIwGEjo “I demonstrate the new IBM LinuxONE system for scalable financial trading at the LinuxCon 2015 conference. The demo shows multiple data loads (live data from the S&P 500 and Tweets) streaming via Maria DB, MongoDB, Spark Analytics, Chef, Docker and PostgreSQL. In this LinuxONE demo, even with drastic upticks in CPU Utilization during the Greek financial crisis, response times are still lightning fast”.
12/10/2015 73 14 5 14 6
12/10/2015 74 z13 Planning: What else can be done? • Plan your z13 LPARs carefully – Why is this important? • We will see that the LPAR layout matters – That means LPAR weight, # of logical processors and the resulting numbers of vertical high, medium and low processors (VH, VM, VL) • A good starting point here is the LPAR Design tool available from the WLM homepage: http://www-03.ibm.com/systems/z/os/zos/features/wlm/WLM_Further_Info_Tools.html#Design • What does it do? – It allows you to layout your LPARs – Examine the VH, VM, VL processors – Provides guidelines on how to set up the LPARs efficiently Preparation for z13: CPU MF • Use the CPU Measurement Facility (Hardware Instrumentation Sampling) to obtain insight into the processor and cache architecture • Value of CPU Measurement Facility (CPU MF) – Recommended methodology for successful z Systems processor capacity planning • Need on “Before” processor to determine LSPR workload – Validate achieved z Systems processor performance • Needed on “Before” and “After” processors – Provide insights for workload pattern, behavior, new features and functions • Continuously running on all LPARs Capturing CPU MF data is an industry “Best Practice”
12/10/2015 75 Preparation for z13: CPU MF … • Introduced in z10 and later processors • Facility that provides hardware instrumentation data for production systems • Two major components – Counters – for capacity planning • Cache and memory hierarchy information • SCPs supported include z/OS and zVM – Sampling – for detailed, module level analysis • z/OS HIS started task – Gathered on an LPAR basis – Writes SMF 113 records • z/VM Monitor Records – Gathered on an LPAR basis – all guests are aggregated – Writes new Domain 5 (Processor) Records 13 (CPU MF Counters) records • Minimal Overhead A plug for Cheryl Watson’s Tuning Letter: User experiences... Frank’s Viewpoint... zIIP Capacity Planning... Prep for VSAM/RLS... z13 Performance... Software pricing workshops 150
12/10/2015 76 Cheryl Watson Tuning Letter article on z13 performance and related items – 2015 no. 2 151 Large Systems Update (LSU) 2015 • Stockholm 9-10 Nov • Oslo 11-12 Nov • Helsinki 16-17 Nov • Copenhagen 18-19 Nov • http://www-03.ibm.com/systems/no/lsu2015/index.html 152
12/10/2015 77 Other Nordic z Systems events 4Q 2015 Free of charge – enroll now • Stormaskin Forum 2015 – Capgemini - Oslo Tuesday 29. october at 15.00 - 20.30 • 2015 ITSO 3 full day Workshops in Stockholm and Copenhagen IBM z/OS Technical Update, WRZ22G IBM z/OS Performance and Availability Topics, WRZ23G IBM z/OS Networking Technologies Update, WRZ28G – Denmark: Copenhagen 30.11-2.12 – Sweden: Stockholm 2-4.12 http://www.redbooks.ibm.com/redbooks.nsf/home?ReadForm&page=workshops • LinuxONE ½ day Workshop in Stockholm December 15 – In cooperation with MariaDB LinuxONE Announcement Overview - Stefan Ekman IBM IBM’s Commitment to Opensource - Utz Bacher IBM MariaDB – Anders Karlsson MariaDB KVM Using KVM to as hypervisor to host 1000’s of servers - Tony Gargya IBM Docker – Utz Bacher IBM -- Sweden: Stockholm 15.12153 This year’s ITSO Presenters Straight from the horse’s own mouth 154
12/10/2015 78 © 2015 IBM Corporation 155 Nordic Mainframe Entry Education Activities Detailed skills surveys, communities, universities/clients/IBM joint programmes • 1H 2015 • DTU Netbank case (Denmark) – Guest Education BEC, JN Data, IBM - 14 students enrolled (growing) • DTU High Availability (Denmark) • Client trainee programmes at many clients (with IBM sparrring) • 2H 2015 • HIG (Norway). An introduction to the modern Mainframe – Guest Education DNB, NAV, IBM - 25 students enrolled (growing) • IBM conduct ”entry MF training also for managers & senior people • 2H 2015 • IBM looking at restoring ”MVS Hissen” for Nordic and Swedish clients. Previous activity with KTH, Stockholm University and Olsen Education (Cobol). • 2H 2015 • Student track on Nordic GSE (Gothenburg this year) • IBM Hursley Lab: zGen community mtg (hosted by Danske Bank) • ... GSE Nordic Region Conference 2016 GUIDE SHARE EUROPE Hotel Reykjavik Nature – Reykjavik, Iceland June 1-5, 2016 156
12/10/2015 79 GUIDE SHARE EUROPE GSE Nordic Region Conference 2016 Hotel Reykjavik Nature – Reykjavik, Iceland from June 1st - 5th 2016 • 6 Tracks with 70 highly technical and educational sessions on – DB2,CICS,IMS,Mainframe Infrastructure, Application Development and Architecture • All of the presentations are brought to you by Users and professional speakers • You will be networking with people with professional background • We take care of you from flight take off at home until you are safely returned home • The Conference is organized by Professionals from Bankdata (DK), DNB(NO), EVRY (NO), Volvo (SE), Tieto (FI), CSC(DK), JN Data (DK) and IBM (UK/DK). • The Program is available now. Visit WWW.GSE-NORDIC.ORG to view and register. • Remember that GSE members get’s a 20% discount on IBM Conferences/training. Visit www.gse.org and register through there to receive the discount. 158

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Z13 update

  • 1.
    12/10/2015 1 Large Systems Update(LSU) 2015 z Systems z13 update Status, Trends and Directions KMD/JN Data Århus, December 8 The Modern Mainframe Redefining Digital Business, …at the brink of the Cognitive Era. Version 5.9 December 2015 January 14, 2015 Announcement Henrik Thorsen, IBM Technical Director Nordic z Systems Platform Leader With credit for certain charts to fellow IBMers Trademarks Notes: Performance is in Internal Throughput Rate (ITR) ratio based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput that any user will experience will vary depending upon considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve throughput improvements equivalent to the performance ratios stated here. IBM hardware products are manufactured from new parts, or new and serviceable used parts. Regardless, our warranty terms apply. All customer examples cited or described in this presentation are presented as illustrations of the manner in which some customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics will vary depending on individual customer configurations and conditions. This publication was produced in the United States. IBM may not offer the products, services or features discussedin this document in other countries, and the information may be subject to change without notice. Consult your local IBM business contact for information on the product or services available in your area. All statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only. Information about non-IBM products is obtained from the manufacturers of those products or their published announcements. IBM has not tested those products and cannot confirm the performance, compatibility, or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. Prices subject to change without notice. Contact your IBM representative or Business Partner for the most current pricing in your geography. This informationprovides only general descriptions of the types and portions of workloads that are eligible for execution on Specialty Engines (e.g, zIIPs, zAAPs, and IFLs) ("SEs"). IBM authorizes customers to use IBM SE only to execute the processing of Eligible Workloads of specific Programs expressly authorized by IBM as specified in the “Authorized Use Table for IBM Machines” provided at www.ibm.com/systems/support/machine_warranties/machine_code/aut.html (“AUT”). No other workload processing is authorized for execution on an SE. IBM offers SE at a lower price than General Processors/CentralProcessors because customers are authorized to use SEs only to process certain types and/or amounts of workloads as specified by IBM in the AUT. * Registered trademarksof IBM Corporation * Other product and service names might be trademarksof IBM or other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. IT Infrastructure Library is a registered trademark of the Central Computer and Telecommunications Agency which is now part of the Office of Government Commerce. Intel, Intel logo, Intel Inside, Intel Inside logo, Intel Centrino, Intel Centrino logo, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both. Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. Windows Server and the Windows logo are trademarks of the Microsoft group of countries. ITIL is a registered trademark, and a registered community trademark of the Office of Government Commerce, and is registered in the U.S. Patent and Trademark Office. UNIX is a registered trademark of The Open Group in the United States and other countries. Java and all Java based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Linear Tape-Open, LTO, the LTO Logo, Ultrium, and the Ultrium logo are trademarks of HP, IBM Corp. and Quantum in the U.S. and other countries. The following are trademarks of the International Business Machines Corporation in the United States and/or other countries. The following are trademarks or registered trademarks of other companies.
  • 2.
    12/10/2015 2 • The followingpeople “contributed” to this presentation: • Riaz Ahmad, Matthias Bangert, Uno Bengtson, Mario Bezzi, Maria K Boisen, Nick Clayton, • Donna Dillenberg, Martin Dvorsky, Michael Eggloff, Harv Emery, Cindy Grossman, Ray Jones, • Frank Kyne (Watson & Walker), Parwez Hamid, Gerard Laumay, Helene Lyon, Silvia Melitta, • Marianne Menå Heltborg, Frank Packheiser, Ewerson Palacio, Alain Poquillon, • Per Rosenquist, Jørgen Riis Andersen, Jeff Seidell, Harri Stranden, Peter Sommer • Christopher Spaight, Svenn-Aage Sønderskov (BEC), Henrik Thorsen, Robert Vaupel, • Dan Wardman, Charles Webb • ... and many many more ...Irv Gordon (”5 million km Volvo man”), ....Per Groth (”2 Volvo book writer”) Acknowledgements Thanks! 3 Agenda 4 IT world transforming fast, creating extra MF requirements • Consumer, Economic and IT MF perspective in ww, European, Nordic context • Digital disruption, CAMSS, IoT, Cognitive Era IBM servers holistic view – survival of the fittest • IBM divesting 86-based servers, emphasizing z Systems and POWER brands • What about Moore’s law? Silicon Semiconductor update • Innovation much more than semiconductors and HW IBM z Systems z13 enabling lower cost: • Simultaneous Multithreading (SMT) • Single Instruction Multiple Data (SIMD) • Large Memory and Memory Affinity • Significant I/O, Security and other enhancements IBM z13 and Real Time Transaction/Predictive Analytics IBM client value of HW/SW currency IBM z13 Status: • Obervations, tips and tricks, tools, videos • workshops – ITSO, LinuxOne, GSE,...
  • 3.
    12/10/2015 3 5 2014 Key IBMSystems Server Milestones 6 2015 Key IBM Systems Server Milestones Systems HW Ongoing Transformation – 2015 Milestones January z13 launch July IBM launch 7 nm chip, world’s smallest: GlobalFoundries and Samsung March 3$B R&D investment in IoT technology LinuxONE Emperor, world’s most advanced Linux system Welcome to the cognitive era IBM CEO Rometty describes new era in technology August -YE October IBM POWER8
  • 4.
    12/10/2015 4 7 The 3rd generationof computing platform, the 3rd phase of the Internet, and the explosion of information are colliding to form a perfect storm of disruption and transformation 2020-2 Amount of Data Collected and Stored Adapted from HorizonWatch: Top Technology Trends To Watch In 2013 Source: Bill Chamberlin 1964 1981 1994 2003 2008 2012 Continuum of Computing Platforms Mainframe Client Server / PC Mobile Devices Internet Web 2.0 Phases of the Internet Web 3.0 (Cloud, Analytics, Mobile, Social) IoT 8 The 3rd generation of computing platform, the 3rd phase of the Internet, and the explosion of information are colliding to form a perfect storm of disruption and transformation The Internet of Things – infographic The Connectivist based on Cisco data -2014 Source: Slide Share: Ammar Sabzwari
  • 5.
    12/10/2015 5 Eras of Computing 9 By2018, 1/3rd of leaders in each industry will be disrupted by 3rd platform competitors. The 3rd Platform allows businesses to: • Create greater operational efficiencies • Build deeper relationships with their customers • Create new revenue streams based on technology-enabled products and services The 3rd Platform is a Business Platform 10
  • 6.
    12/10/2015 6 Digital Disruption isReal 11 75% of S&P 500 companies may be replaced by 2027! > 60 years < 20 years 1st Platform 2nd Platform 3rd Platform Digital Disruption is Real 12 Everything in This 1991 Radio Shack Ad Has Been Replaced w/ Apps Running on a Smartphone
  • 7.
    12/10/2015 7 What drives Mainframeand z13 Growth? Share 2015 Perspective 13 Systems of Insight drives Future Growth 14
  • 8.
    12/10/2015 8 Eagle studies validatethat adding workload to Mainframes reduces labor cost per unit of work 15 Mobile, Mobile, Mobile! Transactions generate business, MF load, and they are growing 16 Mainframes in an Worldwide context: • Recent past - 10 years ago • Less than 1 transaction a day • Today – right now • Up to 37 transactions a day • Future – 3 years and in 10 years • 2017 50 trans/day • 2025 1.6 Trillion+ trans/day from 10 Billion devices =1.600.000.000.000 transactions 30.000.000.000 MF business transaction today 50% Compound annual growth rate Sources: Juniper, Gartner, Wall Street Journal Japan, IBM PoV
  • 9.
    12/10/2015 9 The Platform forthe Future IDC says 60% critical apps run on IBM MF today 17 Mainframes in an European context: • 10.000 mobile transactions happened when you read the heading! • Drive 12% of Europe's economy • Generate more than 110,000 jobs • Creates an ecosystem of 1000 partner organizations • €5.6 billion ecosystem revenue • Open architectures and Linux are sizzling 18 20M+ 100B 6x and 3x apps in the world today apps was downloaded in 2014 Google and Apple respectively have released more major Android and iOS versions than Microsoft has released major Windows PC versions Build and Connect System z mobile web, hybrid, and native app development System z data, service and application integration Lifecycle management Building and connecting System z data to mobile devices to provide a better, more-secure customer experience Building and connecting apps to z Systems
  • 10.
    12/10/2015 10 The Platform forthe Future IDC says 60% critical apps run on IBM MF today 19 Mainframes in a Nordic context: • ~ 90 Mainframes • Surrounded by ~ 900.000+ other servers • ~ 600.000 MIPs • ~ 50% on latest z13 technology (~ 50% YE) • ~ 2% of Worldwide MF server capacity • Growth rate MIPs (5 YR CAGR) ~ 15% • More for specialty engines • Linux on z MIPs (5 YR) ~ 45%! • ~ 1.000.000.000 MF business transactions/day • ~ 50.000 MF business transactions/sec in peak • MFs are vital to Nordic cooperations, our society, you, IBM and myself • Latest version MF z13 announced January 14, 2015 ...continues to carry the torch The Platform for the Future IDC says 60% critical apps run on IBM MF today 20 Mainframes in a Nordic context: • ~ 90 Mainframes • Surrounded by ~ 900.000+ other servers • ~ 600.000 MIPs • ~ 30% on latest z13 technology (~ 50% YE) • ~ 2% of Worldwide MF server capacity • Growth rate MIPs (5 YR CAGR) ~ 15% • More for specialty engines • Linux on z MIPs (5 YR) ~ 45%! • ~ 1-2.000.000.000 MF business transactions/day • ~ 50.000 MF business transactions/sec in peak • MFs are vital to Nordic cooperations, our society, you, IBM and myself • Latest version MF z13 announced January 14, 2015 Mainframes in a Worldwide context:
  • 11.
    12/10/2015 11 The Platform forthe Future IDC says 60% critical apps run on IBM MF today 21 Mainframes in a Nordic context: • ~ 90 Mainframes • Surrounded by ~ 900.000+ other servers • ~ 600.000 MIPs • ~ 30% on latest z13 technology (~ 50% YE) • ~ 2% of Worldwide MF server capacity • Growth rate MIPs (5 YR CAGR) ~ 15% • More for specialty engines • Linux on z MIPs (5 YR) ~ 45%! • ~ 1-2.000.000.000 MF business transactions/day • ~ 50.000 MF business transactions/sec in peak • MFs are vital to Nordic cooperations, our society, you, IBM and myself • Latest version MF z13 announced January 14, 2015 Market Forces 2014-2018 22 2018 Devices: • 2x growth • 40 billion • 5.0/person 2018 Mobile Users: • 50% growth • 3.8 billion • 50% penetration … yesterday’s infrastructure won’t cut it… 2018 Datacenter Cores: • 2x growth • 77 billion • 10/person 2018 Data: • 3x growth • 24 zettabytes • 1.5 TBs/person/day
  • 12.
    12/10/2015 12 IBM z Systemsz13 Platform Positioning • The world’s premier data and transaction engine enabled for the mobile generation • The integrated transaction and analytics system for right-time insights at the point of impact • The world’s most efficient and trusted cloud system that transforms the economics of IT Transaction Processing Data Serving Mixed Workloads Operational Efficiency Trusted and Secure Computing Reliable, Available, Resilient Virtually Limitless Scale 2323 Enable superior Cloud services at up to 32% lower cost than x86 Cloud and up to 60% less than Public Cloud over three years Deliver up to 36% better response time, up to 61% better throughput, and 17 to 37% lower cost per mobile transaction Deliver insights up to 17x faster and with 13x better price performance than closest competitor Accelerate speed of encryption up to 2X over the zEC12 to help protect the privacy of data throughout its life cycle Cloud Analytics Mobile Security The all new IBM z13: Excel in Digital Business Against Competition
  • 13.
    12/10/2015 13 Innovation Drives Performance 0% 20% 40% 60% 80% 100% 180nm 130 nm 90 nm 65 nm 45 nm 32 nm 22 nm Gain by Technology Scaling Gain by Innovation Relative % of Improvement 25 • Future growth also comes from dimensions other than hardware thread speed alone: • Software efficiency – extract full performance value from System z hardware – Compiler technology, dynamic optimization, exploit new architectural facilities • Core density – multi-core processor chips – More efficient use of space, less complex packaging • Cache density – leveraging leadership eDRAM technology – Larger caches closer to more cores Combined result: continued growth in system capacity each generation • Sysplex scale – more systems sharing data and workloads • Workload optimization via specialized capabilities Compute Capability Trends 26
  • 14.
    12/10/2015 14 Java Semiconductor Technology Microprocessor Design Systems Design Virtualization & Operating Systems Compilers & JavaVirtual Machine Optimized Middleware IBM z Systems z13 27 IBM $3B R&D investment in chip technologies over next 5 years IBM is tackling chip challenges by launching two broad research and development programs: 1. 7nm and beyond silicon technology will address serious physical challenges that are threatening current semiconductor scaling techniques 2. Alternative technologies for post-silicon era chips under development: • Silicon Photonics/Nanowire • Silicium Germanium... • (2012 9nm transistor) • (2015 7nm experimental chip capable of holding up to 20 Billion transistors) • Carbon Nanotubes • Graphene • Neurosynaptic Computing • Quantum Computing 28
  • 15.
    12/10/2015 15 IBM $3B R&Dinvestment in chip technologies over next 5 years IBM is tackling chip challenges by launching two broad research and development programs: 1. 7NM and beyond silicon technology will address serious physical challenges that are threatening current semiconductor scaling techniques 2. Alternative technologies for post-silicon era chips under development: • Silicon Photonics/Nanowire • Silicium Germanium... • Carbon Nanotubes • (2012 9NM transistor) • (2015 7NM chip)) • Graphene • Neurosynaptic Computing • Quantum Computing - Qubits 29 IBM is tackling chip challenges by launching two broad research and development programs: 1. 7NM and beyond silicon technology will address serious physical challenges that are threatening current semiconductor scaling techniques 2. Alternative technologies for post-silicon era chips under development: • Silicon Photonics/Nanowire • Silicium Germanium... • Carbon Nanotubes • (2012 9NM transistor) • (2015 7NM chip)) • Graphene • Neurosynaptic Computing • Quantum Computing IBM $3B R&D investment in chip technologies over next 5 years 30 5 year development effort, 1B$ and 500 patents/year involved in launch of z13
  • 16.
    12/10/2015 16 31 z13 - Refinedz Systems Architecture • Performance: How fast a given piece of work can be done? • Throughput: How much work can be done in a given amount of time? • Performance and throughput are the combination of architecture, design innovation and processor frequency (cycle time) • z13 improvements delivered through: – Design innovations such as increased cache sizes, Out of Order instruction processing, restructured pipelines, increased n-way design, etc. – Increasing Uni-processor performance with a reduction in cycle time when compared to zEC12 – Increasing the scale in the system N-way – Introducing Simultaneous Multi Threading (SMT) – Reintroducing SIMD vector processing – Extending memory from 3TB to 10TB Frequency Scaling x86, POWER, z Systems x86 and POWER already transitioned to a throughput-centric model Frequency peaked for x86 in 2005, POWER in 2008 In System z we have held this off with unique cooling, packaging, technology, and design solutions Frequency(MHz) 0 1000 2000 3000 4000 5000 6000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 x86 Power System z
  • 17.
    12/10/2015 17 CPU core speedvs Computer Performance Why the overall CPU frequency approach is changing ? ̶ Consistent frequency growth in the past decade • from hundreds of MHz to GHz ̶ CPU frequency has reduced in the past couple of years Designing chips for better performance ̶ Limits are imposed by physics, technology or economics ̶ Controls the rate of improvements in different dimensions ̶ Different processor architectures have different issues with overclocking Physical limitations ̶ Speed of light limits how fast signals travel form one end to the other on a chip ̶ Power and heat dissipation ̶ Cooling ̶ How many memory elements (caches) can be within a given latency from the CPU Physical limitations force the designers to make trade-offs ̶ “Shrinking” a processor chip • pro: Faster due to the shorter distances • con: Reduced area for dissipation Power dissipation increases as the chip speeds up ̶ Raising the processor voltages would make transistors to switch quicker • pro: Frequency could then be increased • con: current also increases creating more heat Sounds easy.. but… it causes serious problems with heat Emerging technologies allow frequency variation according to processing needs CPU Clock speed versus Computer Performance… GHz is not the only dimension that matters ̶ System z focus is on balanced system design across many factors: • Frequency, pipeline, efficiency, energy efficiency, cache/memory design and I/O design • Greater logic density, power density, wire-ability. All permits more cores per chip, larger cache, additional execution units/circuits, addition of SMT and SIMD on each core. System performance is not linear with frequency ̶ Need to use LSPR and System z capacity planning tools for real client / workload sizing System z leverages technologies to get the most out of chips design ̶ Low latency pipelines ̶ Dense packaging with proper cooling which yields more power-efficient operation ̶ Consistent performance at high utilization The IBM z13 Server is a significant change from zEC12 ̶ Processor speed measured in instructions per second (for a given workload) has increased as compared to the zEC12. • Wider pipeline (up to six per cycle) • Enhanced branch prediction • Optimized resolution of dependencies between instructions.
  • 18.
    12/10/2015 18 z Systems -Processor Roadmap z196 9/2010 zEC12 8/2012 z10 2/2008 z13 1/2015 Leadership Single Thread, Enhanced Throughput Improved out-of-order Transactional Memory Dynamic Optimization 2 GB page support Step Function in System Capacity Top Tier Single Thread Performance System Capacity Accelerator Integration Out of Order Execution Water Cooling PCIe I/O Fabric RAIM Enhanced Energy Management Leadership System Capacity and Performance Modularity & Scalability Dynamic SMT Supports two instruction threads SIMD PCIe attached accelerators (XML) Business Analytics Optimized Workload Consolidation and Integration Engine for CPU Intensive Workloads Decimal FP Infiniband 64-CP Image Large Pages Shared Memory 8 double-wide cores per CP chip 2X Instruction pipe width – Improves IPC for all modes – Symmetry simplifies dispatch/issue rules – Required for effective SMT Added FXU and BFU execution units – 4 FXUs (Fixed point units) – 2 BFUs (Binary floating-point units) – DFUs (Decimal floating-point units) – 2 new SIMD units SIMD unit plus additional registers Pipe depth re-optimized for power/performance – Product frequency reduced (5.0 GHz) – Processor performance increased SMT2 support – Wide, symmetric pipeline – Full architected state per thread – SMT-adjusted CPU usage metering z13 Processor Overview Enhancements at a glance
  • 19.
    12/10/2015 19 IFB ICM LSU L2D (eDRAM) RU IDU ISU XU TU VFU FXU IFB ICM LSU ISU IDU FXU RU L2D L2I XU PC VFU COP Whereis the SIMD Unit? Fixed Point Unit • Counters (Loops) • Adresses Vector & Float Unit • Fixed Point Decimal (BCD) • Floating Point HEX • Floating Point BINARY • Floating Point DECIMAL • Vector string processing • Binary vector integer z13 VFU (Vector and Floating-Point Units) • Enhancements – Two execution pipelines & 4x FPU / VFU registers – SIMD & string (new engine) • String processing & vector execution of binary integer – DFX (decimal FXU) • new engine optimized for BCD add/sub/compare • zEC12: these ops executed on DFU – BFU: Binary & Hex floating-point unit • new: Vector support for Binary 64b float – Leading edge Divide/sqrt engine for binary & hex FP • zEC12: divide & square-root executed in the BFU • Shorter latency and higher throughput than zEC12 – DFU: decimal floating-point unit • 3rd generation, allowing for higher throughput • Executes BCD mul, div, convert, shift – All Units are implemented twice – so in the best case this doubles througput compared to zEC12 BFU BFU Divide Divide DFU DFU DFX SIMD & String DFX SIMD & String RegFiles RegFiles zEC12 FPUs Silvia M Mueller, Jan/2015
  • 20.
    12/10/2015 20 New Memory Controller CryptoExpress5S FICON Express16S 1U Support Element Standalone zBX Node Hybrid Computing 2.7M lines of firmware changed Radiator Design improvements Expanded operating environment (Rear Doors) 22nm Processor with SIMD, SMT Integrated I/O with PCIe Direct Attach Single Chip Modules Drawer-Based CPC Design Cable-Based SMP Fabric Oscillator Backplane Flexible Service Processor (FSP2) Integrated Sparing On-chip power/thermal monitor / control z13 System Design Changes New Memory Controller Crypto Express5S FICON Express16S 1U Support Element Standalone zBX Node Hybrid Computing 2.7M lines of firmware changed Radiator Design improvements Expanded operating environment (Rear Doors) 22nm Processor with SIMD, SMT Integrated I/O with PCIe Direct Attach Single Chip Modules Drawer-Based CPC Design Cable-Based SMP Fabric Oscillator Backplane Flexible Service Processor (FSP2) Integrated Sparing On-chip power/thermal monitor / control z13 System Design Changes
  • 21.
    12/10/2015 21 Physical node: (Twoper drawer) Chips – Three PU chips (with up to 8 active cores each) – One SC chip (480 MB L4 cache) RAIM Memory – Three Memory Controllers: One per CP Chip – Five DDR3 DIMM slots per Controller: 15 total per logical node – Populated DIMM slots: 20 or 25 per drawer SC and CP Chip Interconnects – X-bus: SC and CPs to each other (intra node) – S-bus: SC to SC chip in the (intra drawer) – A-bus: SC to SC chips in the remote drawers (intra box) Mem PSI Mem GX Bus 2x PCIe GX Bus 2x PCIePUPU SCSC MemMem DIMMs GX Bus 2x PCIe Node 1 GX Bus 2x PCIe GX Bus 2x PCIe GX Bus 2x PCIe Fully Populated Drawer MemMem A-Bus S-Bus X-Bus Node 0 X-Bus SCSC A-Bus To other drawers To other drawers z13 Drawer Structure and Interconnect Drawer Based SMP Topology Node 1 Node 0 Node 1 Node 0 Node 1 Node 0 Node 1 Node 0 Drawer Drawer Drawer Drawer PU PU PU PU PU z13 versus zEC12 HW Comparison … and z196 zEC12 (z196) – CPU • 5.5 GHz (1514 PCI) • Enhanced Out-Of-Order – Caches • L1 private 64k i, 96k d (Same) • L2 private 1 MB i + 1 MB d (1.5 MB) • L3 shared 48 MB / chip (24 MB) • L4 shared 384 MB / book (192 MB) z13 – CPU • 5.0 GHz (1695 PCI) • Major pipeline enhancements – Caches • L1 private 96k i, 128k d • L2 private 2 MB i + 2 MB d • L3 shared 64 MB / chip • L4 shared 480 MB / node - plus 224 MB NIC ... Memory L4 Cache L2 CPU1 L1 L3 Cache L2 CPU6 L1... L2 CPU1 L1 L3 Cache L2 CPU6 L1... ... Memory L4 Cache L2 PU1 L1 L3 Cache ... L2 PU8 L1 L2 PU1 L1 L3 Cache ...L2 PU8 L1 ... Memory L4 Cache L2 PU1 L1 L3 Cache ... L2 PU8 L1 L2 PU1 L1 L3 Cache ...L2 PU8 L1 Single Book View Single Drawer View
  • 22.
    12/10/2015 22 z13 Architecture Extensions Core,SIMD, SMT-2 • Core micro-architecture radically altered to increase parallelism – New branch prediction and instruction fetch front end • Supports SMT-2 and improves branch prediction throughput for all modes – Wider instruction decode, dispatch and completion bandwidth: • Up to six instructions per cycle compared to three on zEC12 – Up to ten instructions issued for execution per cycle compared to seven on zEC12 • 2 branch, 4 FXU, 2 LSU, 2 BFU/DFU/SIMD • Single Instruction Multiple Data (SIMD) instruction set and execution: Business Analytics Vector Processing – 139 new instructions operate on 32 new 128-bit registers – String, vector integer and vector floating point operations • Two 64-bit, four 32-bit, eight 16-bit, or 8-bit operands per register • Two-way simultaneous multithreaded (SMT-2) operation – Up to two active execution threads per core • Dynamically share caches, TLBs, and execution resources. • Significant boost in core and chip throughput on top of core performance increase – SMT-2 operation supported for IFLs and zIIPs – Hardware monitoring support for chargeback and capacity planning 43 Simultaneous Multi-threading (SMT) • Simultaneous Multi-threading (SMT) technology • Multiple programs (software threads) run on same processor core • More efficient use of core hardware • Active threads share core resources • In space: data and instruction caches, TLBs, branch history tables, etc. • In time: pipeline slots, execution units, address translator, etc. • Increases overall throughput per core when SMT active • Amount of increase varies widely with workload – typically 1.2-1.6X • Each thread runs more slowly than on a single-thread core A / B A B B Load/Store (L1 Cache) A A B Execution Units (FXU/FPU) instructions A B A A B A A B Shared Cache A B A A B A BA Cache Thread-A Thread-B Use of Pipeline Stages in SMT-2 Both threads Stage idle 44
  • 23.
    12/10/2015 23 Simultaneous Multi Threading(SMT2) • Simultaneous multithreading allows instructions from more than one thread to execute in any given pipeline stage at a time • SMT helps address memory latency, resulting in overall throughput gains • It can increase processing efficiency, and throughput • Currently available on IFLs (Linux and z/VM) and zIIPs (z/OS) • The number of concurrent threads is limited to two and can be turned on or off by an operator command and also set up through parmlib for z/OS 45 Which approach is designed for the highest volume of traffic? Which road is faster? Illustrative numbers only Note: SMT is designed to deliver better overall throughput for many workloads. Performance in some cases may be superior using single threading Why only 45? SMT dependency on Job runtime • SMT increases the efficiency of a core. Instead of lets say running 100 instructions per TIME in a single thread, SMT allows running more than 100 instructions in 2 threads in the same TIME. • However, sharing one ressource amoung 2 threads is „not for free“. There is a „cost“ associated. • SMT Effectivness = (SMT Throughput / Single Thread Throughput). – Lets assume we can run 125 instructions in the same timeintervall with SMT switched on we would end up with SMT Eff. = 125 / 100 • The „cost“ is, that both threads run slower than a single thread. • The elongation is calculated as: Elongation % = (2 / SMT Eff.) - 1 – In our example the single threads would run 60% slower than before. • Lets assume your client has a system zEC12 with 10 ZIIPs. – Because of z13‘s single thread performance improvement you only need 9 ZIIPs now – With SMT switched on and 25% efficiency improvement you end up with 7 ZIIPs. – In that case all workload on your ZIIPs will run 60% slower than without SMT and 46% slower than before on zEC12.
  • 24.
    12/10/2015 24 Remarks on SMT •For z/OS – You enable SMT in LOAD xx at IPL – Once done, enable/disable SMT per LPAR by dynamically activating different OPT files. • For native Linux – There is no support (yet) in Redhat or Suse Linux for SMT. – This is expected to be available in Q3 2015. • For z/VM – No dynamic switching of SMT modes – Linux running under z/VM can exploit SMT • Throughput improvements dependent on the workload with SMT: – SAP runs very nicely with SMT switched on (large SMT throughput benefit) – Imagine you are running OLTP transactions and time critical Batch Jobs on a single core with SMT switched on – may be less beneficial (Bad Cache lines) Enterprise Level Multithreading Expands capacity without compromising predictability • z13 delivers significant boost in IFL and zIIP performance (throughput and capacity) via simultaneous multithreading (SMT) • Extends per-processor capacity growth beyond single-thread performance • z13 will support 2 threads per core • Design will preserve unique System z values and attributes • Predictable core capacity with precise utilization measurement – Supports chargeback, billing, and capacity planning • SMT enablement independently controlled by LPAR • Operating systems must be explicitly enabled for SMT • Operating system may opt to run in single-thread mode • SMT control coordinated across hardware, hypervisors, software • Operating system running in LPAR controls use of threads in each core – Transparent to the application • Hypervisors can leverage SMT for image consolidation • Functionally transparent to middleware and applications • Each hardware thread has full z/Architecture processor function • No changes required to run in SMT partition 48
  • 25.
    12/10/2015 25 49 z13 and SimultaneousMulti Threading • Simultaneous Multi Threading (SMT2) is available on IFLs and zIIPs only (currently) – 10% to 35%+ throughput improvement on top of Uni-processor performance gains • IFLs: 10% to 32% • zIIPs:10% to 40% – Will affect throughput (amount of work that can be done) versus performance (speeding up the IFL / zIIP) • Be sensitive to workloads that need fastest possible single thread performance – Individual work units may run slower due to each thread running slower on an SMT enabled core • Example: If a core can do 1000 MIPS without SMT (one thread) and with SMT turned on runs 2 threads giving 40% more throughput, then the core will deliver 1400 MIPS with SMT. Since the 1400 MIPS is the sum of the two threads, each thread will be running at 1400/2 = 700 MIPS – Workloads, jobs, transactions that are CPU bound and very response time or elapsed time sensitive many not want to exploit the throughput benefits of SMT • Capacity planning tools for SMT – For IFLs: Assume 20% increase in throughput due to SMT – For zIIPs: Assume 25% increase in throughput due to SMT – Capacity planning tools will initially default to 20% throughput improvement for IFLs and 25% throughput improvement for zIIPs – Tools will be updated with the latest information – Remember that SMT throughput is in addition to benefits seen from the faster uni processor • Overall guidance: Processor speed + SMT benefit – IFL: 32% = 10%+ + 20%+ – zIIP: 40% = 10%+ + 25%+ Note: SMT is designed to deliver better overall throughput for many workloads. Performance in some cases may be superior using single threading A3 B3 C3 A2 B2 C2 Scalar SINGLE INSTRUCTION, SINGLE DATA SIMD SINGLE INSTRUCTION, MULTIPLE DATA Instruction is performed for every data element Perform instructions on every element at once Sum and Store C1 C2 C3 A1 B1 A2 B2 A3 B3 INSTRUCTION A1 B1 C1 Sum and Store SIMD (Single Instruction Multiple Data) Increased parallelism to enable analytics processing • Smaller amount of code helps improve execution efficiency • Process elements in parallel enabling more iterations • Supports analytics, compression, cryptography, video/imaging processing • Exploitation by (partial list): – Java8 and C/C++ for z/OS and Linux on System z; GCC for Linux on System z – Enterprise COBOL for z/OS, PL/1 – MASS and ATLAS math libraries from Rational for z/OS and Linux on System z – ILOG-CPLEX, z/OS XML System Services 50
  • 26.
    12/10/2015 26 Single Instruction MultipleData (SIMD) Introduction and background 51 Motivation / Background – The amount of data is increasing exponentially - IT shops need to respond to the diversity of data – Enterprises use traditional integer, floating point data; also string, XML character-based data – It’s becoming more important for customers to do computations, analytics closer to the data Customer Perception of Analytics and System z – System z handles OLTP and Batch; lots of math and changing data is too compute intensive for z Reality of Analytics and System z – For last 2-3 generations, z has changed its capabilities in compute processing space (analytics) • Superscalar, Out of Order (OoO), compiler improvements, floating point • Legacy Capabilities: quad precision floating point, fuse/multiply/add – SIMD provides next phase of enhancements for analytics competitiveness on z SIMD Objective – Leverage data intensity and be competitive with large data volumes; compete by doing more operations on a given byte of data, extract more interesting insight, and turn that insight into customer inspiration Workloads that may benefit from Data Parallelism (SIMD) – High Data Intensity (i.e. data volume) – High Compute Intensity (i.e. operations on a given byte of data) – Predictive IT analytics, Advanced Security/Crypto, BI reporting, Prescriptive Analytics, Next-Gen Data Warehousing Use Cases – Reporting functions: Querying and populating reports, often in batch fashion to process lots of data quickly – Numerically intensive processing • i.e. time forecasting, simulation; ex: Tivoli based analysis with capacity management – Modelers, matrix intensive computations – ILOG CPLEX: optimizations; i.e. delta cruise scheduling, linear programming Instruction pool Data pool Results Instruction pool Data pool Results Single Instruction Multiple Data (SIMD) Vector Processing • When used with provided libraries and compilers: – creates a platform for numeric and data intensive computing, – minimizing effort on the part of middleware/application developers for exploitation. This capability enhances analytics and big data capability of z13 Key enablers: Libraries: MASS, ATLAS Compliers: XLC, Java.Next String processing: Java / Cobol / PL1 string, XMLSS, Cognos Binary Floating Point ILOG, SPSS, analytics, mobile codes OS/Hypervisor Support: z/OS: 2.1 SPE Linux: SLES12 SP1 and RHEL7.2 Workloads Java.Next C/C++Compiler built-ins for SIMD operations (zOS and zLinux) MASS & ATLAS Math Libraries (zOS and zLinux) SIMD Registers and Instruction Set 52
  • 27.
    12/10/2015 27 SIMD - Exploitation Newz13 assembler instructions which directly use the vector facility (this is not the full list): • VL Vector Load • VLL Vector Load with Length • VSTL Vector Store with Length • VCEQ Vector Compare • VFAE Vector Find Any Element Equal • VFEE Vector Find Element Equal • Using these instructions promises maximum improvements from SIMD • ILOG CPLEX, COBOL Inspect .. Tallying, JAVA8, self written ASM programs All floating point operations will benefit – without change – from the new VFU design (all units are doubled now compared to zEC12). tor Element Rotate and Insert under Mask 53 Java 8 SIMD Vector Engine Exploitation 54
  • 28.
    12/10/2015 28 Java Performance onz13 By Java version, HW generation, and w/w/o SMT 30 % increase in Throughput 55 1999 2009 1998 2001 2003 2005 200 7 SDK1.4 1. 31-bit z/OS and 31-bit and 64-bit Linux on z 2. GA 4Q2002 3. z/OS End of Marketing September, 2008 4. z/OS End of Service September, 2011 31-bit and 64-bit SDK 5 1. IBM J9 2.3 VM and JIT Technology 2. z9 Exploitation 3. GA 4Q2005 4. z/OS and Linux on z 31-bit SDK1.1.8 1. OS/390 GA 1999 2. Out of service 31-bit and 64-bit SDK 6 , V6.0.0 1. Supplies Java SE 6 APIs 2. z10 Exploitation 3. IBM J9 2.4 VM and JIT Technology 4. GA 4Q2007 5. z/OS and Linux on z 31-bit SDK1.3.1 1. z/OS and Linux on z 2. GA 3Q2000 3. End of Service: September, 2007 z/OS 64-bit SDK 1.4.2 1. IBM J9 2.2 VM and JIT Technology (1st product use) 2. GA 4Q2004 3. End of Service September, 2008 31-bit SDK1.1.1, then 1.1.4 and 1.1.6 1. First OS/390 Java product – GA 1997 2. Out of service 2014 31-bit and 64-bit z/OS Java SDK 6 V6.0.1 1. Supplies Java SE 6 APIs 2. z196 Exploitation 3. New IBM J9 2.6 VM and JIT Technology 4. Enhanced JZOS and z/OS Security 5. z/OS Java products, GA March 2011: System z Java Product Timeline 31-bit and 64-bit Java SDK 7.x 1. z/OS and Linux on z 2. Supplies Java SE 7 APIs 3. OpenJDK 4. z196/zEC12 Exploitation 5. New IBM J9 2.6/2.7 VM and JIT Technology 6. GA Oct 2011 Testimonials: http://www.ibm.com/software/os/systemz/testimonials/ IBM continues to invest aggressively in Java for System z, demonstrating a rich history of innovation and performance improvements.
  • 29.
    12/10/2015 29 STI z990/z890 STI z9 InfiniBand z10/z196/z114/ zEC12/zBC12 STI: Self-Timed Interconnect 6GBps 2 GBps PCIe Gen2 zEC12/zBC12/ z196/z114 16 GBps System z I/O Subsystem Internal Bus Interconnect Speeds PCIe Gen3 z13 8 GBps 2.7 GBps 57 New FICON Function on z13 • 16 Gbps Link Speeds – Designed to reduce I/O latency to improve response time for performance-critical middleware and to shrink the batch window required to accommodate I/O bound batch work • FICON Dynamic Routing – September 2015 – Designed to allow ISL sharing by FC and FCP traffic to optimize use of ISL bandwidth in the SAN fabric for both types of traffic • SAN Fabric Priority – September 2015 – Extends z/OS WLM policy into the SAN fabric – Gives important work priority to get through SAN traffic congestion (e.g. after SAN hardware failures) • zHPF Extended Distance II – June 2015 – Up to 50% I/O service time improvement for remote write – Designed to help GDPS HyperSwap configurations with secondary DASD in remote site • 32K devices per FICON channel – Up to 85 Logical Partitions: More flexibility for server consolidation • Preserve Virtual WWPNs for NPIV configured FCP channels – Designed to simplify migration to a new-build z13 • Forward Error Correction Codes – September 2015 – Designed to addresses high bit-error rate on high frequency (>= 8Gb/s) links – Estimated equivalence to doubling optical signal power • 6th Logical Channel Subsystem – Up to 85 Logical Partitions: More flexibility for server consolidation • 4th Subchannel Set – Simplifies I/O configurations for a 2nd synchronous copy of data – With multi-target PPRC, can do HyperSwap and still maintain synchronous copy for 2nd HyperSwap58
  • 30.
    12/10/2015 30 DS8870 and z13 Improveperformance and resiliency for mainframe environments 59 • January 2015 Preview announcement DS8870 • FICON Dynamic Routing – Reduce costs with improved and persistent performance for supporting I/O devices • 16Gb host adapters – Improve network performance with 2x faster FC and FICON adapters; minimize latency for DB2 log writes with zHyperWrite • Forward Error Correction – Preserve data integrity with more redundancy on the information transmitted via 16Gb adapters • zHPF Extended Distance II – Increase remote data speed with 50% better IO performance for remote mirror • Fabric Priority – Improved resiliency capabilities while enhancing the value of FICON Dynamic Routing 1200 14000 31000 20000 52000 23000 23000 92000 110000 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 I/O driver benchmark I/Os per second 4k block size Channel 100% utilized FICON Express4 and FICON Express2 z H P F FICON Express8 z H P F FICON Express8 FICON Express4 and FICON Express2 ESCON z H P F FICON Express8S FICON Express8S z10 z10 z196 z10 z196 z10 zEC12 zBC12 z196,z114 zEC12 zBC12 z196,z114 350 520 620 770 620 620 1600 2600 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 FICON Express4 4 Gbps I/O driver benchmark MB/second Full-duplex Large sequential read/write mix FICON Express4 4 Gbps FICON Express8 8 Gbps FICON Express8 8 Gbps FICON Express8S 8 Gbps FICON Express8S 8 Gbps z10 z10 z196 z10 z196 z10 zEC12 zBC12 z196,z114 z H P F z H P F z H P F zEC12 zBC12 z196,z114 z13 GA1 z H P F FICON Express 16S z13 GA1 FICON Express 16S z13 GA1z13 GA1 FICON Express 16S 16 Gbps FICON Express 16S 16 Gbps z H P F zHPF and FICON Performance z13 GA1 DS8000 enhanced to support FICON16S 63% increase 60 20% increase (Controlled measurement environment, results may vary)
  • 31.
    12/10/2015 31 60000 84000 92000 110000 0 20000 40000 60000 80000 100000 120000 I/Os persecond Read/writes/mix 4k block size, channel 100% utilized z10 z196, z10 z13 GA1zEC12 zBC12 z196, z114 520 770 1600 2500 0 500 1000 1500 2000 2500 MegaBytes per second (full-duplex) Large sequential Read/write mix z10 z196, z10 56% increase 20% increase FE4 4 Gbps FE8S 8 Gbps zEC12 zBC12 z196, z114 FE8 8 Gbps FE8S 8 Gbps FE16S 16 Gbps FE16S 16 Gbps z13 GA1 FE8 8 GbpsFE4 4 Gbps FCP Performance for z13 GA1 61 FICON Express 16S • 16 GB/sec link speed –QLogic HBA supports 4G / 8G / 16G –New standard 64b / 66b encoding • Improves link data efficiency –HBA code in IBM memory • Simplifies Qlogic code updates • Reduced latency for large block transfers –z/OS DB2 Log writes 12-14% latency reduction in 128K log writes Up to 5-6% improvement in Transaction Latency –z/OS Managed file transfer DS8000 zDDB feature - exchange data through the SAN –Reduced batch window • Forward Error Correction –Addresses higher error rates at 16GB • Sensitivity to damaged / faulty optical cables –Improved fault isolation for optical links –IBM leading standards to enable FEC • Fabric Priority –Cooperative design across adapter / switch / control unit –Each operation given priority by z/OS / WLM • Priority propagated across SAN fabric –Ensures consistent performance for most important work Z13 GA1 (1Q2015) FL SFP+ SFP+ DRAM PG2 PG2 CNA CNA FL CFAM-S SW Storage Card (LX, SX) PG2 ASIC 4/8/16GFC CNA ASIC Universal FC/FICON spare FICON Express16S Card 62
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    12/10/2015 32 More Memory Makesa Huge Difference Adding more memory can enable you to… – Cut response time – up to 70% reduction for SAP transactions – Achieve faster decision making by leveraging in memory data – Shrink batch windows with no change to applications – Deploy and support more Linux workloads in the same system footprint – Improve system performance (reduced MLC), minimize constraints, and simplify management of applications with database exploitation of additional memory – Get more work done z13 offers up to a Maximum of 10TB (10,000 GB) Up to 2.5 TB per model increment Leadership reliability and availability with RAIM LPAR support of the full memory configured Elimination of the 1TB limit z/OS 2.1 (with SPEs) to support up to 4TB per image z/VM 6.4 to support up to 1TB per VM guest 63 64 Memory Delivers Additional Benefits Go for mega memory… Enable totally new types of applications − Perform faster table scans with in memory data for faster response time; reduce CPU by avoiding IO Simplify Memory capacity planning − Reduce need to fine tune memory Accommodate growing batch workloads − Run sorts using large memory, improving CPU consumption and elapsed times Now easily support new modern computing languages and architectures − Java and other memory intensive languages Customers can see CPU savings − See up to 5% CPU savings with DB2 tuning enabled by Large Memory − See 5% CPU savings for typical workloads, in some cases up to 20% in certain environments, e.g.; when using SAP with DB2 − Your mileage may vary, and is highly depending on buffer pool hit ratios 70GB buffer pools 1MB frames with Page Fixed is the best performer Candidates benefiting from large memory include: Analytics Java DB2 Cognos Indexing Batch LE CF PR/SM 1.5 1.55 1.6 1.65 4K Pagable 4K Fixed 1M Pagable 1M Fixed milli-seconds Total DB2 CPU time per Transaction
  • 33.
    12/10/2015 33 z13 PR/SM Roleand Capabilities expanded Dynamic Memory Management! • zEC12 and earlier PR/SM controlled relationship between logical and physical CPs. it pseudo-dedicate physical CPs to vertical high logical CPs, and try to keep logical CPs for an LPAR together in 1 book and one chip, to maximize the value of the L3 and L4 cache. PR/SM can change relationship and transparently move logical CPs between physical CPs. • z13 capabilities of PR/SM expands. Not only assigning logical CPs to physical CPs, now also responsible for controlling where the memory for each LPAR will be allocated. And it is now able to not only dynamically move logical CPs between drawers, it can also (transparently) move memory between drawers. • The complex algorithms PR/SM uses to determine best location for each LPAR include considerations, including: – The number and type of logical CPs in LPAR. – The LPAR's weight as % of total weight of all active LPARs (fair share' of overall capacity). – The number of vertical high, vertical medium, and vertical low (“parked” and “un-parked”) CPs in the LPAR. – The amount of LPAR memory and drawer memory. – The number of assigned and unassigned PUs in each chip, node, and drawer. – Many others. 65 IBM z13 Large Memory Client Value from Large Memory • Response Time: Consistent, fast transactional response time drives a series of bottom line benefits for Clients including higher productivity and sales. • Availability Gains: The culture to hoard memory and tune tightly exists. 3X and mega memory help break this. Availability gains show up in (1) how well the system survives I/O disruptions (2) workload tuning to handle spikes and (3) all of the Flash use cases get better (workload startup and failure scenarios). • Application Productivity and Memory Hungry Workloads: Analytics and other large memory users can be placed closer to data. Changes to charge back allocation reflecting the lower cost of memory enable Application Designers' use of caching software layers and other industry performance and productivity technology. • Long Term Architectural Vision: The 3X memory purchase acts as a shock to the system to move the balance point of workloads towards gaining the advantages of large memory usage. This change in vision in an organization is a very significant Client advantage of the 3X and Mega memory pricing approaches. (Controlled measurement environment, results may vary) http://www.redbooks.ibm.com/abstracts/redp5146.html?Open
  • 34.
    12/10/2015 34 IBM z13 LargeMemory Enabling optimal middleware efficiency • DB2 Buffer pools – By configuring more memory for DB2 buffer pools • up to 3% CPU time savings • up to 10% transaction response time savings in the DB2 portion of their workload – DB2 synchronous I/O simulation tool now available on DB2 11 • Predict expected reduction in sync I/O resulting from a specific increase in the size of a DB2 buffer pool • Estimate CPU savings based on reduction in sync IOs /sec • IMS – Page fix IMS Full Function database buffers • Improve response time for IMS DB accesses from application • Save up to 3% CPU time of the IMS portion of your workload • MQ – Large memory for IBM MQ V8 can help to cost effectively manage the increasing message volumes generated from today's mobile and cloud applications (Controlled measurement environment, results may vary)http://www.redbooks.ibm.com/abstracts/redp5146.html?Open IBM z13 Large Memory Unleashing the Next Wave of Innovation • MDM (InfoSphere) – Right-sizing your group buffer pools • Configure large enough group buffer pools to cache all MDM Tables and indexes – Internal measurements • Increasing group buffer pool size from 120 GB to 565 GB and retaining all MDM tables and indexes in the group buffer pool • Reduced CPU time by 6.8% • Reduced application elapsed time by 18% • Java / WAS – Allow larger Java heaps and more Java instances • Reduce latency and CPU cost • Cognos Dynamic Cubes on z/OS – Data in memory for faster report generation – Scale up from 1 to 3 Cognos instances with – Increasing one COGNOS Instance to 3 instances using 480GB, we measured 95% scale efficiency (Controlled measurement environment, results may vary)http://www.redbooks.ibm.com/abstracts/redp5146.html?Open
  • 35.
    12/10/2015 35 IBM z13 LargeMemory Demonstrating Customer Value • Ensure that middleware is optimally tuned – Are DB2 buffer pools optimized? • Are the DB2 buffer pools sized correctly? – Customers with DB2 V11 to use DB2 synchronous I/O simulation tool. • Is the customer using fixed large pages for buffer pools? – Is other middleware taking full use of memory (IMS, MQ, MDM)? • There are other opportunities to exploit mega-memory – Benefits from more JVMs, or larger JVM heaps? • WAS applications, Java applications, Java transactions – Other opportunities? http://www.redbooks.ibm.com/abstracts/redp5146.html?Open Large Memory remarks Rethink memory usage on z Systems • With z13 option to include up to 10 TB in the machine, 2.5 TB per drawer. – “Mega memory“ affordable option 1H 2015 • Large memory expolitation? – Assiging the memory to Linux images, as Linux applications often like that. – Assigning memory to DB2 Bufferpools. Good idea, also consider: • Adding more memory to local buffer pools means you need to add memory in the global bufferpools (CF) • If your customer already has high local bufferpol hit ratios, larger bufferpools offer less value. Consider revamping BP layout. – Other middleware smaller benefits from large memory than DB2 (like IMS, CICS, ...) – Java may use large memory – For MQ Series, Version 8 provides 64 bit support • Application programmers should be involved in planning for large memory exploitaion.
  • 36.
    12/10/2015 36 Large Memory MeasurementResults DB2 and SAP http://www-03.ibm.com/support/techdocs/atsmastr.nsf/WebIndex/WP102461 z13 Extends Scale, Relieves Constraints Advanced system design optimized for digital business * Servers exploit a subset of its designed I/O capability PCI – Processor Capacity Index Customer Processors PCI for 1-way 1695 Memory 80-way 64-way 54-way 1.5 TB 512 GB 1202920600 288 GB/sec* 172.8 GB/sec* 15033 TB 384 GB/Sec* 101-way z10 EC z9 EC z196 zEC12 z13 10 TB 141-way System I/O Bandwidth 832 GB/Sec* 72
  • 37.
    12/10/2015 37 Acceleration & Optimization Integrationversus time-to-market, • Embedded accelerators • Computation engines for analytics • Assists for dynamic software optimization • Enablement for integrated function • Close collaboration with compiler and other software teams • PCIe-attached accelerators • FPGAs, Field Programmable Gate Arrays • Leverage flexibility for special functions • In-line processing of data entering or leaving system • Off-load specialized data processing • Crypto, FICON, Compression, ROCE, Flash,... • More may come... • Heterogeneous system optimization • Integrate special-purpose appliances into System z workloads • Enhanced system scale and price/performance 73 IBM z13 Designed for Analytics - Summary Accelerate insight and simplify implementation • IBM DB2 Analytics Accelerator accelerates queries for faster insight – New innovative use cases, such as in- database transformation and advanced predictive analytics • Large memory allows new opportunities for in-memory computing – per system & per LPAR • SMT2 for increased zIIP & IFL cores capacity • SIMD delivers accelerated analytics processing for complex queries – Enable vector processing capabilities to z Systems • More – Optimized math libraries and compilers that will speed up and simplify application development – Faster thread speeds – z Enterprise Data Compression (zEDC) to improve the economics of keeping data on z Systems 74 Which approach is designed for the highest volume of traffic? Which road is faster? Illustrative numbers only A3 B3 C3 A2 B2 C2 Scalar SINGLE INSTRUCTION, SINGLE DATA SIMD SINGLE INSTRUCTION, MULTIPLE DATA Instruction is performed for every data element Perform instructions on every element at once Sum and Store C1 C2 C3 A1 B1 A2 B2 A3 B3 INSTRUCTION A1 B1 C1 Sum and Store
  • 38.
    12/10/2015 38 Analytics as partof the flow of business; Insights on every transaction • Purchase made • Resources consumed • Bill paid • Claim submitted • Information updated • Call center contacted • What happened? • How many, how often, where? • What actions are needed? • What will happen if? • What will produce the best outcome? Transactions & Analytics processed together Made possible by using LARGE memory - in addition to SIMD and SMT 75 What’s changing? Data is the new basis of competitive advantage Data is the world’s newest resource Decision-making extends from few to many As data value grows, current systems won’t keep pace 76
  • 39.
    12/10/2015 39 77 What is Bigdata? Google can give you nearly 2 Billion options Vendors have even more definitions Here is how Gartner defines Big Data Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative information processing for enhanced insight and decision making. Big Data Growth is Astounding… The four V’s of Big Data 1 in 2 business leaders don’’’’t have access to data they need 83% of CIO’’’’s cited BI and analytics as part of their visionary plan 54% of companies use analytics for competitive advantage 80% of the world’s data today is unstructured 90% of the world’s data was created in the last two years 20% is the amount of available data traditional systems leverages Source: GigaOM, Software Group, IBM Institute for Business Value“, “Volume, Velocity, Variety, Veracity” 78
  • 40.
    12/10/2015 40 Extracting insight froman immense volume, variety and velocity of data, in context, beyond what was previously possible Big Data includes any of the following Characteristics Manage the complexity of data in many different structures, ranging from relational, to logs, to raw text Streaming data and large volume data movement Scale from Terabytes to Petabytes (1K TBs) to Zettabytes (1B TBs) Establish trust as the number of data sources grows Variety: Velocity: Volume: Veracity: Create IT AgilityManage RiskOutperform Why Act Now? Only 1 in 5 organizations allocate more than 50% of IT budget to new projects Of leaders cite growth as the key source of value from analytics Source: 1 - IBM IBV Study: Analytics: A blueprint for value, October 2013 2 - IBM Global Study on the Economic Impact of IT Risk, 2013 3 - IBM Global Data Center Study, 2012 Of respondents were impacted by a cyber security breach over the past 24 months 46%75% 1in5
  • 41.
    12/10/2015 41 Analytics is becomingthe Keystone of every organization … • Analytics derive insight from data – To help optimize business performance – To build new innovative services – To fight against fraud – To make all customer interaction personal! … • Analytics become Business Critical! – Analytics services are tightly integrated with business critical applications and data • Often hosted in z/OS transaction and batch systems • Often relying on copies or aggregation of transaction and application data – Analytics is part of the flow of the business. – Decision processes have to be improved with new business insight derived from real time or near real time data. – Failure of these applications for any length of time can result in lost business or reputation. – Analytics solutions need to support a large concurrent user population with high volumes of requests. • Analytics are only as good as the underlying data foundation – Data governance & Security & Performance 81 … but IT remains aligned to the old way of doing business analytics. • Some reluctances from the past – Core business is primary, analytics is secondary! • On core business side: High volume transactions and batch processing running concurrently, shared everything DB • On analytics side: Low volume complex queries and batch reporting, shared nothing DB – Cost of running analytics on z … without looking at all hidden costs concerning data movement – latency, data governance, IT complexity – Impact on operational performance & security • Key drivers to change IT perception – Awareness of z position as primary Systems of Records – Technology availability to build a fully-integrated, end-to-end system that executes intelligent business processes – Recognized business value of advanced real time analytics – Business leaders brainstorm to identify to rethink business process with HTAP influence • Gartner Research Note G00259033 28 January 2014: Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation Insights on every transaction Analytics as part of the flow of business Analytics close to the data z Systems Transactions Analytics 8282
  • 42.
    12/10/2015 42 Data Moved Analytics Approaches forMainframe Data DATA Transaction Data Customer Data Account Data Payment Data Claims Data System z Host System z/OS Systems of Record Orchestrate Processing Predictive Scoring Business Rules Move the data to the analytics Performance of critical transactions may not meet SLAs due to data movement Customer needs to create security infrastructures across multiple servers Customer needs to create audit infrastructure across multiple servers to ensure governance Customer needs to create availability and DR function for multi-server transaction flows and in- transit data Move the analytics to the data, and within the transaction Unparalleled, proven performance execution for models and rules, with NO or seamless data movement Leverage existing best of class security with System z infrastructure Leverage existing transaction level auditing and logging for governance Leverage existing, tested HA / DR capabilities already configured with System z Network Network Data Moved Data Moved Network DATA Transaction Data Customer Data Account Data Payment Data Claims Data Orchestrate Processing Predictive Scoring Business Rules System z/OS Systems of Record System z Host 83 System z Mainframe CP(s) z/OS IFL … z/VM S MF Linux for System z IBM InfoSphere BigInsights HDFS MapReduce, Hbase, Hive IFL IFL DB2 VSAM IMS Logs System z Connector For Hadoop System z Connector For Hadoop Simplified data transfer from z/OS to Hadoop IBM InfoSphere System z Connector for Hadoop Easily extract data from mainframe sources Purpose built for Hadoop Complementary to other IBM tools: IBM MQ FTE, DataStage Fast, secure data transfer Interactive or batch Supports popular Hadoop distributions IBM BigInsights Cloudera CDH Hortonworks HDP
  • 43.
    12/10/2015 43 Operational Data Store Enterprise Data Warehouse Analytics Accelerator What is happening? What happened? Whatis likely to happen and what do I do about it? OLTP Transactions Operational analytics Real time data ingestion High concurrency Advanced analytics Standard reports Complex queries Historical queries OLAP Integrated Transformation/Warehousing Single DB2 z/OS Data Sharing Group Big Data Accelerator Accelerated Reporting Real-Time Predictive and Prescriptive Analytics Customer Interaction Data In Business Insight Out z Systems Hybrid Transaction and Analytic Processing Everything is online – Analytics in the Right Place 85 Why did it happen? Hybrid transaction/Analytical processing The hybrid computing platform on z Systems Supports transaction processing and analytics workloads concurrently, efficiently and cost-effectively Delivers industry leading performance for mixed workloads The unique heterogeneous scale- out platform in the industry Superior availability, reliability and security Transaction Processing Analytics Workload
  • 44.
    12/10/2015 44 • Business agilitythrough simplified architecture with in-database transformation and multi-step processing • Real-time, actionable business processes through in-database analytics • Insight into now to maximize business opportunities through enterprise Incremental Update enhancements Announcing DB2 Analytics Accelerator Version 5.1 Enabling real-time analytic solutions on a single, integrated system combining transactional data, historical data and predictive analytics • Extended security through encryption of data at rest and in motion while taking advantage of the renowned built-in security of z Systems • Enriched systems management capabilities and improved serviceability through IBM Call Home Strategy Enable DB2 transition into a truly universal DBMS that provides best characteristics for both OLTP and analytical workloads. Complement DB2's industry leading transactional processing capabilities Provide specialized access path for data intensive queries Enable real and near-real time analytics processing Execute transparency to the applications Operate as an integral part of DB2 and z Systems Reusing industry leading PDA's query and analytics capabilities and take advantage of future enhancements Extend query acceleration to new, innovative usage cases, such as: – in-database transformations – advanced analytical capabilities – multi-temperature and storage saving solutions Ultimately allow consolidation and unification of transactional and analytical data stores DB2 for z/OS In-database Transformation Query Accelerator Storage Saver OLTP Advanced Analytics
  • 45.
    12/10/2015 45 IBM DB2 AnalyticsAccelerator Do things you could never do before! • What is it? – The IBM DB2 Analytics Accelerator is a workload optimized, appliance add- on to DB2 for z/OS, that enables the integration of analytic insights into operational processes to drive business critical analytics and exceptional business value • What does it do? – Accelerates complex queries, up to 2000x faster – Lowers the cost of storing, managing and processing historical data – Minimizes latency – Reduces z Systems capacity requirements – Improves security and governance – Reduces operational costs and risk – Complements existing investments 89 IBM DB2 Analytics Accelerator Product Components OSA- Express4 10 GbE CLIENT Data Studio Foundation DB2 Analytics Accelerator Admin Plug-in z Systems Data Warehouse application DB2 for z/OS enabled for IBM DB2 Analytics Accelerator IBM DB2 Analytics Accelerator Netezza Technology (PDA) Users/ Applications Note: There are several connection options using switches to increase redundancy Dedicated highly available network connection 90
  • 46.
    12/10/2015 46 Customer Table ~ 5Billion Rows 300 Mixed Workload Queries Times Faster Query Total Rows Reviewed Total Rows Returned Hours Sec(s) Hours Sec(s) Query 1 2,813,571 853,320 2:39 9,540 0.0 5 1,908 Query 2 2,813,571 585,780 2:16 8,220 0.0 5 1,644 Query 3 8,260,214 274 1:16 4,560 0.0 6 760 Query 4 2,813,571 601,197 1:08 4,080 0.0 5 816 Query 5 3,422,765 508 0:57 4,080 0.0 70 58 Query 6 4,290,648 165 0:53 3,180 0.0 6 530 Query 7 361,521 58,236 0:51 3,120 0.0 4 780 Query 8 3,425.29 724 0:44 2,640 0.0 2 1,320 Query 9 4,130,107 137 0:42 2,520 0.1 193 13 DB2 Only DB2 with IDAA 270 of the Mixed Workload Queries Executes in DB2 returning results in seconds or sub- seconds 30 of the Mixed Workload Queries took minutes to hours Successfully accelerated the problem queries without affecting the rest Customer “A” Example 91 Without accelerator With accelerator Customer Example – LPAR CPU utilization comparison with and without IDAA 92
  • 47.
    12/10/2015 47 Analytic needs areexpanding from enterprise data to big data 5 Facts to consider to ensure success 1 Organizations are using analytics to outperform their competition 2 More users across the organization want access to analytics, woven into the fabric of the business 3 Analytics is business critical and demands low latency, high qualities of service and performance 4 Spreading analytic components across multiple departments can increase data latency, cost, complexity and governance risk 5 Bringing analytic components to where data originates improves data governance while minimizing data latency, cost and complexity 93 Five Business Critical Analytics Use Cases Big Data Exploration Find, visualize, understand all big data to improve business knowledge Enhanced 360o View of the Customer Achieve a true unified view, incorporating internal and external sources Operations Insight Analyze a variety of machine data for improved business results. Data Warehouse Augmentation Integrate big data and data warehouse capabilities to increase operational efficiency Security/Intelligence Extension Risk, compliance and counter fraud detection. z13 can deliver real time analytics and scale up to meet future demand 94
  • 48.
    12/10/2015 48 More Users acrossthe Organization want access to Business Critical Analytics 95 The “Big Data” Starting Point Where are organizations getting the most return on Big Data projects? 0 10 20 30 40 50 60 70 80 Audio Video Others Images Geospatial data Free-form text Social media data E-mails or documents Machine or sensor data Log data Transactions Percentage of respondents Gartner research note “Survey Analysis - Big Data Adoption in 2013 Shows Substance Behind the Hype“ Sept 12 2013 Analyst(s): Lisa Kart, Nick Heudecker, Frank Buytendijk N=465, multiple responses allowed Most big data initiatives involve transactional or log-file data 96
  • 49.
    12/10/2015 49 Unfortunately for mostof our clients, their data lifecycle is too fragmented to gain advantage from that data • Significant complexity – Data is move from operational databases to separated data warehouses/data marts to support analytics • Analytics latency – Transactional data is not readily or easily available for analytics when created • Lack of synchronization – Data is not easily aggregated and users are not assured they have access to “fresh” data • Data duplication – Multiple copies of the same data is proliferated throughout the organization • Excessive costs – An IT infrastructure that was not designed nor can support real-time analytics Historical Data Predictive Data Transaction Data 97 Business Critical Analytics Systems with IBM z Systems An Hybrid Vision Bring analytics to the data Reduced latency Reduced complexity Reduced cost Deliver business critical analytics Timely, accurate, secure data Availability, scalability, performance Rapid deployment & expansion Evolve with the business Start with your top analytic requirement(s) Grow without changing customer existing IT environment Business System OLTP & Batch Business Critical Analytics Improved business performance out Transactions in Data Transformati on Data Warehousing Minimize latency. Improve performance. Drive innovation. • Purchase made • Resources consumed • Bill paid • Claim submitted • Information updated • Call center contacted • What happened? • How many, how often, where? • What actions are needed? • What will happen if? • What will produce the best outcome? 98
  • 50.
    12/10/2015 50 On average, 70%of the data that feeds data warehousing and business analytics solutions originates on the System z platform (financial information, customer lists, personal records, manufacturing…) DB2 Analytics Accelerator – Four Usage Scenarios Understand your workload and data: Where transaction source data is being analyzed today Use Case Benefits If the data is analyzed on the mainframe Rapid Acceleration of Business Critical Queries Performance improvements and cost reduction while retaining System z security and reliability If the data is offloaded to a distributed data warehouse or data mart Reduce IT Sprawl for analytics Simplify and consolidate complex infrastructures, low latency, reliability, security and TCO If the data is not being analyzed yet Derive business insight from z/OS transaction systems One integrated, hybrid platform, optimized to run mixed workload. Simplicity and time to value If the analysis is based on a lot of historical data Improve access to historical data and lower storage costs Performance improvements and cost reduction 1 2 3 4 99 System z Point of View Building a foundation to grow with business needs 10 0 Why z13? 3X larger memory enables in-memory analytics for faster insight MASS libraries can see a 2X to 10X improvement making it advantageous to port x86 analytic workloads CPLEX on z/OS exploitation of SIMD instructions provides up to 80% improvement complex modeling Add real-time scoring to your OLTP workload with minimal impact on CPU consumption zIIP exploitation of SMT2 Linux exploitation of SMT2 Why System z Currency of data Reduce complexity Bring analytic function to the data Improve synchronization Eliminate data duplication
  • 51.
    12/10/2015 51 101 Systems of Record Systemsof Record are well integrated and mostly complete Order Fulfillment Corporate Data Ware- house Accounting Finance z/OS Systems of Engagement Systems of Engagement are disconnected, piece parts Internet of Things (e.g., RFID) Mobile Apps Social Channels Siloed Dept. Apps SaaS/ Cloud Apps Linux on System z Campaign Mgmt App Other Dept. Apps System z Bridging Systems of Record with Systems of Engagement 10 2 z13 Potential Cost Reduction Use Cases: HW/SW “currency” Summary Workload Options Performance Improvement (estimation) Potential Cost Reduction (estimation) SAP with DB2 on z/OS zHW+zSW currency + more memory 10%-18% +20% 10% 30% CICS/DB2 banking zHW+zSW currency + more memory 10%-15% +5% 10% 15% WAS on z/OS e.g. internet banking zHW+zSW currency + SMT + more memory 10%-12% +25% +5% 5% 5% 15% SPSS predictive analytics zHW+zSW currency + more memory + SIMD 10%-18% +5% tbd 10% 15% tbd Traditional Batch processing zHW+zSW currency + more memory + SIMD 10% +5% tbd 10% 15% tbd Linux consolidation on IFL e.g. Private Cloud zHW+zSW currency + SMT + more memory + GDPS 15% +20% tbd D/R improvement 15% 35% tbd tbd Mobile zHW+zSW currency + more memory 10% +5% 5% 15% All performance information was determined in a controlled environment. Results may vary. Sample output from new version of z13 Benefits Estimator tool
  • 52.
    12/10/2015 52 L1 miss Instrs 1 2 3 4 5 6 7 Time In-order coreexecution L1 miss Time z196 Out-of-order core execution L1 miss zEC12 Out-of-order core execution Time Improved overlapping opportunities Execution Storage access Dependency Out of Order Execution z10, z196 vs zEC12/zBC12 12/10/2015 zBC12/zEC12 Cache Topology L1 64KI + 96KD 8w (D) / 4w (I) Set Associative L2 Private 1+1MB Inclusive of L1s; new split 2nd level cache design L3 Shared 48MB Inclusive of L2s 12w Set Associative L4 384/192MB Inclusive 24w Set Associative zBC12/zEC12 4 L4 Caches 384/192MB Shared eDRAM L4 L1 64KI + 128KD 8w (D) / 4w (I) Set Associative L2 Private 1.5MB Inclusive of L1s 12w Set Associative L3 Shared 24MB Inclusive of L2s 12w Set Associative L4 192MB Inclusive 24w Set Associativez114/z196 4 L4 Caches 192MB Shared eDRAM L4 6 L3s, 24 L1 / L2s L2 L1 24MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 24MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 48MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 48MB Shared eDRAM L3 L2 L1 L2 L1 L2 L1 L2 L1 L2 L1 6 L3s, 36 L1 / L2s
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    12/10/2015 53 Data Compression Acceleration High Speed Communication Fabric Proactive Systems Health Analytics SSDFlash Exploitation Hybrid Computing Enhancements Reduce CP consumption, free up storage & speed cross platform data exchange Optimize server to server networking with reduced latency and lower CPU overhead Increase availability by detecting unusual system behavior for faster problem determination and resolution Improve availability at critical times like market open or during abnormal situations; New coupling & Linux exploitation x86 blade resource optimization; New alert & notification for blade virtual servers; Expanding future roadmap zEDC Express 10GbE RoCE Express IBM zAware IBM Flash Express zBX Mod 003; zManager Automate; EAM zEnterprise compilers (COBOL, PL/I, C/C++), Run time monitoring and Transactional Memory provide an optimized application infrastructure for increased software performance Innovations available on zBC12 and zEC12 w zEC12/ zBC12 w zEC12 zBC12 w zEC12 zBC12 w zEC12 zBC12 General Performance Disclaimers MSU Ratings MIPS Tables IBM System z Capacity Planning in a nutshell Please do not use “single-number tables” for capacity comparisons
  • 54.
    12/10/2015 54 General Performance Disclaimers IBMSystem z Capacity Planning in a nutshell Please do not use “single-number tables” for capacity comparisons Preparing for z13: Why CPU MF Important? Collect SMF 113, Calculate RNI, use zPCR • z13 provides lower single thread improvements than previous processor changes, e.g. zEC12 versus z196 • z13 provides more variability in capacity improvement – Capacity projections and expectations should be reasonably accurate – Relative Nest Intensity (RNI) is a metric describing access to various cache levels of the processor architecture
  • 55.
    12/10/2015 55 Although System zservers are famous for their scalability, CPU consumed/Trans is a function of total load on system as this real life Nordic client case illustrates Approx. 3-4% growth in CPU/transaction for each 10% growth in CPU busy Increasing Data and Compute Requirements 11 0 Mathematical Optimization Next Best Action What will happen next? Business Intelligence- Reporting Big Sort Small Sort Small String Ops Large String Ops Big Scan Traditional IT Analytics Predictive IT Analytics Data Intensity Small Matrix Math Large Matrix Math ComputeIntensity Integer Ops Floating- Point Ops String Ops FP, String, Int String, Int Traditional Algorithms Competitive Algorithms Data Warehousing (DW) Next- Generation DW Small Compress Large Compress Security/ Crypto Adv Security/ Crypto
  • 56.
    12/10/2015 56 11 1 PerformanceGain HW upgrade SIMD and other instructions Large Memory SW currency z13Performance and relevance in marketplace Transparent Transformational Cobol 5 HLASM New SIMD Instructions 2 GB Pages Java 8 DB2 V11 + Large Memory Reduce Paging with larger Memory MQ Series V8 + Large Memory z13 vs. zEC12 Performance XMLSS exploiting SIMD SMT Throughput gain (zIIPs and IFLs) New Floating Point Unit exploitation Processor Performance Single engine performance function Drawer layout zEC12 z/OS 2.1 z13 z/OS 2.1 MIPS MSU MIPS MSU Mips Delta Percent 2827-701 1514 188 2964-701 1695 210 181 12 2827-702 2853 352 2964-702 3196 394 343 12 2827-703 4151 511 2964-703 4644 571 493 12 2827-704 5409 664 2964-704 6041 740 632 12 2827-705 6628 813 2964-705 7392 905 764 12 2827-706 7809 957 2964-706 8700 1062 891 11 2827-707 8954 1092 2964-707 9964 1212 1010 11 2827-708 10063 1224 2964-708 11188 1356 1125 11 2827-709 11137 1350 2964-709 12371 1496 1234 11 2827-710 12179 1473 2964-710 13515 1632 1336 11 2827-711 13188 1593 2964-711 14622 1764 1434 11 2827-712 14166 1709 2964-712 15693 1891 1527 11 2827-713 15126 1822 2964-713 16729 2011 1603 11 2827-714 16069 1934 2964-714 17731 2129 1662 10 2827-715 16994 2043 2964-715 18700 2244 1706 10 2827-716 17904 2149 2964-716 19665 2358 1761 10 2827-717 18797 2254 2964-717 20624 2472 1827 10 2827-718 19673 2359 2964-718 21579 2584 1906 10 2827-719 20534 2462 2964-719 22529 2695 1995 10 2827-720 21380 2564 2964-720 23475 2801 2095 10 2827-721 22222 2661 2964-721 24415 2905 2193 10 2827-722 23061 2755 2964-722 25351 3009 2290 10 2827-723 23896 2848 2964-723 26282 3111 2386 10 2827-724 24728 2940 2964-724 27209 3212 2481 10 2827-725 25557 3032 2964-725 28130 3313 2573 10 2827-726 26382 3122 2964-726 29048 3414 2666 10 2827-727 27205 3212 2964-727 29960 3516 2755 10 2827-728 28023 3301 2964-728 30868 3619 2845 10 2827-729 28839 3389 2964-729 31772 3725 2933 10 2827-730 29651 3480 2964-730 32671 3830 3020 10 Same CHIP Same NODE, different Chip Different NODE or different Drawer
  • 57.
    12/10/2015 57 To MIPS ornot to MIPS that is z question • You can not/should not just convert MIPS from zEC12 (or older machines) to z13. Hollistic“ view performance needed to make a prediction on what machine you need. • Collecting data from existing CEC is „strongly recommended“ – HW Instrumentation Services (HIS) is support built into the z/OS operating system to allow customers to capture CPU MF information, both counter data and sample data. – Please use the tool from Patrice Megard to analyse the data. – Use the HIS data also as „feed“ for zPCR. – This is also important to have a detailed documentation about the status quo before migrating to a new machine. • Native Linux does neither support SMT2 nor SIMD until 3Q 2015. Please keep that in mind when sizing machines for Linux for system z workloads. • You need to involve Software People in the performance discussions. Their job is to do an assessment for (list is incomplete): – Java 8 improvements due to SIMD. – Cobol 5.2 Compiler improvements and project evaluation with the client. – Large memory analysis and exploitation activity definition with the client. – Check that SMT can be exploited as planned. z13 Sizing Methodology 1. Collect HIS data from the peak hours of the last month (Software AND Hardware) Please use the IBM supplied tool to analyse the data. Use the HIS data also as „feed“ for zPCR. Collect the SMF70 records from the same time intervall 2. Collect CP3000 data from one full week (incl. WE). It must be the week where the 4HRA is also included (you get this from the SCRT report) -> Ask your SW rep to provide it to you. Once you have the data you need, analyse what workload is running in the 4HRA and what effect a migration to z13 would have to this particular workload. 3. Use zPCR to get a first proposal of the new HW size. This is dependent on the HW PEAK you have – not the SW peak (the SW peak is a 4hour rolling AVERAGE). For a first iteration on sizing, leave out SMT benefits. 4. After you analyzed the HIS and zCP3000 data do a workshop with IBM about z13 and its capabilities and dependencies. Make sure you involve Software IBMers as they need to explain Java, Cobol, PL1 etc. Improvements of z13.
  • 58.
    12/10/2015 58 2001 20052000 20082010 20152003 Capacity comparison based on LSPR & IBM zPCR tool * Average SMT benefit on z13 Linux Performance Evolution TCO improvement through server generations, and SODs +58% +10-12% +33% +61% +36% 20152012 +20-32%* +26% 1 IFL on all Systems = 120 PVUs KVM support for Linux on System z SODGDPS support for Linux on System z SOD (1H 2015) Source: 2014 IBM Market Intelligence, Percentage of survey respondents Recommended Linux Workloads for Linux on z Systems • Data services: Cognos®, SPSS®, DB2®, InfoSphere™, Informix®, Oracle Database, IBI WebFOCUS, … • Business applications: WebSphere Application Server, WebSphere Process Server, Oracle Application Server, … • Development and test: WebSphere®/Java applications – Rational® Asset Manager, Build Forge®, ClearCase®, Quality Manager • Email and collaboration: IBM Domino®, IBM Connections, IBM SameTime, WebSphere Portal, … • Enterprise Content Management: FileNet® Content Manager, Content Manager, Content Manager On Demand • Business Process Management: Business Process Manager, WebSphere Business Monitor, FileNet Business Process Manager, WebSphere Operational Decision Management, … • Infrastructure services: WebSphere MQ, WebSphere Message Broker, WebSphere Enterprise Service Bus, DB2 Connect™, FTP, NFS, DNS, Firewall, Proxy, … • Cloud management: Infrastructure (IaaS), Platform (PaaS), Software (SaaS), Business Process as a Service – Tivoli® System Automation Manager, Tivoli Provisioning Manager, Integrated Service Management for System z, Maximo® Asset Management, … • Print (Ubiquitech)
  • 59.
    12/10/2015 59 Before Enterprise Linux Servers 2*15 IFL =30 Oracle RAC EE Licenses 380 cores = 285 Oracle RAC EE Licensesfrom to Nordic customer - Oracle consolidation SOD: zKVM – An Open Hypervisor for zEnterprise Open • Open Source based Virtualization • Open Source with Enterprise scale capabilities • Accelerate adoption Linux on System z Cloud • Standards-based Cloud enablement • OpenStack Efficient • KVM already used by existing users by FIEs and MSPs Use the tools you know and use today • Puppet, Chef, Heat, Knife, Moab • Home Grown Scripts in Perl , Ruby , Java…. KVM – Optimized for System z System z Host z CPU, Memory and IO Support Element PR/SM™ . . . z/TFP z/OSz/OS LinuxonSysz z/OSz/OS LinuxonSysz LinuxonSysz LinuxonSys z KVMz/VM Modern Open Standard Simple LinuxonSysz A new non disruptive hypervisor choice for the mainframe z13, zEC12, zBC12 supported KVM as an additional choice to run existing and new Linux centric workload on zEnterprise in parallel to your existing z/VM virtualization environment and z/OS 11 8
  • 60.
    12/10/2015 60 Introducing the z13 Removingconstraints on growth through innovation Greater Workload Performance, Capacity and Scale in the same energy footprint 40% more total capacity 40% more configurable cores (up to 141 vs 101) New vector facility (SIMD) for faster mathematical computation Up to 6 instructions per cycle (double that of zEC12) 3+ x more memory to reduce latency (10 TB vs 3 TB) New multithreading (SMT2) to expand IFL (Linux) and zIIP capacity 40% more LPARs to securely host more cloud tenants (85 vs 60) z13: An innovative, intelligent and integrated system that provides a trusted foundation for sustainable growth today and in the future Better Economics, Flexibility and Efficiency Almost 50% increase in granularity to fine tune system usage and cost 4x data access with zEDC Standalone zBX support for more flexibility New resilient IO Infrastructure addresses Skills, Complexity, Cost and Availability Price Performance gains for Linux, zIIP, mobile and new SW workloads Investment protection with full upgradeability from z196 and zEC12 Resilient and Secure Growth Highest level of Security (PR/SM EAL5+) Next Generation Hardware Cryptography Best System z RAS with integrated sparing Much enhanced hypervisor 11 9 Focused on Enterprise Linux Extending Linux to wider audience with LinuxONE, Linux/KVM (SOD)* Continuous data availability for z/OS and Linux guests under z/VM with new GDPS Appliance (SOD)* Faster diagnosis with IBM zAware – now extended to Linux on z Systems IBM zAware Support for Linux on System z 12 0 • Linux on System z system logs can now be analyzed by IBM zAware • Upgraded analytics engine for better results on z/OS analysis • Upgraded internal database for improved RAS • Completely rewritten UI, including heat map views HiperSockets ™ OSA (for data from other servers) LPAR z13 Host 1 IBM zAware Partition Web Server Analytics z/OS operlog LOGGER Data Transport Linux on System z HiperSockets ™ OSA (for data from other servers) LPAR zServer Host 2 z/OS operlog LOGGER Data Transport z/OS operlog LOGGER Data Transport syslog Results Models Data Retrieval Manage zAware Firmware partition (similar to CF) File System IBM zAware GUI Persistent Storage Control IBM zAware-specific knobs View IBM zAware results SE zAware Partition Shipped as firmware with z13 zVM
  • 61.
    12/10/2015 61 z Aware HeatMap – All systems in a group enhanced zAware interface z Systems z13 Model Overview Processors CP, IFL, zIIP, ICF, optional SAP Specialty engines: ‒ IFL and zIIP exploitation of SMT-2 ‒ 2:1 zIIP to CP ratio on a core basis ‒ zAAP eliminated as per SOD 3 Sub capacity levels for up to a 30 way Full support of Capacity on Demand Features 85 LPARs, increased from 60 Memory Maximum 10 TB / 2.5 TB per drawer RAIM LPAR support of the full memory enabled I/O Up to 5 PCIe I/O drawers ‒ FICON Exp8S, OSA-Exp4S, OSA Exp5S, 10Gb RoCE, ‒ FICON 16S Up to 2 Legacy I/O Drawers ‒ Up to16 FICON 8 features ‒ Carry forward only Crypto Exp4S, Crypto Exp5S, zEDC, zFlash Express Environmental Enhanced integrated sparing Directional setting of rear exhaust airflow 1 U rack mountable HMC option zEC12 z196 Model # PUs Max Memory NE1 141 10 TB NC9 129 10 TB N96 96 7.5 TB N63 63 5 TB N30 30 2.5 TB Investment protection strategy supported by: Announced upgrade paths from z196 and zEC12 N-2 Sysplex co-existence (backwards and forwards) Ensemble co-existence Node based zBX mod 004 eliminates tight coupling to CEC 12 2
  • 62.
    12/10/2015 62 Prior Servers z9 EC Z10EC z196 zEC12 z13 Unscheduled Outages Scheduled Outages Planned Outages Preplanning requirements Power/Thermal Management Application Availability zAware Flash IT Analytics System z overall RAS Strategy: Never Rest Design objective: Continuous end-to-end availability 12 3 © 2015 IBM Corporation 124 Operating Systems focused on exploiting Hardware innovation z/OS V2.1 and 2.2 • Improved price performance for zIIP workloads with SMT • Support new analytics workloads with SIMD and large memory • Digitally sign audit records to reduce risk • Improved operations agility with entitled z/OSMF z/VM® V6.3 • Improved price performance with SMT– support for twice as many processors • Improved systems management and economics • Embracing Open Standards and Open Source Interoperability • Supports more virtual servers than any other platform in a single footprint (up to 8000) Linux on z Systems • Multithreading allows for per core software savings • Starting with z/VM support today • Linux distributions expected shortly • Ability to host and manage more workloads efficiently and cost-effectively • Automatic identification of unusual messages (zAware) • Integrated continuous availability & disaster recovery solution (GDPS SOD) • KVM Support - SOD
  • 63.
    12/10/2015 63 Supported Operating Systems OperationSystem Supported Levels z/OS • z/OS V2.1 with PTFs (exploitation) • z/OS V1R13 with PTFs (coexistance) • z/OS V1R12 with PTFs (Toleration. Life cycle extension required) z/VM • z/VM 6.3 (exploitation) • z/VM 6.2 (toleration) z/VSE • z/VSE 5.1 and 5.2 will support z13 GA1 in compatibility mode with PTFs • z/VSE 5.2 (with PTFs) will exploit Crypto Express5s zTPF • z/TPF 1.1 Linux1 • SLES 11 and SLES 12 • RHEL 6 and RHEL 7 1The intention is to support n and n-1 Linux distribution releases depending on how their product cycle rolls out. The majority of z13 exploitation will be made available with the major distribution releases following System zNext general availability. IBM intends to make selected exploitation items available on selected distributions and work with its Linux distribution partners to make these available A Complete Workload-Optimized System Integration of operations and business-critical analytics into one streamlined, end-to- end data lifecycle 126 Transact Transform Analyze Report Better business response Reduced data movement, reduced complexity, reduced configuration resources More accurate, more secure, more available CUSTOMER INTERACTION DATA IN BUSINESS INSIGHTS OUT
  • 64.
    12/10/2015 64 Sources of Information IBMDB2 Analytics Accelerator – Primary Product Page – Prerequisites and Maintenance – Guides and manuals – Knowledge Center Customer Testimonials – https://engage.vevent.com/index.jsp?eid=556&seid=68284&code=brand Redbooks and Redpapers – Reliability and Performance with IBM DB2 Analytics Accelerator Version 4.1 – Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS – Hybrid Analytics Solutions using IBM DB2 Analytics Accelerator for z/OS V3.1 – IBM DB2 Analytics Accelerator: High Availability and Disaster Recovery – SAP Integration with IBM DB2 Analytics Accelerator for z/OS All TechDocs available at the following link. 127
  • 65.
    12/10/2015 65 Eight major trendsthat will affect the industry in coming years Business Services Hardware Software Technology Services Security Through 2016, the financial impact of cybercrime will grow 10% per year, due to the continuing discovery of new vulnerabilities Growth Markets By 2015, IDC expects emerging markets to generate over 33% of all IT spending Big Data Through 2015, more than 85% of Fortune 500 organizations will fail to effectively exploit big data for competitive advantage Analytics Through 2015, more than 90% of business leaders contend information is a strategic asset, yet fewer than 10% will quantify its economic value Social Business By 2014, 20% of business users will replace email as the primary interpersonal communications with social networking Smarter Planet Over $100 billion: Global investment in technology to support smart city development by 2020 Mobile Enterprise 66% of CIOs ranked mobility as a top investment priority in 2012 Cloud Economic benefits of cloud will continue to be the #1 driver of adoption through 2016 for most companies.* Strategic Market Trends Spring 2012 Customer Sets *Source: IDC, IDC's CloudTrack 2012 Summer Survey, Part 1: Cost Savings in the Cloud, November 14, 2012 Business Initiatives % 10% 20% 30% 40% 50% 60% 130 Q. In 2015, which of the following business initiatives will be significant in driving IT investments at your organization? N=242 Increase Productivity Reduce Costs Improve Business Processes Increase Revenue Introduce new Products & Services Increase Agility Improve Customer Retention Source: IDC IT Experience Survey, January 2015
  • 66.
    12/10/2015 66 Power & Cooling Mgmt& Administration New Server Spending Why OPEX Matters IT Efficiency? IT Effectiveness? 131 1995 2000 2005 2010 2015 OPEX/CAPEX Ratio 0.48 0.77 1.51 2.83 3.91 $94B $123B $138B $206B $272B WW Server, P&C and Administration Spending 3x 8x 2009 800,000 petabytes 2020 35 zettabytes as much Data and Content Over Coming Decade 44x Business leaders frequently make decisions based on information they don’t trust, or don’t have 1in3 83% of CIOs cited “Business intelligence and analytics” as part of their visionary plans to enhance competitiveness Business leaders say they don’’’’t have access to the information they need to do their jobs 1in2 of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions 60% Of world’s data is unstructured 80% Big Data Big Value to enterprise and society The resulting explosion of information (plus intermediate data) creates a need for a new kind of intelligence Kilobyte (kB) 1,000 Bytes Megabyte (MB) 1,000 Kilobytes Gigabyte (GB) 1,000 Megabytes Terabyte (TB) 1,000 Gigabytes Petabyte (PB) 1,000 Terabytes Exabyte (EB) 1,000 Petabytes Zettabyte (ZB) 1,000 Exabytes
  • 67.
    12/10/2015 67 Conclusion • There isno shortage of Big Problems that require Big Data • The Nature of Data in IT is changing. • Volume – Data doubling every two years • Variety – Heading to a trillion devices; Unstructured data; • Velocity – Sometimes all you have is milliseconds to respond • Veracity – My business, finances, safety, health, life depend on • Not all Big Data systems are created equal even if the datasheet says they are! 13 4 Big Data Big Data Big Data allows to bring together all kinds of data on one single platform
  • 68.
    12/10/2015 68 135 Unstructured Data Documents (Word, Excel,PDF, TXT, etc.) Social Media Data Facebook, Twitter, YouTube, Internet, etc. Multimedia Data Films, Music, Pictures, etc. Events / Streams Live-Cam, Exchange-Trigger, Microphone, etc. Operational Data Policy, Claim, Underwriting, General Ledger, CRM, etc. Big Data on z Big Data allows to bring together all kinds of data on one single z13 platform Most of the structured data used today resides there already, and extracting golden nuggets from unstructructured data has never been easier 136 Big Data Analytics allows various kinds of Analytics on one single z13 platform Real time, predictive and in-transaction. Bring analytics to the data. Analytics on Databases BigSheets, reports, dashboards, etc. on unstructured data Operational Analytics Reports, dashbords, etc. on operational data (i.e. legacy systems) Business Analytics Reports, dashbords, etc. on dispositive Data (i.e. Data Warehouse) Predictive Analysis What-if-Analysis, Cluster-Analysis, etc. Visualization/Discovery Search, connection and visualization of data of different datasources and –types with one application Realtime Analytics Dashboards, reports of events and streamed data
  • 69.
    12/10/2015 69 Top 5 UseCases for z13 Risk, compliance and counter fraud detection z13 can deliver real time analytics and scale up to meet future demand Operational Insight z13 can deliver customer insight and next-best-action with a competitive TCO Payment z13 is the industry-proven platform for realtime payment and transaction management Cloud, API management and Linux z13 can deliver the required scaleability for private clouds, API management platforms and critical Linux-based workload Mobile z13 can scale up to meet the high-volume transactions demand from mobile YouTube z13 Nordic Videos 13 8 https://www.youtube.com/watch?v=YuytuCEYIUg https://www.youtube.com/watch?v=sl8VKXiatjM https://www.youtube.com/watch?v=Bi4-0guBbOI
  • 70.
    12/10/2015 70 How many timeshave you used a Mainframe today? IBM Mainframe: Make the Extraordinary Possible http://www.youtube.com/watch?v=0tgt4VSrPso Mainframe 50 IBM Mainframe: Make the Extraordinary Possible http://www.youtube.com/watch?v=x6MpJL9XBlU
  • 71.
    12/10/2015 71 Server Consolidation onSystem z http://www.youtube.com/watch?v=mMsvTR0x334 z13: Installations Status per October 2015 500+ thus far worldwide (15+ Nordic) – Most installations, migrations and upgrades went smooth w/o problems – What we see: • Most installation are within -1% to +8% of the zPCR modelled expectation • Expectation is ~10% better ITR than zEC12 – Plan upgrade carefully • So far few installations experience some problems with the migration: – Less compared to previous significant processor design changes (for example z10) – But enough to warrant planning considerations
  • 72.
    12/10/2015 72 Matthias R. Bangert,Executive IT- Specialist IBM Europe, z Systems November 14, 2015 Exploiting IBM z13 to the utmost (these results were NOT measured in an “IBM controlled environment” – it is CUSTOMER PRODUCTION data) • Java 7 to Java 8 migration: - 25% CPU consumption • DB2 V10 to V11 migration: - 15% CPU consumption • SIMD usage in Assembler programs: - 30% CPU consumption • Cobol 4 to 5.2 migration (project started): - 20% CPU consumption (expected) • MQ Series V7 to V8 migration: up to 50% less batch elapsed time • SMT exploitation for ZIIPs: + 20% capacity • Large memory exploitation: VSAM/RLS, DB2 Buffer Pools, MQ Series V8 The benefits this particular client got out of their z13 clearly shows, that a holistic approach to gain the most out of z13 is not optional – it is mandatory. It also confirms the results the z13 Benefit Estimator tool is calculating. IBM LinuxONE In Action… IBM LinuxOne In Action: Scalable Financial Trading https://www.youtube.com/watch?v=VWBNoIwGEjo “I demonstrate the new IBM LinuxONE system for scalable financial trading at the LinuxCon 2015 conference. The demo shows multiple data loads (live data from the S&P 500 and Tweets) streaming via Maria DB, MongoDB, Spark Analytics, Chef, Docker and PostgreSQL. In this LinuxONE demo, even with drastic upticks in CPU Utilization during the Greek financial crisis, response times are still lightning fast”.
  • 73.
  • 74.
    12/10/2015 74 z13 Planning: Whatelse can be done? • Plan your z13 LPARs carefully – Why is this important? • We will see that the LPAR layout matters – That means LPAR weight, # of logical processors and the resulting numbers of vertical high, medium and low processors (VH, VM, VL) • A good starting point here is the LPAR Design tool available from the WLM homepage: http://www-03.ibm.com/systems/z/os/zos/features/wlm/WLM_Further_Info_Tools.html#Design • What does it do? – It allows you to layout your LPARs – Examine the VH, VM, VL processors – Provides guidelines on how to set up the LPARs efficiently Preparation for z13: CPU MF • Use the CPU Measurement Facility (Hardware Instrumentation Sampling) to obtain insight into the processor and cache architecture • Value of CPU Measurement Facility (CPU MF) – Recommended methodology for successful z Systems processor capacity planning • Need on “Before” processor to determine LSPR workload – Validate achieved z Systems processor performance • Needed on “Before” and “After” processors – Provide insights for workload pattern, behavior, new features and functions • Continuously running on all LPARs Capturing CPU MF data is an industry “Best Practice”
  • 75.
    12/10/2015 75 Preparation for z13:CPU MF … • Introduced in z10 and later processors • Facility that provides hardware instrumentation data for production systems • Two major components – Counters – for capacity planning • Cache and memory hierarchy information • SCPs supported include z/OS and zVM – Sampling – for detailed, module level analysis • z/OS HIS started task – Gathered on an LPAR basis – Writes SMF 113 records • z/VM Monitor Records – Gathered on an LPAR basis – all guests are aggregated – Writes new Domain 5 (Processor) Records 13 (CPU MF Counters) records • Minimal Overhead A plug for Cheryl Watson’s Tuning Letter: User experiences... Frank’s Viewpoint... zIIP Capacity Planning... Prep for VSAM/RLS... z13 Performance... Software pricing workshops 150
  • 76.
    12/10/2015 76 Cheryl Watson TuningLetter article on z13 performance and related items – 2015 no. 2 151 Large Systems Update (LSU) 2015 • Stockholm 9-10 Nov • Oslo 11-12 Nov • Helsinki 16-17 Nov • Copenhagen 18-19 Nov • http://www-03.ibm.com/systems/no/lsu2015/index.html 152
  • 77.
    12/10/2015 77 Other Nordic zSystems events 4Q 2015 Free of charge – enroll now • Stormaskin Forum 2015 – Capgemini - Oslo Tuesday 29. october at 15.00 - 20.30 • 2015 ITSO 3 full day Workshops in Stockholm and Copenhagen IBM z/OS Technical Update, WRZ22G IBM z/OS Performance and Availability Topics, WRZ23G IBM z/OS Networking Technologies Update, WRZ28G – Denmark: Copenhagen 30.11-2.12 – Sweden: Stockholm 2-4.12 http://www.redbooks.ibm.com/redbooks.nsf/home?ReadForm&page=workshops • LinuxONE ½ day Workshop in Stockholm December 15 – In cooperation with MariaDB LinuxONE Announcement Overview - Stefan Ekman IBM IBM’s Commitment to Opensource - Utz Bacher IBM MariaDB – Anders Karlsson MariaDB KVM Using KVM to as hypervisor to host 1000’s of servers - Tony Gargya IBM Docker – Utz Bacher IBM -- Sweden: Stockholm 15.12153 This year’s ITSO Presenters Straight from the horse’s own mouth 154
  • 78.
    12/10/2015 78 © 2015 IBMCorporation 155 Nordic Mainframe Entry Education Activities Detailed skills surveys, communities, universities/clients/IBM joint programmes • 1H 2015 • DTU Netbank case (Denmark) – Guest Education BEC, JN Data, IBM - 14 students enrolled (growing) • DTU High Availability (Denmark) • Client trainee programmes at many clients (with IBM sparrring) • 2H 2015 • HIG (Norway). An introduction to the modern Mainframe – Guest Education DNB, NAV, IBM - 25 students enrolled (growing) • IBM conduct ”entry MF training also for managers & senior people • 2H 2015 • IBM looking at restoring ”MVS Hissen” for Nordic and Swedish clients. Previous activity with KTH, Stockholm University and Olsen Education (Cobol). • 2H 2015 • Student track on Nordic GSE (Gothenburg this year) • IBM Hursley Lab: zGen community mtg (hosted by Danske Bank) • ... GSE Nordic Region Conference 2016 GUIDE SHARE EUROPE Hotel Reykjavik Nature – Reykjavik, Iceland June 1-5, 2016 156
  • 79.
    12/10/2015 79 GUIDE SHARE EUROPE GSENordic Region Conference 2016 Hotel Reykjavik Nature – Reykjavik, Iceland from June 1st - 5th 2016 • 6 Tracks with 70 highly technical and educational sessions on – DB2,CICS,IMS,Mainframe Infrastructure, Application Development and Architecture • All of the presentations are brought to you by Users and professional speakers • You will be networking with people with professional background • We take care of you from flight take off at home until you are safely returned home • The Conference is organized by Professionals from Bankdata (DK), DNB(NO), EVRY (NO), Volvo (SE), Tieto (FI), CSC(DK), JN Data (DK) and IBM (UK/DK). • The Program is available now. Visit WWW.GSE-NORDIC.ORG to view and register. • Remember that GSE members get’s a 20% discount on IBM Conferences/training. Visit www.gse.org and register through there to receive the discount. 158