This talk will present an overview of shared-memory heterogeneous ( accelerated ) computing starting with the Cell Broadband Engine used in the Playstation 3 and the world's first 1 Petaflop supercomputer in 2008, to the current number 1 and 2 supercomputers: Summit and Sierra a decade later that combine POWER processors and NVIDIA GPUs in a high-bandwidth shared-memory configuration. We examine the architectural foundations, and we show the benefits of this architecture in a number of different computing domains, from HPC applications to Big Data and ML/DL for AI. We also spend time discussing FPGAs connected to the host processor in a shared-memory configuration and the recent developments in shared-memory programming for such systems.
[Café techno] - Ibm power7 - Les dernières annoncesGroupe D.FI
Annonces de la nouvelle technologie IBM Power7+
Depuis plus de 10 ans, les entreprises privilégient la technologie Power pour AIX, IBM i et Linux. Aujourd'hui, IBM élargit le leadership de ses plateformes Power en introduisant une évolution technologique avec l'architecture Power 7+.
D.FI vous invite à découvrir les nouvelles fonctionnalités Power 7+ qui peuvent vous aider à répondre aux exigences de votre informatique en vous offrant dynamiquement une plus grande efficacité, des fonctions d'analyse métier et centraliser les charges de travail.
Nouveautés :
Le Power 7+ est une puce octocoeurs gravée en 32 nm (contre 45 mn pour Power7) :
- Atouts : La fréquence d'horloge est dopée ;
- La taille du cache eDRAM est multipliée par 2,5.
De nouvelles fonctions : assistance matérielle à la compression de mémoire AME ("Active Memory Expansion“) et accélération cryptographiques.
Une évolution majeure permet désormais de créer jusqu'à 20 micro-partitions par coeurs Power7+
The IBM Power System AC922 is a high-performance server designed for supercomputing and AI workloads. It features IBM's POWER9 CPUs, NVIDIA Tesla V100 GPUs connected via NVLink 2.0, and a high-speed Mellanox interconnect. The AC922 delivers high memory bandwidth, GPU computing power, and optimized hardware and software for workloads like deep learning. Several of the world's most powerful supercomputers, including Summit and Sierra, use large numbers of AC922 nodes to achieve exascale-level performance for scientific research.
The IBM POWER10 processor represents the 10th generation of the POWER family of enterprise computing engines. Its performance is a result of both powerful processing cores and high-bandwidth intra- and inter-chip interconnect. POWER10 systems can be configured with up to 16 processor chips and 1920 simultaneous threads of execution. Cross-system memory sharing, through the new Memory Inception technology, and 2 Petabytes of addressing space support an expansive memory system. The POWER10 processing core has been significantly enhanced over its POWER9 predecessor, including a doubling of vector units and the addition of an all-new matrix math engine. Throughput gains from POWER9 to POWER10 average 30% at the core level and three-fold at the socket level. Those gains can reach ten- or twenty-fold at the socket level for matrix-intensive computations.
This document discusses IBM's involvement in artificial intelligence and deep learning. It includes:
- An introduction to IBM's Cognitive Systems team working in AI.
- A brief history of IBM's AI projects including Deep Blue, Blue Gene, and Watson.
- Explanations of concepts like machine learning, deep learning, and how they relate to high performance computing.
- Details of IBM's current hardware, software, and services for AI workloads including the Power9 processor, PowerAI tools, and storage solutions.
The document provides an overview of IBM's expertise and offerings in the field of artificial intelligence.
This document provides a summary of IBM Power Systems technical updates for the fourth quarter of 2010. It discusses the new POWER7 processor and IBM Power Systems product portfolio including the Power 750, 770, 780, 795, 710, 730, 720 and 740 systems. It highlights the Power 750's performance in SAP Sales and Distribution and Business Intelligence benchmarks. It also covers total integration with IBM i 7.1 and the business value of workload optimization, virtualization, resiliency, energy optimization, management and integration provided by IBM Power Systems.
This document provides a summary of the IBM POWER9 AC922 system with 6 GPUs. It includes details on the POWER9 processor which features 24 cores per die, an enhanced cache hierarchy up to 120MB, and on-chip accelerators. The AC922 system utilizes two POWER9 processors, supports up to 512GB memory via 16 DDR4 DIMMs, and has three Nvidia Volta GPUs per socket connected via NVLink 2.0. It also discusses the POWER ISA v3.0 instruction set and how POWER9 serves as a premier acceleration platform with technologies like CAPI, OpenCAPI, and NVLink.
In this deck from the HPC User Forum in Tucson, Jeff Stuecheli from IBM presents: POWER9 for AI & HPC.
"Built from the ground-up for data intensive workloads, POWER9 is the only processor with state-of-the-art I/O subsystem technology, including next generation NVIDIA NVLink, PCIe Gen4, and OpenCAPI."
Watch the video: https://wp.me/p3RLHQ-isJ
Learn more: https://www.ibm.com/it-infrastructure/power/power9
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
[Café techno] - Ibm power7 - Les dernières annoncesGroupe D.FI
Annonces de la nouvelle technologie IBM Power7+
Depuis plus de 10 ans, les entreprises privilégient la technologie Power pour AIX, IBM i et Linux. Aujourd'hui, IBM élargit le leadership de ses plateformes Power en introduisant une évolution technologique avec l'architecture Power 7+.
D.FI vous invite à découvrir les nouvelles fonctionnalités Power 7+ qui peuvent vous aider à répondre aux exigences de votre informatique en vous offrant dynamiquement une plus grande efficacité, des fonctions d'analyse métier et centraliser les charges de travail.
Nouveautés :
Le Power 7+ est une puce octocoeurs gravée en 32 nm (contre 45 mn pour Power7) :
- Atouts : La fréquence d'horloge est dopée ;
- La taille du cache eDRAM est multipliée par 2,5.
De nouvelles fonctions : assistance matérielle à la compression de mémoire AME ("Active Memory Expansion“) et accélération cryptographiques.
Une évolution majeure permet désormais de créer jusqu'à 20 micro-partitions par coeurs Power7+
The IBM Power System AC922 is a high-performance server designed for supercomputing and AI workloads. It features IBM's POWER9 CPUs, NVIDIA Tesla V100 GPUs connected via NVLink 2.0, and a high-speed Mellanox interconnect. The AC922 delivers high memory bandwidth, GPU computing power, and optimized hardware and software for workloads like deep learning. Several of the world's most powerful supercomputers, including Summit and Sierra, use large numbers of AC922 nodes to achieve exascale-level performance for scientific research.
The IBM POWER10 processor represents the 10th generation of the POWER family of enterprise computing engines. Its performance is a result of both powerful processing cores and high-bandwidth intra- and inter-chip interconnect. POWER10 systems can be configured with up to 16 processor chips and 1920 simultaneous threads of execution. Cross-system memory sharing, through the new Memory Inception technology, and 2 Petabytes of addressing space support an expansive memory system. The POWER10 processing core has been significantly enhanced over its POWER9 predecessor, including a doubling of vector units and the addition of an all-new matrix math engine. Throughput gains from POWER9 to POWER10 average 30% at the core level and three-fold at the socket level. Those gains can reach ten- or twenty-fold at the socket level for matrix-intensive computations.
This document discusses IBM's involvement in artificial intelligence and deep learning. It includes:
- An introduction to IBM's Cognitive Systems team working in AI.
- A brief history of IBM's AI projects including Deep Blue, Blue Gene, and Watson.
- Explanations of concepts like machine learning, deep learning, and how they relate to high performance computing.
- Details of IBM's current hardware, software, and services for AI workloads including the Power9 processor, PowerAI tools, and storage solutions.
The document provides an overview of IBM's expertise and offerings in the field of artificial intelligence.
This document provides a summary of IBM Power Systems technical updates for the fourth quarter of 2010. It discusses the new POWER7 processor and IBM Power Systems product portfolio including the Power 750, 770, 780, 795, 710, 730, 720 and 740 systems. It highlights the Power 750's performance in SAP Sales and Distribution and Business Intelligence benchmarks. It also covers total integration with IBM i 7.1 and the business value of workload optimization, virtualization, resiliency, energy optimization, management and integration provided by IBM Power Systems.
This document provides a summary of the IBM POWER9 AC922 system with 6 GPUs. It includes details on the POWER9 processor which features 24 cores per die, an enhanced cache hierarchy up to 120MB, and on-chip accelerators. The AC922 system utilizes two POWER9 processors, supports up to 512GB memory via 16 DDR4 DIMMs, and has three Nvidia Volta GPUs per socket connected via NVLink 2.0. It also discusses the POWER ISA v3.0 instruction set and how POWER9 serves as a premier acceleration platform with technologies like CAPI, OpenCAPI, and NVLink.
In this deck from the HPC User Forum in Tucson, Jeff Stuecheli from IBM presents: POWER9 for AI & HPC.
"Built from the ground-up for data intensive workloads, POWER9 is the only processor with state-of-the-art I/O subsystem technology, including next generation NVIDIA NVLink, PCIe Gen4, and OpenCAPI."
Watch the video: https://wp.me/p3RLHQ-isJ
Learn more: https://www.ibm.com/it-infrastructure/power/power9
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The POWER AC922 system is a 2U server featuring two POWER9 processors, support for up to six NVIDIA Volta GPUs, 1TB of memory, and PCIe Gen4 and CAPI interfaces. It provides unprecedented performance and application gains for HPC and AI workloads through high CPU to GPU bandwidth via NVLink 2.0 and increased I/O bandwidth of PCIe Gen4. Air and water cooling options are available depending on the GPU configuration.
HPC Infrastructure To Solve The CFD Grand ChallengeAnand Haridass
This document summarizes Anand Haridass' presentation on using HPC infrastructure to solve computational fluid dynamics (CFD) grand challenges. It discusses how CFD utilizes physics, mathematics, computational geometry, and computer science. Solving CFD problems is bound by memory usage, computation needs, and network requirements. The presentation outlines IBM's POWER processor roadmap and how the POWER9 will have stronger cores, enhanced caches, and improved interfaces like NVLink and CAPI to accelerate workloads like CFD. Case studies demonstrate how IBM systems using GPUs and NVLink can provide faster performance for CFD codes and reservoir simulations.
Heterogeneous Computing : The Future of SystemsAnand Haridass
Charts from NITK-IBM Computer Systems Research Group (NCSRG)
- Dennard Scaling,Moore's Law, OpenPOWER, Storage Class Memory, FPGA, GPU, CAPI, OpenCAPI, nVidia nvlink, Google Microsoft Heterogeneous system usage
IBM Cloud Paris Meetup - 20190520 - IA & PowerIBM France Lab
The document discusses large model support (LMS) which allows deep learning models to leverage both GPU and system memory. LMS treats GPU memory as a cache for data stored primarily in system memory, enabling models too large to fit in GPU memory alone. Frameworks like Caffe, Chainer, and TensorFlow have been enhanced with LMS. The IBM Power AC922 server is highlighted as uniquely able to leverage up to 2TB of system memory from the GPU through NVLink. Benchmark results show LMS reducing epoch times and improving GPU utilization compared to servers without high-speed CPU-GPU interconnects.
Everything is changing from Health Care to the Automotive markets without forgetting Financial markets or any type of engineering everything has stopped being created as an individual or best-case scenario a team effort to something that is being developed and perfectioned by using AI and hundreds of computers.And even AI is something that we no longer can run in a single computer, no matter how powerful it is. What drives everything today is HPC or High-Performance Computing heavily linked to AI In this session we will discuss about AI, HPC computing, IBM Power architecture and how it can help develop better Healthcare, better Automobiles, better financials and better everything that we run on them
EXTENT-2017: Heterogeneous Computing Trends and Business Value CreationIosif Itkin
This document discusses heterogeneous computing trends and how they can create business value. It notes that the end of CPU frequency scaling around 2005-2007 led to approaches like core replication and exploiting concurrency. Fabrication technology advances have led to parity between CPUs, FPGAs, and GPUs on process nodes, with each expected to increase cores/estate/FLOPS significantly by 2030. Mapping applications to heterogeneous resources optimally is challenging due to expanding options and interconnect limitations. Value can be created through non-functional enhancements, functional enhancements, and business model transformations. A case study shows consistent 10x performance gains and optimal space efficiency through fine-grained use case based optimization across CPUs, FPGAs, and GPUs.
How to apply the latest advances in hitachi mainframe storage webinarHitachi Vantara
Join Hitachi experts in a short, valuable webcast on recent mainframe storage advances and Hitachi mainframe storage support, followed by a live Q and A session. Mainframe storage is moving beyond simply maintaining plug compatibility and onto more advanced storage capabilities introduced and proven in the open systems storage world.
This document discusses how HPC infrastructure is being transformed with AI. It summarizes that cognitive systems use distributed deep learning across HPC clusters to speed up training times. It also outlines IBM's hardware portfolio expansion for AI training, inference, and storage capabilities. The document discusses software stacks for AI like Watson Machine Learning Community Edition that use containers and universal base images to simplify deployment.
The document discusses strategies for improving application performance on POWER9 processors using IBM XL and open source compilers. It reviews key POWER9 features and outlines common bottlenecks like branches, register spills, and memory issues. It provides guidelines on using compiler options and coding practices to address these bottlenecks, such as unrolling loops, inlining functions, and prefetching data. Tools like perf are also described for analyzing performance bottlenecks.
Blue line Supermicro Server Building Block SolutionsBlue Line
This document provides information on Supermicro's next generation serverboard and embedded solutions using Intel's Ivy Bridge and Haswell architectures. It summarizes various serverboards, chassis, and embedded boards that provide improved performance up to 40%, lower power consumption, support for the latest Intel Xeon E5-2600 v2 processors with up to 12 cores, SAS 3.0 (12Gbps), PCI-E 3.0, and up to 1.5TB of DDR3 memory. Options include datacenter optimized, mainstream, workstation, and embedded form factors.
Introduction of Fujitsu's HPC Processor for the Post-K Computerinside-BigData.com
Toshio Yoshida from Fujitsu presented this deck at the 2016 Hot Chips conference. Slated for delivery sometime around 2022, the Post-K supercomputer. Originally targeted for completion in 2020, the ARM-based Post K supercomputer has a performance target of being 100 times faster than the original K computer within a power envelope that will only be 3-4 times that of its predecessor.
The document provides an overview of the IBM DS8000 storage system and its capabilities for data protection and cyber resiliency. Some key points:
- The DS8000 offers balanced performance, reliability, scalability, and flexibility for critical enterprise storage needs.
- It provides modern data protection features like data encryption, thin provisioning, and IBM Database Protection.
- The system is designed for cyber resiliency with functions that optimize caching, prefetching, and data placement to improve I/O performance.
BGE provides clients with the capability to integrate GPUs into the IBM BladeCenter ecosystem. This is ideal for clients running applications that can leverage the value of double precision performance and also value the RAS features of IBM BladeCenter.
The document discusses IBM's PowerAI software for large model support and distributed deep learning. It describes how PowerAI uses large model support (LMS) to enable processing of high-definition images, large models, and higher batch sizes that don't fit in GPU memory. It provides examples of using LMS with Caffe and TensorFlow. It also describes IBM's distributed deep learning library (DDL) for scaling deep learning training across multiple servers and GPUs, and how tools like ddlrun automatically handle tasks like topology detection and mpirun options.
SCFE 2020 OpenCAPI presentation as part of OpenPWOER TutorialGanesan Narayanasamy
This document introduces hardware acceleration using FPGAs with OpenCAPI. It discusses how classic FPGA acceleration has issues like slow CPU-managed memory access and lack of data coherency. OpenCAPI allows FPGAs to directly access host memory, providing faster memory access and data coherency. It also introduces the OC-Accel framework that allows programming FPGAs using C/C++ instead of HDL languages, addressing issues like long development times. Example applications demonstrated significant performance improvements using this approach over CPU-only or classic FPGA acceleration methods.
The session will present HPC challenges in fuelling machine learning and deep learning into the simulations. Besides, we will present a user-centric view of IBM Watson ML Community Edition and the newly IBM inference system IC922 adoption into AIops of large HPC clusters (from deployment to inference).
This talk will present an overview of shared-memory heterogeneous ( accelerated ) computing starting with the Cell Broadband Engine used in the Playstation 3 and the world's first 1 Petaflop supercomputer in 2008, to the current number 1 and 2 supercomputers: Summit and Sierra a decade later that combine POWER processors and NVIDIA GPUs in a high-bandwidth shared-memory configuration. We examine the architectural foundations, and we show the benefits of this architecture in a number of different computing domains, from HPC applications to Big Data and ML/DL for AI. We also spend time discussing FPGAs connected to the host processor in a shared-memory configuration and the recent developments in shared-memory programming for such systems.
This document provides an overview of OpenCAPI and FPGA technology. It discusses how OpenCAPI enables high-speed connectivity between FPGAs, CPUs, memory and I/O devices. Several systems are highlighted that incorporate FPGAs connected via OpenCAPI, including the Wistron "MiHawk" motherboard and AlphaData modules. The performance of OpenCAPI-connected FPGA solutions is compared to other architectures like AWS F1. Future OpenCAPI and memory interface roadmaps are also outlined.
The POWER AC922 system is a 2U server featuring two POWER9 processors, support for up to six NVIDIA Volta GPUs, 1TB of memory, and PCIe Gen4 and CAPI interfaces. It provides unprecedented performance and application gains for HPC and AI workloads through high CPU to GPU bandwidth via NVLink 2.0 and increased I/O bandwidth of PCIe Gen4. Air and water cooling options are available depending on the GPU configuration.
HPC Infrastructure To Solve The CFD Grand ChallengeAnand Haridass
This document summarizes Anand Haridass' presentation on using HPC infrastructure to solve computational fluid dynamics (CFD) grand challenges. It discusses how CFD utilizes physics, mathematics, computational geometry, and computer science. Solving CFD problems is bound by memory usage, computation needs, and network requirements. The presentation outlines IBM's POWER processor roadmap and how the POWER9 will have stronger cores, enhanced caches, and improved interfaces like NVLink and CAPI to accelerate workloads like CFD. Case studies demonstrate how IBM systems using GPUs and NVLink can provide faster performance for CFD codes and reservoir simulations.
Heterogeneous Computing : The Future of SystemsAnand Haridass
Charts from NITK-IBM Computer Systems Research Group (NCSRG)
- Dennard Scaling,Moore's Law, OpenPOWER, Storage Class Memory, FPGA, GPU, CAPI, OpenCAPI, nVidia nvlink, Google Microsoft Heterogeneous system usage
IBM Cloud Paris Meetup - 20190520 - IA & PowerIBM France Lab
The document discusses large model support (LMS) which allows deep learning models to leverage both GPU and system memory. LMS treats GPU memory as a cache for data stored primarily in system memory, enabling models too large to fit in GPU memory alone. Frameworks like Caffe, Chainer, and TensorFlow have been enhanced with LMS. The IBM Power AC922 server is highlighted as uniquely able to leverage up to 2TB of system memory from the GPU through NVLink. Benchmark results show LMS reducing epoch times and improving GPU utilization compared to servers without high-speed CPU-GPU interconnects.
Everything is changing from Health Care to the Automotive markets without forgetting Financial markets or any type of engineering everything has stopped being created as an individual or best-case scenario a team effort to something that is being developed and perfectioned by using AI and hundreds of computers.And even AI is something that we no longer can run in a single computer, no matter how powerful it is. What drives everything today is HPC or High-Performance Computing heavily linked to AI In this session we will discuss about AI, HPC computing, IBM Power architecture and how it can help develop better Healthcare, better Automobiles, better financials and better everything that we run on them
EXTENT-2017: Heterogeneous Computing Trends and Business Value CreationIosif Itkin
This document discusses heterogeneous computing trends and how they can create business value. It notes that the end of CPU frequency scaling around 2005-2007 led to approaches like core replication and exploiting concurrency. Fabrication technology advances have led to parity between CPUs, FPGAs, and GPUs on process nodes, with each expected to increase cores/estate/FLOPS significantly by 2030. Mapping applications to heterogeneous resources optimally is challenging due to expanding options and interconnect limitations. Value can be created through non-functional enhancements, functional enhancements, and business model transformations. A case study shows consistent 10x performance gains and optimal space efficiency through fine-grained use case based optimization across CPUs, FPGAs, and GPUs.
How to apply the latest advances in hitachi mainframe storage webinarHitachi Vantara
Join Hitachi experts in a short, valuable webcast on recent mainframe storage advances and Hitachi mainframe storage support, followed by a live Q and A session. Mainframe storage is moving beyond simply maintaining plug compatibility and onto more advanced storage capabilities introduced and proven in the open systems storage world.
This document discusses how HPC infrastructure is being transformed with AI. It summarizes that cognitive systems use distributed deep learning across HPC clusters to speed up training times. It also outlines IBM's hardware portfolio expansion for AI training, inference, and storage capabilities. The document discusses software stacks for AI like Watson Machine Learning Community Edition that use containers and universal base images to simplify deployment.
The document discusses strategies for improving application performance on POWER9 processors using IBM XL and open source compilers. It reviews key POWER9 features and outlines common bottlenecks like branches, register spills, and memory issues. It provides guidelines on using compiler options and coding practices to address these bottlenecks, such as unrolling loops, inlining functions, and prefetching data. Tools like perf are also described for analyzing performance bottlenecks.
Blue line Supermicro Server Building Block SolutionsBlue Line
This document provides information on Supermicro's next generation serverboard and embedded solutions using Intel's Ivy Bridge and Haswell architectures. It summarizes various serverboards, chassis, and embedded boards that provide improved performance up to 40%, lower power consumption, support for the latest Intel Xeon E5-2600 v2 processors with up to 12 cores, SAS 3.0 (12Gbps), PCI-E 3.0, and up to 1.5TB of DDR3 memory. Options include datacenter optimized, mainstream, workstation, and embedded form factors.
Introduction of Fujitsu's HPC Processor for the Post-K Computerinside-BigData.com
Toshio Yoshida from Fujitsu presented this deck at the 2016 Hot Chips conference. Slated for delivery sometime around 2022, the Post-K supercomputer. Originally targeted for completion in 2020, the ARM-based Post K supercomputer has a performance target of being 100 times faster than the original K computer within a power envelope that will only be 3-4 times that of its predecessor.
The document provides an overview of the IBM DS8000 storage system and its capabilities for data protection and cyber resiliency. Some key points:
- The DS8000 offers balanced performance, reliability, scalability, and flexibility for critical enterprise storage needs.
- It provides modern data protection features like data encryption, thin provisioning, and IBM Database Protection.
- The system is designed for cyber resiliency with functions that optimize caching, prefetching, and data placement to improve I/O performance.
BGE provides clients with the capability to integrate GPUs into the IBM BladeCenter ecosystem. This is ideal for clients running applications that can leverage the value of double precision performance and also value the RAS features of IBM BladeCenter.
The document discusses IBM's PowerAI software for large model support and distributed deep learning. It describes how PowerAI uses large model support (LMS) to enable processing of high-definition images, large models, and higher batch sizes that don't fit in GPU memory. It provides examples of using LMS with Caffe and TensorFlow. It also describes IBM's distributed deep learning library (DDL) for scaling deep learning training across multiple servers and GPUs, and how tools like ddlrun automatically handle tasks like topology detection and mpirun options.
SCFE 2020 OpenCAPI presentation as part of OpenPWOER TutorialGanesan Narayanasamy
This document introduces hardware acceleration using FPGAs with OpenCAPI. It discusses how classic FPGA acceleration has issues like slow CPU-managed memory access and lack of data coherency. OpenCAPI allows FPGAs to directly access host memory, providing faster memory access and data coherency. It also introduces the OC-Accel framework that allows programming FPGAs using C/C++ instead of HDL languages, addressing issues like long development times. Example applications demonstrated significant performance improvements using this approach over CPU-only or classic FPGA acceleration methods.
The session will present HPC challenges in fuelling machine learning and deep learning into the simulations. Besides, we will present a user-centric view of IBM Watson ML Community Edition and the newly IBM inference system IC922 adoption into AIops of large HPC clusters (from deployment to inference).
This talk will present an overview of shared-memory heterogeneous ( accelerated ) computing starting with the Cell Broadband Engine used in the Playstation 3 and the world's first 1 Petaflop supercomputer in 2008, to the current number 1 and 2 supercomputers: Summit and Sierra a decade later that combine POWER processors and NVIDIA GPUs in a high-bandwidth shared-memory configuration. We examine the architectural foundations, and we show the benefits of this architecture in a number of different computing domains, from HPC applications to Big Data and ML/DL for AI. We also spend time discussing FPGAs connected to the host processor in a shared-memory configuration and the recent developments in shared-memory programming for such systems.
This document provides an overview of OpenCAPI and FPGA technology. It discusses how OpenCAPI enables high-speed connectivity between FPGAs, CPUs, memory and I/O devices. Several systems are highlighted that incorporate FPGAs connected via OpenCAPI, including the Wistron "MiHawk" motherboard and AlphaData modules. The performance of OpenCAPI-connected FPGA solutions is compared to other architectures like AWS F1. Future OpenCAPI and memory interface roadmaps are also outlined.
Multiple Shared Processor Pools In Power SystemsAndrey Klyachkin
The document discusses Multiple Shared-Processor Pools (MSPPs) in IBM Power Systems servers. MSPPs allow administrators to partition physical CPUs into multiple shared pools, making capacity management and administration easier. After an introduction, the document covers PowerVM partitioning, MSPP implementation in POWER6 systems, and considerations for architecting virtualized services with MSPPs. It aims to explain the basic concepts and configuration of MSPPs and their benefits.
Transparent Hardware Acceleration for Deep LearningIndrajit Poddar
This document provides an overview of transparent hardware acceleration for deep learning using IBM's PowerAI platform. It discusses how PowerAI leverages POWER CPUs and NVIDIA GPUs connected via NVLink to dramatically accelerate deep learning model training and inference. Using this approach, IBM has achieved significant performance improvements over x86 platforms, including faster training times, support for larger models, and more efficient distributed training across multiple servers.
Deploying Massive Scale Graphs for Realtime InsightsNeo4j
Graph databases have been at the forefront of helping organizations manage and generate insights from data relationships, and applying those insights in real-time to drive competitive advantage. As organizations gain value in deploying graph databases, the data volumes managed are growing exponentially pushing the limits of large-scale in-memory graph processing. Neo4j and IBM Power Systems combined forces to deliver a market leading scalable graph database platform capable of affordably storing and processing graphs of extremely large size and offering real-time insights, using flash and FPGA accelerators. In this session we will cover the use cases driving the need for this extremely scalable platform and how this platform offers an easy to deploy model for extreme scale graph databases.
Enterprise power systems transition to power7 technologysolarisyougood
This document summarizes an IBM presentation about Power7 technology. It introduces Power7 processors and systems, compares them to competitors' offerings, and highlights Power7's performance, reliability, availability, security and virtualization capabilities. Key points include Power7 having 8 cores with 32MB eDRAM L3 cache, outperforming Intel's best processors on major workloads, and delivering "mainframe-class" reliability with 99.997% availability.
IBM FlashSystem and other SSD's are being adopted for OLTP and Analytics applications. Fast 16Gb Flash storage requires a reliable, high performance network to ensure applications can utilize it effectively. Learn how to plan for a highspeed reliable network to handle the increased demands while delivering reliable application response times. Understand the reliability, performance, and simplified management features of Gen5 FC and Fabric Vision. Be prepared for the next jump in SAN's.
This document discusses tools and techniques for troubleshooting Java applications. It begins with an introduction to the speaker, Chris Bailey, and his background in Java monitoring and diagnostics. It then covers various approaches for monitoring memory usage at both the operating system and Java runtime levels, including tools for capturing garbage collection data and heap dumps. Finally, it discusses performance analysis and profiling CPU and lock usage at the application level.
The document summarizes IBM's FlashSystem portfolio of all-flash storage solutions. It highlights several IBM FlashSystem products, including the FlashSystem 900, FlashSystem A9000, FlashSystem V9000, and Storwize V7000F. It discusses the performance, scalability, and data protection capabilities of these solutions. It also provides information on IBM's flash core technology, real-time compression, and software-defined storage offerings.
IBM Power Systems - enabling cloud solutionsDavid Spurway
This document discusses IBM Power Systems and their ability to enable cloud solutions. It provides an overview of Power8 architecture and performance advantages over Intel systems. It also discusses how Power Systems can be used to build hybrid cloud infrastructures with on-premises and off-premises components using technologies like PowerVC and Bluemix. Case studies on Oracle and SAP workloads show Power Systems provide better performance and lower TCO compared to x86 servers.
IBM Spectrum Virtualize is a software defined storage solution that provides storage virtualization, data mobility, protection and copy services. It supports a wide range of storage platforms and can scale to manage over 400 storage arrays. The solution provides agility, efficiency and protection for applications and data.
The document discusses IBM's POWER7 technology and Power 755 server. It provides details on the POWER7 processor including its 8 cores, 32 threads per chip, and 32MB on-chip memory. It compares POWER7's performance against Intel's Nehalem and Westmere processors, noting POWER7's advantages in core count, cache size, memory bandwidth, and scalability. The Power 755 server is highlighted as delivering high performance for HPC workloads with better performance and efficiency than competitors.
Ibm symp14 referentin_barbara koch_power_8 launch bkIBM Switzerland
The document discusses IBM's Power Systems and how they are designed for big data and analytics workloads. Some key points:
- Power8 processors deliver 82x faster insights for business intelligence and analytics workloads compared to x86 servers.
- Power Systems create an open ecosystem for innovation through the OpenPOWER Foundation and enable industry partners to build servers optimized for the Power architecture.
- Power Systems foster open innovation for cloud applications by allowing over 95% of Linux applications written in common languages to run with no code changes.
- Power Systems are optimized for big data and analytics through features like high core counts, large memory and cache sizes, and high bandwidth I/O.
The document discusses storage virtualization using IBM's Storwize V7000 and SVC storage arrays. It provides an overview of the key benefits of storage virtualization such as reducing complexity, improving availability and enabling better use of tiered storage. It also summarizes the history and enhancements of the SVC software, features of Storwize V7000 such as Easy Tier and support for VMware vSphere.
OpenStack and z/VM – What is it and how do I get it?Anderson Bassani
The document discusses OpenStack and how to get it running on z/VM. It provides an overview of OpenStack, describing what it is and who it is for. It then covers specifics of the z/VM OpenStack implementation, including supported features in Nova, Neutron and Cinder. Finally, it outlines the steps to install the z/VM OpenStack appliance, including requirements, downloading the necessary files, and configuring directories.
Safeguarded Copy function that is available with IBM® Spectrum Virtualize software Version
8.4.2 supports the ability to create cyber-resilient point-in-time copies of volumes that cannot
be changed or deleted through user errors, malicious actions, or ransomware attacks. The
system integrates with IBM Copy Services Manager to provide automated backup copies and
data recovery.
This IBM Redpaper publication introduces the features and functions of Safeguarded Copy
function by using several examples.
This document is aimed at pre-sales and post-sales technical support and storage
administrators.
Fujitsu m10 server features and capabilitiessolarisyougood
This document provides an overview of the Fujitsu M10 server product line. It describes the hardware features and capabilities of the Fujitsu M10-1, M10-4, and M10-4S servers including their processors, memory, I/O, storage, and virtualization support. It also discusses the reliability, availability, and serviceability features, and performance advantages for running Oracle databases and SAP workloads on the Fujitsu M10 servers.
Enabling a hardware accelerated deep learning data science experience for Apa...DataWorks Summit
Deep learning techniques are finding significant commercial success in a wide variety of industries. Large unstructured data sets such as images, videos, speech and text are great for deep learning, but impose a lot of demands on computing resources. New types of hardware architectures such as GPUs and faster interconnects (e.g. NVLink), RDMA capable networking interface from Mellanox available on OpenPOWER and IBM POWER systems are enabling practical speedups for deep learning. Data Scientists can intuitively incorporate deep learning capabilities on accelerated hardware using open source components such as Jupyter and Zeppelin notebooks, RStudio, Spark, Python, Docker, and Kubernetes with IBM PowerAI. Jupyter and Apache Zeppelin integrate well with Apache Spark and Hadoop using the Apache Livy project. This session will show some deep learning build and deploy steps using Tensorflow and Caffe in Docker containers running in a hardware accelerated private cloud container service. This session will also show system architectures and best practices for deployments on accelerated hardware. INDRAJIT PODDAR, Senior Technical Staff Member, IBM
This document discusses PowerAI, IBM's platform for artificial intelligence and machine learning. It provides an overview of key AI concepts like training and inference. It then discusses how PowerAI leverages POWER8 CPUs and Nvidia GPUs, particularly the high bandwidth NVLink connection between them, to accelerate deep learning training. It shares benchmark results showing PowerAI can provide 30% faster performance on TensorFlow compared to other systems. The document concludes by explaining how users can easily install and get started with PowerAI and popular deep learning frameworks like Caffe and TensorFlow.
The document describes a 5-day residency program hosted by the OpenPOWER Academic Discussion Group (ADG) at NIE Mysore from June 6-10, 2022. The program aims to bridge industry and academia knowledge in chip design by developing curriculum on OpenPOWER technology and training lab assistants. Engineers and academicians with 5+ years experience in chip design/verification are eligible to participate. They will collaborate on developing course materials and lab exercises to teach undergraduate students in fields like ECE and CSE. The program seeks to help fulfill India's goals in chip design manpower and self-reliance through initiatives like Make in India and the India Semiconductor Mission.
This document provides an overview of digital design and Verilog. It discusses binary numbers and boolean algebra as the foundation of digital systems. It also describes logic gates, combinational and sequential circuits, finite state machines, and datapath and control units. Finally, it introduces Verilog, describing different modeling types like gate level, behavioral, dataflow, and switch level modeling. It positions Verilog as a hardware description language used to more easily design digital circuits compared to manual drawing.
The Libre-SOC Project aims to create an entirely Libre-Licensed, transparently-developed fully auditable Hybrid 3D CPU-GPU-VPU, using the Supercomputer-class OpenPOWER ISA as the foundation.
Our first test ASIC is a 180nm "Fixed-Point" Power ISA v3.0B processor, 5.1mm x 5.9mm, as a proof-of-concept for the team, whose primary expertise is in Software Engineering. Software Engineering training brings a radically different approach to Hardware development: extensive unit tests, source code revision control, automated development tools are normal. Libre Project Management brings even more: bug trackers, mailing lists, auditable IRC logs and a wiki are standard fare for Libre Projects that are simply not normal Industry-Standard practice.
This talk therefore goes through the workflow, from the original HDL through to the GDS-II layout, showing how we were able to keep track of the development that led to the IMEC 180nm tape-out in July 2021. In particular, by following a parallel development process involving "Real" and "Symbolic" Cell Libraries, developed by Chips4Makers, will be shown how our developers did not need to sign a Foundry NDA, but were still able to work side-by-side with a University that did. With this parallel development process, the University upheld their NDA obligations, and Libre-SOC were simultaneously able to honour its Transparency Objectives.
Workload Transformation and Innovations in POWER Architecture Ganesan Narayanasamy
IT Industry is going through two major transformations. One is adaption of AI and tight integration of the same in the commercial applications and enterprise workflow. Two the transformation in software architecture through the concepts like microservices and the cloud native architecture. These transformation alongside the aggressive adaption of IoT/mobile and 5G in all our day today activities is making the world operate in more real time manner which opens-up a new challenge to improve the hardware architecture to adapt to these requirements. These above two major transformation pushes the boundary of the entire systems stack making the designer rethink hardware. This talk presents you a picture of how the enterprise Industry leading POWER architecture is transforming to fulfill the performance demands of these newer generation workloads with primary focus on the AI acceleration on the chip.
July 16th 2021 , Friday for our newest workshop with DoMS, IIT Roorkee, Concept to Solutions using OpenPOWER Stack. It's time to discover advances in #DeepLearning tools and techniques from the world's leading innovators across industries, research, and public speakers.
Register here:
https://lnkd.in/ggxMq2N
This presentation covers two uses cases using OpenPOWER Systems
1. Diabetic Retinopathy using AI on NVIDIA Jetson Nano: The objective is to classify the diabetic level solely on retina image in a remote area with minimum doctor's inference. The model uses VGG16 network architecture and gets trained from scratch on POWER9. The model was deployed on the Jetson Nano board.
1. Classifying Covid positivity using lung X-ray images: The idea is to build ML models to detect positive cases using X-ray images. The model was trained on POWER9, and the application was developed using Python.
IBM Bayesian Optimization Accelerator (BOA) is a do-it-yourself toolkit to apply state-of-the-art Bayesian inferencing techniques and obtain optimal solutions for complex, real-world design simulations without requiring deep machine learning skills. This talk will describe IBM BOA, its differentiation and ease of use, and how researchers can take advantage of it for optimizing any arbitrary HPC simulation.
This presentation covers various partners and collaborators who are currently working with OpenPOWER foundation ,Use cases of OpenPOWER systems in multiple Industries , OpenPOWER Workgroups and OpenCAPI features .
Macromolecular crystallography is an experimental technique allowing to explore 3D atomic structure of proteins, used by academics for research in biology and by pharmaceutical companies in rational drug design. While up to now development of the technique was limited by scientific instruments performance, recently computing performance becomes a key limitation. In my presentation I will present a computing challenge to handle 18 GB/s data stream coming from the new X-ray detector. I will show PSI experiences in applying conventional hardware for the task and why this attempt failed. I will then present how IC 922 server with OpenCAPI enabled FPGA boards allowed to build a sustainable and scalable solution for high speed data acquisition. Finally, I will give a perspective, how the advancement in hardware development will enable better science by users of the Swiss Light Source.
AI in healthcare and Automobile Industry using OpenPOWER/IBM POWER9 systemsGanesan Narayanasamy
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Industires such as Healthcare and Automotive , the AI ladder and AI life cycle and infrastructure architecture considerations.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
Healthcare has became one of the most important aspects of everyones life. Its importance has surged due to the latests outbreaks and due to this latest pandemic it has become mandatory to collaborate to improve everyones Healthcare as soon as possible.
IBM has reacted quickly sharing not only its knowledge but also its Artificial Intelligence Supercomputers all around the world.
Those Supercomputers are helping to prevail this outbreak and also future ones.
They have completely different features compared to proposals from other players of this Supercomputers market.
We will try to make a quick look at the differences of those AI focused Supercomputers and how they can help in the R&D of Healthcare solutions for everyone, from those ones with access to a big IBM AI Supercomputer to those ones with access to only one small IBM AI focused server.
Healthcare has became one of the most important aspects of everyones life. Its importance has surged due to the latests outbreaks and due to this latest pandemic it has become mandatory to collaborate to improve everyones Healthcare as soon as possible.
IBM has reacted quickly sharing not only its knowledge but also its Artificial Intelligence Supercomputers all around the world.
Those Supercomputers are helping to prevail this outbreak and also future ones.
They have completely different features compared to proposals from other players of this Supercomputers market.
We will try to make a quick look at the differences of those AI focused Supercomputers and how they can help in the R&D of Healthcare solutions for everyone, from those ones with access to a big IBM AI Supercomputer to those ones with access to only one small IBM AI focused server.
Moving object recognition (MOR) corresponds to the localization and classification of moving objects in videos. Discriminating moving objects from static objects and background in videos is an essential task for many computer vision applications. MOR has widespread applications in intelligent visual surveillance, intrusion detection, anomaly detection and monitoring, industrial sites monitoring, detection-based tracking, autonomous vehicles, etc. In this session, Murari provided a poster about the deep learning algorithms to identify both locations and corresponding categories of moving objects with a convolutional network. The challenges in developing such algorithms have been discussed.
The document discusses AI in the enterprise, including use cases, infrastructure considerations, and the AI lifecycle. It provides examples of how AI can be applied in various industries and common patterns of analytics using AI. It also outlines the data science model development workflow and considerations for AI infrastructure, software, and data management throughout the AI lifecycle.
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
18. Fundamental forces are accelerating change in our industryPrice/Performance
Full system stack innovation required
Moore’s Law
IT innovation can no longer come
from just the processor
Cognitive
Custom Hyperscale
Data Centers
Hybrid Cloud
Open Solutions
IT consumption models
are expanding
Technology and
Processors
2000 2020
Firmware / OS
Accelerators
Software
Storage
NetworkFull Stack
Acceleration (Lower is
better)
18
19. POWER8 Architecture POWER9 Architecture
2014
POWER8
12 cores
22nm
New Micro-
Architecture
New Process
Technology
2016
POWER8
w/ NVLink
12 cores
22nm
Enhanced
Micro-
Architecture
With NVLink
2017
P9 SO
24 cores
14nm
New Micro-
Architecture
Direct attach
memory
New Process
Technology
2018
P9 SU
24 cores
14nm
Enhanced
Micro-
Architecture
Buffered
Memory
POWER7 Architecture
2010
POWER7
8 cores
45nm
New Micro-
Architecture
New Process
Technology
2012
POWER7+
8 cores
32nm
Enhanced
Micro-
Architecture
New Process
Technology
2020+
P10
TBD cores
New Micro-
Architecture
New
Technology
POWER10
2019
P9
w/ Adv. I/O
24 cores
14nm
Enhanced
Micro-
Architecture
New
Memory
Subsystem
Up To
150 GB/s
PCIe Gen4 x48
192GB/s
25 GT/s
300GB/s
CAPI 2.0,
OpenCAPI3.0,
NVLink2.0
Sustained Memory Bandwidth
Standard I/O Interconnect
Advanced I/O Signaling
Advanced I/O Architecture
Up To
210 GB/s
PCIe Gen4 x48
25 GT/s
300GB/s
CAPI 2.0,
OpenCAPI3.0,
NVLink2.0
Up To
350 GB/s
PCIe Gen4 x48
25 GT/s
300GB/s
CAPI 2.0,
OpenCAPI4.0,
NVLink3.0
Up To
435 GB/s
PCIe Gen5
32 & 50 GT/s
TBD
Up To
210 GB/s
PCIe Gen3
N/A
CAPI 1.0
Up To
210 GB/s
PCIe Gen3
20 GT/s
160GB/s
CAPI 1.0 ,
NVLink 1.0
Up To
65 GB/s
PCIe Gen2
N/A
N/A
Up To
65 GB/s
PCIe Gen2
N/A
N/A
Statement of Direction, Subject to Change 19
26. 26
Apache Spark
JVM
Memory
(off-heap)
Serialize /
Deserialize
Network
Disk
A ccelerat or
Native
library
A pplicat ion
Python
libary
Apache Spark
JVM
Shared data set / memory in Arrow format
NetworkStorage
FPGA
A ccelerat or
Native
library
A pplicat ion
Python
tool
A pache A rrow libraries Flet cher
J. Peltenburg, e.a., TU Delft ( OpenPOWER Summit USA 2018 )
27. CAPI
...
......
Host
AXI Interconnect 2:1
AWS F1 Shell
PCIe
DDR
Controller
DDR
SDRAM
(on board)
Fletcher Interconnect (N:1)
Column
Reader
AXI4AXI4
Lite
Bus master side
General dataflow
MMIO
DMA
0 R-1
Accumulators
Regexunit0
Column
Reader
0 R-1
RegexunitN-1
Regex matcher
R no. matches
Layer A
Layer B
AW S EC2 F1
Host Memory
CAPP
SNAP
POW ER8 CA PI
AXI4
Lite
MMIO
AXI4
PSL
PCIe
Off-board components
Non-FPGA components
27
R=16 different regular expressions per unit
AWS EC2 F1:
• Virtex Ultrascale+
• N=16 regex units
• 256 regexes being matched in parallel
POWER8 CAPI (Supervessel, & soon at Nimbix):
• AlphaData KU3 (Kintex Ultrascale)
• N=8 regex units
• 128 regex being matched in parallel
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
log2(Bytes)
0
1
2
3
GB/s
AWS EC2 F1 (16 units)
POWER8+CAPI (8 units)
37. IBM Open Source Based AI Stack
37
Accelerated AC922
Power9 Servers
Storage
(Spectrum Scale ESS)
Watson
Studio
SnapML
WML CE
Runtime Environment
Train, Deploy, Manage Models
Watson
OpenScale
Model Metrics,
Bias, and Fairness
Monitoring
Watson
Machine Learning
Watson ML CE
Watson ML Accelerator
Data Preparation
Model Development
Environment
Auto-AI software: PowerAI Vision, IBM Auto-AI
Previous Names:
WML Accelerator = PowerAI Enterprise
WML Community Ed. = PowerAI-base
Runs on x86 & other storage too
39. +1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
+1
-1
-1
-1
-1
-1
-1
+1
-1
4.2 billion
examples
1 million
features
Goal: Predict whether a user will click on a given advert based
on an anonymized set of features.
Train: Fit model parameters using 4.2 billion examples.
Inference: Evaluate model on 180 million unseen examples.
+1 – click
-1 – no click
Sparse data
matrix
2.3TB
labels
Criteo Labs. 2015. Criteo Releases Industry s Largest-Ever Dataset for Machine Learning to Academic
Community. h ps://www.criteo.com/news/press-releases/2015/07/criteo-releases-industrys-largest-ever-dataset/
*
*
52. | 52
IBM, the IBM logo, ibm.com, IBM System Storage, IBM Spectrum Storage, IBM Spectrum Control, IBM Spectrum Protect, IBM Spectrum Archive, IBM Spectrum Virtualize, IBM Spectrum Scale, IBM Spectrum Accelerate, Softlayer, and XIV are
trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. A current list of IBM trademarks is available on the Web at "Copyright and trademark information" at http://www.ibm.com/legal/copytrade.shtml
The following are trademarks or registered trademarks of 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 Trade Mark of AXELOS Limited.
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.
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.
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.
ITIL is a Registered Trade Mark of AXELOS Limited.
UNIX is a registered trademark of The Open Group in the United States and other countries.
* All other products may be trademarks or registered trademarks of their respective companies.
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.
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 discussed in 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 presentation and the claims outlined in it were reviewed for compliance with US law. Adaptations of these claims for use in other geographies must be reviewed
by the local country counsel for compliance with local laws.
53. | 53
This document was developed for IBM offerings in the United States as of the date of publication. IBM may not make these offerings available in other countries, and the information is
subject to change without notice. Consult your local IBM business contact for information on the IBM offerings available in your area.
Information in this document concerning non-IBM products was obtained from the suppliers of these products or other public sources. Questions on the capabilities of non-IBM products
should be addressed to the suppliers of those products.
IBM may have patents or pending patent applications covering subject matter in this document. The furnishing of this document does not give you any license to these patents. Send
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