Raghu nambiar:industry standard benchmarks
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BDTC 2013 Beijing China

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Raghu nambiar:industry standard benchmarks Presentation Transcript

  • 1. Industry Standard Benchmarks: Past, Present and Future Raghunath Nambiar Distinguished Engineer, Cisco RNambiar@cisco.com Invited Talk 1
  • 2. • Cisco Distinguished Engineer, Chief Architect, Big Data Solutions, Cisco • General Chair, TPC’s International Conference Series on Performance Evaluation and Benchmarking (TPCTC) • Chairman, TPC Big Data Committee • Industry Chair, IEEE Big Data 2013, ICPE 2014 • Board Member TPC, WBDB, BigDataTop100 2
  • 3. 3
  • 4. • Synthetic Benchmarks  Simulate functions that yield an indicative measure of the subsystem performance  Widely adapted in the industry and academic community  Several open source tools • Application Benchmarks  Developed and administered by application vendors  VMmark, SAP and Oracle application benchmarks • Industry Standard Benchmarks  Driven by industry standard consortia which are represented by vendors, customers, and research organizations  Democratic procedures for all key decision making  TPC, SPEC and SPC 4
  • 5. • Industry standard benchmarks have played, and continue to play, a crucial role in the advancement of the computing industry • Demands for them have existed since buyers were first confronted with the choice between purchasing one system over another • Historically we have seen that industry standard benchmarks enable healthy competition that results in product improvements and the evolution of brand new technologies Better products, Lower PricePerformance 5
  • 6. Critical to Vendors, Customers and Researchers • Vendor  Demonstrate competitiveness of their products  Monitor release-to-release progress of their products under development • Customer  Cross-vendor evaluation of technologies and products in terms of performance, price-performance, energy efficiency • Researcher  Known, measurable, and repeatable workloads to develop and enhance relevant technologies 6
  • 7. Major Activities Benchmark Development Process • Development of new benchmarks • Publication of benchmark results • Refinement of existing benchmarks • Resolution of disputes and challenges Source: Raghunath Nambiar, Meikel Poess: The Making of TPC-DS. VLDB 2006: 1049-1058 7
  • 8. • The TPC is a non-profit, vendor-neutral organization, established in August 1988 • Reputation of providing the most credible performance results to the industry. • Role of “consumer reports” for the computing industry • Solid foundation for complete system-level performance • Methodology for calculating total-system-price and price- performance • Methodology for measuring energy efficiency of complete system Source: Raghunath Nambiar, Matthew Lanken, Nicholas Wakou, Forrest Carman, Michael Majdalany: Transaction Processing Performance Council (TPC): Twenty Years Later - A Look Back, a Look Ahead, First TPC Technology Conference, TPCTC 2009, Lyon, France, ISBN 978-3-642-10423-7 8
  • 9. Benchmark Standards TPC-A TPC-B TPC-C TPC-D TPC-R TPC-H TPC-W TPC-App TPC-E TPC-DS TPC-VMS Common Specifications Pricing Energy Developments in Progress TPC-DI TPC-VMC TPC-V Source: Raghunath Nambiar, Meikel Poess, Andrew Masland, H. Reza Taheri, Matthew Emmerton, Forrest Carman, Michael Majdalany: TPC Benchmark Roadmap 2012, 4th TPC Technology Conference, TPCTC 2012, Istanbul, Turkey, ISBN 978-3-642-36726-7 • Obsolete • Active • Common Specifications • In Progress • • • Developed 11 Benchmark Standards 5 Standards are current What’s new ? • TPC-VMS – new standard for measuring database performance in a virtualized environment TPC-DI - standard for measuring data integration performance. Expected to be standard in 2014 TPC-Big Data committee was formed in October 2013 • • 9
  • 10. Universities and research organizations are encouraged to join the TPC as Associate Members. To join the TPC: http://www.tpc.org/information/about/join.asp 10
  • 11. • The Standard Performance Evaluation Corporation (SPEC) is a non- profit organization established in 1988 • Develop standards for system level performance measurements • History of developing relevant benchmarks to the industry in a timely manner • Four diverse groups - Graphics and Workstation Performance Group (GWPG), High Performance Group (HPG), Open Systems Group (OSG) and Research Group (RG) • Represented by system and software vendors and a number of academic and research organizations 11
  • 12. • SPEC CPU2006 is designed to measure the compute power of systems, contains two benchmark suites: CINT2006 for measuring and comparing compute-intensive integer performance, and CFP2006 for measuring and comparing compute-intensive floating point performance • SPEC MPI2007 is designed for evaluating MPI-parallel, floating point, and computeintensive performance across a wide range of cluster and SMP hardware • SPECjbb2013 measures server performance based on the Java application features by emulating a three-tier client/server system • SPECjEnterprise2010 measures system performance for Java Enterprise Edition based application servers, databases, and supporting infrastructure • SPECsfs2008 is designed to evaluate the speed and request-handling capabilities of file servers utilizing the NFSv3 and CIFS protocols • SPECpower_ssj2008 evaluates the power and performance characteristics of volume server class computers • SPECvirt_sc2010 measures the end-to-end performance of all system components, including the hardware, virtualization platform, virtualized guest operating system, and application software 12
  • 13. • The Storage Performance Council (SPC) is a vendor-neutral consortia established in 2000 • Focused on industry standards for storage system performance • Serve as a catalyst for performance improvement in storage subsystems • Robust methodology measuring, auditing, and publishing performance, price-performance, and energy-efficiency metrics for storage systems • Major systems and storage vendors are members of the SPC 13
  • 14. • SPC Benchmark 1 (SPC-1) consists of a single workload designed to demonstrate the performance of a storage subsystem under OLTP workloads characterized by random reads and writes • SPC Benchmark 1/Energy (SPC-1/E) is an extension of SPC-1 that consists of the complete set of SPC-1 performance measurement and reporting plus the measurement and reporting of energy consumption • SPC Benchmark 2 (SPC-2) consists of three distinct workloads: large file processing, large database queries, and video on-demand simulating the concurrent large-scale sequential movement of data • SPC Benchmark 2/Energy (SPC-2/E) is extension of SPC-2 that consists of the complete set of SPC-2 performance measurement and reporting plus the measurement and reporting of energy consumption • SPC Benchmark 1C (SPC-1C) is based on SPC-1 for storage component products such as disk drives, host bus adapters, storage enclosures, and storage software stacks such as volume managers • SPC Benchmark 1C/Energy (SPC-1C/E) is an extension of SPEC-1C that consists of the complete set of SPC-1C performance measurements and reporting plus measurement and reporting of energy consumption • SPC Benchmark 2C (SPC-2C) is based on SPC-2, predominately by large I/Os organized into one or more concurrent sequential patterns for storage component products • SPC Benchmark 2C/Energy (SPC-2C/E) is an extension of SPC-2C that consists of the complete set of SPC-2 performance measurement and reporting plus the measurement and reporting of energy consumption 14
  • 15. TPC-C Performance 1992-2010 Average tpmC/Processor 1000000 Average tpmC per processor Moore's Law First result using solid state drives (SDD) First result using 15K RPM SAS SFF disk drives 100000 First result using 15K RPM disk drives First Linux result First multi core result 10000 Intel introduces multi threading TPC-C Revision 5 and First x86-64 bit result First storage area network (SAN) based result First result using 7.2K RPM disk drives 1000 First clustered result TPC-C Revision 3, First Windows result and first x86 result 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 100 1993 TPC-C Revision 2 Publication Year Source: Nambiar R., Poess M. (2010). Transaction Performance vs. Moore’s Law. Performance Evaluation, Measurement and Characterization of Complex Systems. Lecture Notes in Computer Science 6417, Springer 2011, ISBN 978-3-642-18205-1 15
  • 16. TPC-C Price-Performance 1992-2010 10000 Price per NtpmC TPC-C Revision 2 1000 Price per NtpmC Moore's Law TPC-C Revision 3, First Windows result and first x86 result First clustered result First result using 7.2 RPM disk drives First storage area network (SAN) based result 100 TPC-C Revision 5 and First x86-64 bit result Intel introduces multi threading First multi core result First Linux result 10 First result using 15 K RPM disk drives First result using 15K RPM SAS SFF disk drives 1 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 0.1 1993 First result using solid state drives SDD Publication Year Source: Nambiar R., Poess M. (2010). Transaction Performance vs. Moore’s Law. Performance Evaluation, Measurement and Characterization of Complex Systems. Lecture Notes in Computer Science 6417, Springer 2011, ISBN 978-3-642-18205-1 16
  • 17. • IT 1.0: 1980-2000  Transaction Processing, Data Warehousing, File server, Web server, Multi-tier Applications • IT 2.0: 2000-2010  Internet centric, Massive scale-out systems, Virtualization, Energy efficient systems • IT 3.0: 2010 Industry Standard Committees have done a great job Call for new standards Cloud, Big Data, Internet of things, Software defined and application centric infrastructure 17
  • 18. 18
  • 19. 34.3% of World’s Population have internet access today 50% by 2020 19
  • 20. 60 50 Billion 40 20 15 Billion 0 2012 2020 Connected Devices There are 15 billion devices connected to the Internet that’s 2.2 devices for every man, woman, and child on the planet earth 50 Billion devices by 2020 Trillion+ with IOT. Source: Cisco, webpronews.com ...... 20
  • 21. Time’s Man of the Year 1982 Source: time.com 21
  • 22. 1. Were a country … 2. India (1.218 billion) 3. Facebook (1 billion) 4. If China (1.339 billion) United States (311 million) 5. Indonesia (237 million) 6. Brazil (190 billion) 7. Pakistan (175 million) 8. Nigeria (158 million) 9. Bangladesh (150 million) 10. Russia (142 million) ...... 22
  • 23. The third generation of IT platform driven by new applications and services built on cloud, mobile devices, social media, IoTs and more Intelligent Economy Big Data/ Social Analytics Business Mobile Cloud of “Things” BroadbandServices Mobile Billions Millions Devices of Users of Apps and Apps Trillions 2011 Hundreds of Millions Examples: Recommendation engines, Personalized contents, Crowd-sourcing LAN/ ClientInternet Server Tens of Thousands PC of Users of Apps 1986 Millions of Users Thousands of Apps Source: IDC 23
  • 24. 2008 0.5 Zettabyte 2011 2.5 Zettabytes 1 Zettabyte 2020 35 Zettabytes = 1 099 511 627 776 Gigabytes = 1 Billion 1TB Disk Drives How many disk drives were sold in 2012 ? Source: IDC, EMC 24
  • 25. Global IP Traffic 616EB Per Capita Internet Traffic Per Capita IP Traffic Per Capita Internet Traffic In 2016, equivalent of all movies ever made will cross global IP networks every 3 minutes Source: Cisco 25
  • 26. 26
  • 27. • Big Data is becoming integral part of IT ecosystem across all major verticals • One of the most talked about topics in research and government sectors • Big Data challenges can be summed up in 5V’s: Volume, Velocity, Varity, Value, Veracity • Big Data is becoming center of 3I’s: Investments, Innovation, Improvization* * Source: http://blogs.cisco.com/datacenter/ieee-bigdata/ 27
  • 28. Source: Gartner 2011 Source: Mckinsey Global Institute Analysis Source: Cisco
  • 29. • TeraSort • YCSB • GridMiX • HiBench • TPC-DS (at large scale ?) • BigBench, BigDataBench 29
  • 30. State of the Nature - early 1980's the industry began a race that has accelerated over time: automation of daily end-user business transactions. The first application that received wide-spread focus was automated teller transactions (ATM), but we've seen this automation trend ripple through almost every area of business, from grocery stores to gas stations. As opposed to the batch-computing model that dominated the industry in the 1960's and 1970's, this new online model of computing had relatively unsophisticated clerks and consumers directly conducting simple update transactions against an on-line database system. Thus, the online transaction processing industry was born, an industry that now represents billions of dollars in annual sales. Early Attempts at Civilized Competition In the April 1, 1985 issue of Datamation, Jim Gray in collaboration with 24 others from academy and industry, published (anonymously) an article titled, "A Measure of Transaction Processing Power." This article outlined a test for on-line transaction processing which was given the title of "DebitCredit." Unlike the TP1 benchmark, Gray's DebitCredit benchmark specified a true system-level benchmark where the network and user interaction components of the workload were included. In addition, it outlined several other key features of the benchmarking process that were later incorporated into the TPC process: The TPC Lays Down the Law While Gray's DebitCredit ideas were widely praised by industry opinion makers, the DebitCredit benchmark had the same success in curbing bad benchmarking as the prohibition did in stopping excessive drinking. In fact, according to industry analysts like Omri Serlin, the situation only got worse. Without a standards body to supervise the testing and publishing, vendors began to publish extraordinary marketing claims on both TP1 and DebitCredit. They often deleted key requirements in DebitCredit to improve their performance results. From 1985 through 1988, vendors used TP1 and DebitCredit--or their own interpretation of these benchmarks--to muddy the already murky performance waters. Omri Serlin had had enough. He spearheaded a campaign to see if this mess could be straightened out. On August 10, 1988, Serlin had successfully convinced eight companies to form the Transaction Processing Performance Council (TPC). 30
  • 31. • Performance • Cost of ownership • Energy efficiency • Floor space efficiency • Manageability • User experience 31
  • 32. • Relevant • Repeatable • Understandable • Fair • Verifiable • Economical Reference: K. Huppler, The Art of Building a Good Benchmark, Performance Evaluation and Benchmarking, LNCS vol. 5895, Springer 2009 • Time to Market – Long development cycle is not acceptable 32
  • 33. • Business Case • Data Definition and Data Generation • Workload • Execution Rules • Metric • Audit Rules • Full Disclosure Report 33
  • 34. • TPC International Technology Conference Series on Performance Evaluation and Benchmarking (TPCTC) • Workshop Series on Big Data Benchmarking (WBDB) • TPC - Big Data Benchmark Work Group (TPC- BD) 34
  • 35. TPC International Technology Conference Series on Performance Evaluation and Benchmarking (TPCTC)  Accelerate the development of relevant benchmark standards  Enable collaboration between industry experts and researchers  Collocated with International Conference on Very Large Data Bases (VLDB) since 2009  TPCTC 2009 in conjunction with VLDB 2009, Lyon, France  TPCTC 2010 in conjunction with VLDB 2010, Singapore  TPCTC 2011 in conjunction with VLDB 2011, Seattle, Washington  TPCTC 2012 in conjunction with VLDB 2012, Istanbul, Turkey  TPCTC 2013 in conjunction with VLDB 2013, Riva Del Garda, Italy  TPCTC 2014 – will collocate with VLDB 2014 in Hangzhou, China (More information available at http://www.tpc.org/tpctc/ 35
  • 36. Workshop Series on Big Data Benchmarking (WBDB) • A first important step towards the development of a set of benchmarks for providing objective measures of the effectiveness of hardware and software systems dealing with Big Data applications • Open forum for discussions issues related to Big Data benchmarking • WBDB Workshops  WBDB 2012, San Jose  WBDB 2012.in, Pune  WBDB 2013.cn, Xi’an  WBDB 2013, San Jose  WBDB 2014, Potsdam, Germany (August 5-6, 2014) • BigData100 36
  • 37. 37
  • 38. • Evaluate big data workload(s) and make recommendations • Four workloads under evaluation • Additional workloads will be considered • Accept one or more benchmarks to address various Big Data use cases • More information: http://www.tpc.org/tpcbd/ 38
  • 39. • TPC is an international organization. Vendors, Customers, Universities, and Research Institutions are invited to join • Membership Benefits  Influence in the TPC benchmarking development process  Timely access to ongoing proceedings  Product Improvement • Memberships  Full membership - Participate in all aspects of the TPC's work, including development of benchmark standards and setting strategic direction.  Associate Membership – Reserved for non-profit, educational institutions, market researchers, publishers, consultants, governments, businesses who do not create, market or sell computer products or services.  Promotional membership for new members • More Information: http://www.tpc.org/information/about/join.asp 39
  • 40. Thank you.