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Investigations on Hardware Compression of IBM Power9 Processors
Investigations on hardware compression of IBM Power9 processors Jérome Kieffer, Pierre Paleo, Antoine Roux, Benoît Rousselle Outline ● The bandwidth issue at synchrotrons sources ● Presentation of the evaluated systems: – Intel Xeon vs IBM Power9 – Benchmarks on bandwidth ● The need for compression of scientific data – Compression as part of HDF5 – The hardware compression engine NX-gzip within Power9 – Gzip performance benchmark – Bitshuffle-LZ4 benchmark – Filter optimizations – Benchmark of parallel filtered gzip ● Conclusions – on the hardware – on the compression pipeline in HDF5 Page 2 HDF5 on Power9 18/09/2019 Bandwidth issue at synchrotrons sources Data analysis computer with the main interconnections and their associated bandwidth. Data reduction Upgrade to → Azimuthal integration 100 Gbit/s Data compression ! Figures from former generation of servers Kieffer et al. Volume 25 | Part 2 | March 2018 | Pages 612–617 | 10.1107/S1600577518000607 Page 3 HDF5 on Power9 18/09/2019 Topologies of Intel Xeon servers in 2019 Source: intel.com Page 4 HDF5 on Power9 18/09/2019 Architecture of the AC922 server from IBM featuring Power9 Credit: Thibaud Besson, IBM France Page 6 HDF5 on Power9 18/09/2019 Bandwidth measurement: Xeon vs Power9 Computer Dell R840 IBM AC922 Processor 4 Intel Xeon (12 cores) 2 IBM Power9 (16 cores) 2.6 GHz 2.7 GHz Cache (L3) 19 MB 8x 10 MB Memory channels 4x 6 DDR4 2x 8 DDR4 Memory capacity → 3TB → 2TB Memory speed theory 512 GB/s 340 GB/s Measured memory speed 160 GB/s 270 GB/s Interconnects PCIe v3 PCIe v4 NVlink2 & CAPI2 GP-GPU co-processor 2Tesla V100 PCIe v3 2Tesla V100 NVlink2 Interconnect speed 12 GB/s 48 GB/s CPU ↔ GPU Page 8 HDF5 on Power9 18/09/2019 Strength and weaknesses of the OpenPower architecture While amd64 is today’s de facto standard in HPC, it has a few competitors: arm64, ppc64le and to a less extend riscv and mips64. -
Wind Rose Data Comes in the Form >200,000 Wind Rose Images
Making Wind Speed and Direction Maps Rich Stromberg Alaska Energy Authority [email protected]/907-771-3053 6/30/2011 Wind Direction Maps 1 Wind rose data comes in the form of >200,000 wind rose images across Alaska 6/30/2011 Wind Direction Maps 2 Wind rose data is quantified in very large Excel™ spreadsheets for each region of the state • Fields: X Y X_1 Y_1 FILE FREQ1 FREQ2 FREQ3 FREQ4 FREQ5 FREQ6 FREQ7 FREQ8 FREQ9 FREQ10 FREQ11 FREQ12 FREQ13 FREQ14 FREQ15 FREQ16 SPEED1 SPEED2 SPEED3 SPEED4 SPEED5 SPEED6 SPEED7 SPEED8 SPEED9 SPEED10 SPEED11 SPEED12 SPEED13 SPEED14 SPEED15 SPEED16 POWER1 POWER2 POWER3 POWER4 POWER5 POWER6 POWER7 POWER8 POWER9 POWER10 POWER11 POWER12 POWER13 POWER14 POWER15 POWER16 WEIBC1 WEIBC2 WEIBC3 WEIBC4 WEIBC5 WEIBC6 WEIBC7 WEIBC8 WEIBC9 WEIBC10 WEIBC11 WEIBC12 WEIBC13 WEIBC14 WEIBC15 WEIBC16 WEIBK1 WEIBK2 WEIBK3 WEIBK4 WEIBK5 WEIBK6 WEIBK7 WEIBK8 WEIBK9 WEIBK10 WEIBK11 WEIBK12 WEIBK13 WEIBK14 WEIBK15 WEIBK16 6/30/2011 Wind Direction Maps 3 Data set is thinned down to wind power density • Fields: X Y • POWER1 POWER2 POWER3 POWER4 POWER5 POWER6 POWER7 POWER8 POWER9 POWER10 POWER11 POWER12 POWER13 POWER14 POWER15 POWER16 • Power1 is the wind power density coming from the north (0 degrees). Power 2 is wind power from 22.5 deg.,…Power 9 is south (180 deg.), etc… 6/30/2011 Wind Direction Maps 4 Spreadsheet calculations X Y POWER1 POWER2 POWER3 POWER4 POWER5 POWER6 POWER7 POWER8 POWER9 POWER10 POWER11 POWER12 POWER13 POWER14 POWER15 POWER16 Max Wind Dir Prim 2nd Wind Dir Sec -132.7365 54.4833 0.643 0.767 1.911 4.083 -
POWER® Processor-Based Systems
IBM® Power® Systems RAS Introduction to IBM® Power® Reliability, Availability, and Serviceability for POWER9® processor-based systems using IBM PowerVM™ With Updates covering the latest 4+ Socket Power10 processor-based systems IBM Systems Group Daniel Henderson, Irving Baysah Trademarks, Copyrights, Notices and Acknowledgements Trademarks IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. These and other IBM trademarked terms are marked on their first occurrence in this information with the appropriate symbol (® or ™), indicating US registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the Web at http://www.ibm.com/legal/copytrade.shtml The following terms are trademarks of the International Business Machines Corporation in the United States, other countries, or both: Active AIX® POWER® POWER Power Power Systems Memory™ Hypervisor™ Systems™ Software™ Power® POWER POWER7 POWER8™ POWER® PowerLinux™ 7® +™ POWER® PowerHA® POWER6 ® PowerVM System System PowerVC™ POWER Power Architecture™ ® x® z® Hypervisor™ Additional Trademarks may be identified in the body of this document. Other company, product, or service names may be trademarks or service marks of others. Notices The last page of this document contains copyright information, important notices, and other information. Acknowledgements While this whitepaper has two principal authors/editors it is the culmination of the work of a number of different subject matter experts within IBM who contributed ideas, detailed technical information, and the occasional photograph and section of description. -
Openpower AI CERN V1.Pdf
Moore’s Law Processor Technology Firmware / OS Linux Accelerator sSoftware OpenStack Storage Network ... Price/Performance POWER8 2000 2020 DRAM Memory Chips Buffer Power8: Up to 12 Cores, up to 96 Threads L1, L2, L3 + L4 Caches Up to 1 TB per socket https://www.ibm.com/blogs/syst Up to 230 GB/s sustained memory ems/power-systems- openpower-enable- bandwidth acceleration/ System System Memory Memory 115 GB/s 115 GB/s POWER8 POWER8 CPU CPU NVLink NVLink 80 GB/s 80 GB/s P100 P100 P100 P100 GPU GPU GPU GPU GPU GPU GPU GPU Memory Memory Memory Memory GPU PCIe CPU 16 GB/s System bottleneck Graphics System Memory Memory IBM aDVantage: data communication and GPU performance POWER8 + 78 ms Tesla P100+NVLink x86 baseD 170 ms GPU system ImageNet / Alexnet: Minibatch size = 128 ADD: Coherent Accelerator Processor Interface (CAPI) FPGA CAPP PCIe POWER8 Processor ...FPGAs, networking, memory... Typical I/O MoDel Flow Copy or Pin MMIO Notify Poll / Int Copy or Unpin Ret. From DD DD Call Acceleration Source Data Accelerator Completion Result Data Completion Flow with a Coherent MoDel ShareD Mem. ShareD Memory Acceleration Notify Accelerator Completion Focus on Enterprise Scale-Up Focus on Scale-Out and Enterprise Future Technology and Performance DriVen Cost and Acceleration DriVen Partner Chip POWER6 Architecture POWER7 Architecture POWER8 Architecture POWER9 Architecture POWER10 POWER8/9 2007 2008 2010 2012 2014 2016 2017 TBD 2018 - 20 2020+ POWER6 POWER6+ POWER7 POWER7+ POWER8 POWER8 P9 SO P9 SU P9 SO 2 cores 2 cores 8 cores 8 cores 12 cores w/ NVLink -
IBM Power Systems Performance Report Apr 13, 2021
IBM Power Performance Report Power7 to Power10 September 8, 2021 Table of Contents 3 Introduction to Performance of IBM UNIX, IBM i, and Linux Operating System Servers 4 Section 1 – SPEC® CPU Benchmark Performance 4 Section 1a – Linux Multi-user SPEC® CPU2017 Performance (Power10) 4 Section 1b – Linux Multi-user SPEC® CPU2017 Performance (Power9) 4 Section 1c – AIX Multi-user SPEC® CPU2006 Performance (Power7, Power7+, Power8) 5 Section 1d – Linux Multi-user SPEC® CPU2006 Performance (Power7, Power7+, Power8) 6 Section 2 – AIX Multi-user Performance (rPerf) 6 Section 2a – AIX Multi-user Performance (Power8, Power9 and Power10) 9 Section 2b – AIX Multi-user Performance (Power9) in Non-default Processor Power Mode Setting 9 Section 2c – AIX Multi-user Performance (Power7 and Power7+) 13 Section 2d – AIX Capacity Upgrade on Demand Relative Performance Guidelines (Power8) 15 Section 2e – AIX Capacity Upgrade on Demand Relative Performance Guidelines (Power7 and Power7+) 20 Section 3 – CPW Benchmark Performance 19 Section 3a – CPW Benchmark Performance (Power8, Power9 and Power10) 22 Section 3b – CPW Benchmark Performance (Power7 and Power7+) 25 Section 4 – SPECjbb®2015 Benchmark Performance 25 Section 4a – SPECjbb®2015 Benchmark Performance (Power9) 25 Section 4b – SPECjbb®2015 Benchmark Performance (Power8) 25 Section 5 – AIX SAP® Standard Application Benchmark Performance 25 Section 5a – SAP® Sales and Distribution (SD) 2-Tier – AIX (Power7 to Power8) 26 Section 5b – SAP® Sales and Distribution (SD) 2-Tier – Linux on Power (Power7 to Power7+) -
Towards a Portable Hierarchical View of Distributed Shared Memory Systems: Challenges and Solutions
Towards A Portable Hierarchical View of Distributed Shared Memory Systems: Challenges and Solutions Millad Ghane Sunita Chandrasekaran Margaret S. Cheung Department of Computer Science Department of Computer and Information Physics Department University of Houston Sciences University of Houston TX, USA University of Delaware Center for Theoretical Biological Physics, [email protected],[email protected] DE, USA Rice University [email protected] TX, USA [email protected] Abstract 1 Introduction An ever-growing diversity in the architecture of modern super- Heterogeneity has become increasingly prevalent in the recent computers has led to challenges in developing scientifc software. years given its promising role in tackling energy and power con- Utilizing heterogeneous and disruptive architectures (e.g., of-chip sumption crisis of high-performance computing (HPC) systems [15, and, in the near future, on-chip accelerators) has increased the soft- 20]. Dennard scaling [14] has instigated the adaptation of heteroge- ware complexity and worsened its maintainability. To that end, we neous architectures in the design of supercomputers and clusters need a productive software ecosystem that improves the usability by the HPC community. The July 2019 TOP500 [36] report shows and portability of applications for such systems while allowing how 126 systems in the list are heterogeneous systems confgured every parallelism opportunity to be exploited. with one or many GPUs. This is the prevailing trend in current gen- In this paper, we outline several challenges that we encountered eration of supercomputers. As an example, Summit [31], the fastest in the implementation of Gecko, a hierarchical model for distributed supercomputer according to the Top500 list (June 2019) [36], has shared memory architectures, using a directive-based program- two IBM POWER9 processors and six NVIDIA Volta V100 GPUs. -
Upgrade to POWER9 Planning Checklist
Achieve your full business potential with IBM POWER9 Future-forward infrastructure designed to crush data-intensive workloads Upgrade to POWER9 Planning Checklist Using this checklist will help to ensure that your infrastructure strategy is aligned to your needs, avoiding potential cost overruns or capability shortfalls. 1 Determine current and future capacity 5 Identify all dependencies for major database requirements. Bring your team together, platforms, including Oracle, DB2, SAP HANA, assess your current application workload and open-source databases like EnterpriseDB, requirements and three- to five-year MongoDB, neo4j, and Redis. You’re likely outlook. You’ll then have a good picture running major databases on the Power of when and where application growth Systems platform; co-locating your current will take place, enabling you to secure servers may be a way to reduce expenditure capacity at the appropriate time on an and increase flexibility. as-needed basis. 6 Understand current and future data center 2 Assess operational efficiencies and identify environmental requirements. You may be opportunities to improve service levels unnecessarily overspending on power, while decreasing exposure to security and cooling and space. Savings here will help your compliancy issues/problems. With new organization avoid costs associated with data technologies that allow you to easily adjust center expansion. capacity, you will be in a much better position to lower costs, improve service Identify the requirements of your strategy for levels, and increase efficiency. 7 on- and off-premises cloud infrastructure. As you move to the cloud, ensure you 3 Create a detailed inventory of servers have a strong strategy to determine which across your entire IT infrastructure. -
AC922 Data Movement for CORAL
AC922 Data Movement for CORAL Roberts, Steve Ramanna, Pradeep Walthour, John IBM IBM IBM Cognitive Systems Cognitive Systems Cognitive Systems Austin, TX Austin, TX Austin, TX [email protected] [email protected] [email protected] Abstract—Recent publications have considered the challenge of and GPU processing elements associated with their respective movement in and out of the high bandwidth memory in attempt DRAM or high bandwidth memory (HBM2). Each processor to maximize GPU utilization and minimize overall application element creates a NUMA domain which in total encompasses wall time. This paper builds on previous contributions, [5] [17], which simulate software models, advocated optimization, and over > 2PB worth of total memory (see Table I for total suggest design considerations. This contribution characterizes the capacity). data movement innovations of the AC922 nodes IBM delivered to Oak Ridge National Labs and Lawrence Livermore National TABLE I Labs as part of the 2014 Collaboration of Oak Ridge, Argonne, CORAL SYSTEMS MEMORY SUMMARY and Livermore (CORAL) joint procurement activity. With a single HPC system able to perform up to 200PF of processing Lab Nodes Sockets DRAM (TB) GPUs HBM2 (TB) with access to 2.5PB of memory, this architecture motivates a ORNL 4607 9216 2,304 27648 432 careful look at data movement. The AC922 POWER9 system LLNL 4320 8640 2,160 17280 270 with NVIDIA V100 GPUs have cache line granularity, more than double the bandwidth of PCIe Gen3, low latency interfaces Efficient programming models call for accessing system and are interconnected by dual-rail Mellanox CAPI/EDR HCAs. memory with as little data replication as possible and As such, the bandwidth and latency assumptions from previous with low instruction overhead. -
Power8 Quser Mspl Nov 2015 Handout
IBM Power Systems Power Systems Hardware: Today and Tomorrow November 2015 Mark Olson [email protected] © 2015 IBM Corporation IBM Power Systems POWER8 Chip © 2015 IBM Corporation IBM Power Systems Processor Technology Roadmap POWER11 Or whatever it is POWER10 named Or whatever it is POWER9 named Or whatever it is named POWER7 POWER8 POWER6 45 nm 22 nm POWER5 65 nm 130 nm POWER4 90 nm 180 nm 130 nm 2001 2004 2007 2010 2014 Future 3 © 2015 IBM Corporation IBM Power Systems Processor Chip Comparisons POWER5 POWER6 POWER7 POWER7+ POWER8 2004 2007 2010 2012 45nm SOI 32nm SOI 22nm SOI Technology 130nm SOI 65nm SOI eDRAM eDRAM eDRAM Compute Cores 2 2 8 8 12 Threads SMT2 SMT2 SMT4 SMT4 SMT8 Caching On-chip 1.9MB (L2) 8MB (L2) 2 + 32MB (L2+3) 2 + 80MB (L2+3) 6 + 96MB (L2+3) Off-chip 36MB (L3) 32MB (L3) None None 128MB (L4) Bandwidth Sust. Mem. 15GB/s 30GB/s 100GB/s 100GB/s 230GB/s Peak I/O 6GB/s 20GB/s 40GB/s 40GB/s 96GB/s 4 © 2015 IBM Corporation IBM Power Systems Processor Designs POWER5+ POWER6 POWER7 POWER7+ POWER8 Max cores 4 2 8 8 12 Technology 90nm 65nm 45nm 32nm 22nm Size 245 mm2 341 mm2 567 mm2 567 mm2 650 mm2 * Transistors 276 M 790 M 1.2 B 2.1 B 4.2 B * 1.9 4 - 5 3 – 4 Up to 4.4 Up to 4.1 Frequencies GHz GHz GHz GHz GHz ** SMT (threads) 2 2 4 4 8 L2 Cache 1.9MB Shared 4MB / Core 256KB / core 256KB / core 512KB/core 4MB / Core 10MB / Core 8MB / Core L3 Cache 36MB 32MB On chip On chip On chip L4 Cache -- -- -- -- Up to 128MB Bandwidth Sust memory 15GB/s 30GB/s 100GB/s 100GB/s 230GB/s Peak I/O 6GB/s 20GB/s 40GB/s 40GB/s 96GB/s * with 12-core -
A Bibliography of Publications in IEEE Micro
A Bibliography of Publications in IEEE Micro Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 16 September 2021 Version 2.108 Title word cross-reference -Core [MAT+18]. -Cubes [YW94]. -D [ASX19, BWMS19, DDG+19, Joh19c, PZB+19, ZSS+19]. -nm [ABG+16, KBN16, TKI+14]. #1 [Kah93i]. 0.18-Micron [HBd+99]. 0.9-micron + [Ano02d]. 000-fps [KII09]. 000-Processor $1 [Ano17-58, Ano17-59]. 12 [MAT 18]. 16 + + [ABG+16]. 2 [DTH+95]. 21=2 [Ste00a]. 28 [BSP 17]. 024-Core [JJK 11]. [KBN16]. 3 [ASX19, Alt14e, Ano96o, + AOYS95, BWMS19, CMAS11, DDG+19, 1 [Ano98s, BH15, Bre10, PFC 02a, Ste02a, + + Ste14a]. 1-GHz [Ano98s]. 1-terabits DFG 13, Joh19c, LXB07, LX10, MKT 13, + MAS+07, PMM15, PZB+19, SYW+14, [MIM 97]. 10 [Loc03]. 10-Gigabit SCSR93, VPV12, WLF+08, ZSS+19]. 60 [Gad07, HcF04]. 100 [TKI+14]. < [BMM15]. > [BMM15]. 2 [Kir84a, Pat84, PSW91, YSMH91, ZACM14]. [WHCK18]. 3 [KBW95]. II [BAH+05]. ∆ 100-Mops [PSW91]. 1000 [ES84]. 11- + [Lyl04]. 11/780 [Abr83]. 115 [JBF94]. [MKG 20]. k [Eng00j]. µ + [AT93, Dia95c, TS95]. N [YW94]. x 11FO4 [ASD 05]. 12 [And82a]. [DTB01, Dur96, SS05]. 12-DSP [Dur96]. 1284 [Dia94b]. 1284-1994 [Dia94b]. 13 * [CCD+82]. [KW02]. 1394 [SB00]. 1394-1955 [Dia96d]. 1 2 14 [WD03]. 15 [FD04]. 15-Billion-Dollar [KR19a]. -
IBM's Next Generation POWER Processor
IBM’s Next Generation POWER Processor Hot Chips August 18-20, 2019 Jeff Stuecheli Scott Willenborg William Starke Proposed POWER Processor Technology and I/O Roadmap Focus of 2018 talk POWER7 Architecture POWER8 Architecture POWER9 Architecture POWER10 2010 2012 2014 2016 2017 2018 2020 2021 POWER7 POWER7+ POWER8 POWER8 P9 SO P9 SU P9 AIO P10 8 cores 8 cores 12 cores w/ NVLink 12/24 cores 12/24 cores 12/24 cores TBA cores 45nm 32nm 22nm 12 cores 14nm 14nm 14nm 22nm New Micro- New Micro- Enhanced Enhanced New Micro- Enhanced Enhanced Micro- New Micro- Architecture Micro- Architecture Architecture Micro- Micro- Architecture Architecture Architecture Architecture Architecture With NVLink Direct attach memory Buffered New New Process Memory Memory New Process New Process Subsystem New Process Technology Technology Technology New Process Technology Technology Up To Up To Up To Up To Up To Up To Up To Up To Sustained Memory Bandwidth 65 GB/s 65 GB/s 210 GB/s 210 GB/s 150 GB/s 210 GB/s 650 GB/s 800 GB/s Standard I/O Interconnect PCIe Gen2 PCIe Gen2 PCIe Gen3 PCIe Gen3 PCIe Gen4 x48 PCIe Gen4 x48 PCIe Gen4 x48 PCIe Gen5 20 GT/s 25 GT/s Advanced I/O Signaling N/A N/A N/A 25 GT/s 25 GT/s 32 & 50 GT/s 160GB/s 300GB/s 300GB/s 300GB/s CAPI 2.0, CAPI 2.0, CAPI 2.0, CAPI 1.0 , Advanced I/O Architecture N/A N/A CAPI 1.0 OpenCAPI3.0, OpenCAPI3.0, OpenCAPI4.0, TBA NVLink NVLink NVLink NVLink © 2019 IBM Corporation Statement of Direction, Subject to Change 2 Proposed POWER Processor Technology and I/O Roadmap Focus of today’s talk POWER7 Architecture POWER8 -
Ilore: Discovering a Lineage of Microprocessors
iLORE: Discovering a Lineage of Microprocessors Samuel Lewis Furman Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Computer Science & Applications Kirk Cameron, Chair Godmar Back Margaret Ellis May 24, 2021 Blacksburg, Virginia Keywords: Computer history, systems, computer architecture, microprocessors Copyright 2021, Samuel Lewis Furman iLORE: Discovering a Lineage of Microprocessors Samuel Lewis Furman (ABSTRACT) Researchers, benchmarking organizations, and hardware manufacturers maintain repositories of computer component and performance information. However, this data is split across many isolated sources and is stored in a form that is not conducive to analysis. A centralized repository of said data would arm stakeholders across industry and academia with a tool to more quantitatively understand the history of computing. We propose iLORE, a data model designed to represent intricate relationships between computer system benchmarks and computer components. We detail the methods we used to implement and populate the iLORE data model using data harvested from publicly available sources. Finally, we demonstrate the validity and utility of our iLORE implementation through an analysis of the characteristics and lineage of commercial microprocessors. We encourage the research community to interact with our data and visualizations at csgenome.org. iLORE: Discovering a Lineage of Microprocessors Samuel Lewis Furman (GENERAL AUDIENCE ABSTRACT) Researchers, benchmarking organizations, and hardware manufacturers maintain repositories of computer component and performance information. However, this data is split across many isolated sources and is stored in a form that is not conducive to analysis. A centralized repository of said data would arm stakeholders across industry and academia with a tool to more quantitatively understand the history of computing.