A Matrix Math Facility for Power ISA™ Processors
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Development of a Dynamically Extensible Spinnaker Chip Computing Module
FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO Development of a Dynamically Extensible SpiNNaker Chip Computing Module Rui Emanuel Gonçalves Calado Araújo Master in Electrical and Computers Engineering Supervisor: Jörg Conradt Co-Supervisor: Diamantino Freitas January 27, 2014 Resumo O projeto SpiNNaker desenvolveu uma arquitetura que é capaz de criar um sistema com mais de um milhão de núcleos, com o objetivo de simular mais de um bilhão de neurónios em tempo real biológico. O núcleo deste sistema é o "chip" SpiNNaker, um multiprocessador System-on-Chip com um elevado nível de interligação entre as suas unidades de processamento. Apesar de ser uma plataforma de computação com muito potencial, até para aplicações genéricas, atualmente é ape- nas disponibilizada em configurações fixas e requer uma estação de trabalho, como uma máquina tipo "desktop" ou "laptop" conectada através de uma conexão Ethernet, para a sua inicialização e receber o programa e os dados a processar. No sentido de tirar proveito das capacidades do "chip" SpiNNaker noutras áreas, como por exemplo, na área da robótica, nomeadamente no caso de robots voadores ou de tamanho pequeno, uma nova solução de hardware com software configurável tem de ser projetada de forma a poder selecionar granularmente a quantidade do poder de processamento. Estas novas capacidades per- mitem que a arquitetura SpiNNaker possa ser utilizada em mais aplicações para além daquelas para que foi originalmente projetada. Esta dissertação apresenta um módulo de computação dinamicamente extensível baseado em "chips" SpiNNaker com a finalidade de ultrapassar as limitações supracitadas das máquinas SpiN- Naker atualmente disponíveis. Esta solução consiste numa única placa com um microcontrolador, que emula um "chip" SpiNNaker com uma ligação Ethernet, acessível através de uma porta série e com um "chip" SpiNNaker. -
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 -
NVIDIA CUDA on IBM POWER8: Technical Overview, Software Installation, and Application Development
Redpaper Dino Quintero Wei Li Wainer dos Santos Moschetta Mauricio Faria de Oliveira Alexander Pozdneev NVIDIA CUDA on IBM POWER8: Technical overview, software installation, and application development Overview The exploitation of general-purpose computing on graphics processing units (GPUs) and modern multi-core processors in a single heterogeneous parallel system has proven highly efficient for running several technical computing workloads. This applied to a wide range of areas such as chemistry, bioinformatics, molecular biology, engineering, and big data analytics. Recently launched, the IBM® Power System S824L comes into play to explore the use of the NVIDIA Tesla K40 GPU, combined with the latest IBM POWER8™ processor, providing a unique technology platform for high performance computing. This IBM Redpaper™ publication discusses the installation of the system, and the development of C/C++ and Java applications using the NVIDIA CUDA platform for IBM POWER8. Note: CUDA stands for Compute Unified Device Architecture. It is a parallel computing platform and programming model created by NVIDIA and implemented by the GPUs that they produce. The following topics are covered: Advantages of NVIDIA on POWER8 The IBM Power Systems S824L server Software stack System monitoring Application development Tuning and debugging Application examples © Copyright IBM Corp. 2015. All rights reserved. ibm.com/redbooks 1 Advantages of NVIDIA on POWER8 The IBM and NVIDIA partnership was announced in November 2013, for the purpose of integrating IBM POWER®-based systems with NVIDIA GPUs, and enablement of GPU-accelerated applications and workloads. The goal is to deliver higher performance and better energy efficiency to companies and data centers. This collaboration produced its initial results in 2014 with: The announcement of the first IBM POWER8 system featuring NVIDIA Tesla GPUs (IBM Power Systems™ S824L). -
Robust Architectural Support for Transactional Memory in the Power Architecture
Robust Architectural Support for Transactional Memory in the Power Architecture Harold W. Cain∗ Brad Frey Derek Williams IBM Research IBM STG IBM STG Yorktown Heights, NY, USA Austin, TX, USA Austin, TX, USA [email protected] [email protected] [email protected] Maged M. Michael Cathy May Hung Le IBM Research IBM Research (retired) IBM STG Yorktown Heights, NY, USA Yorktown Heights, NY, USA Austin, TX, USA [email protected] [email protected] [email protected] ABSTRACT in current p795 systems, with 8 TB of DRAM), as well as On the twentieth anniversary of the original publication [10], strengths in RAS that differentiate it in the market, adding following ten years of intense activity in the research lit- TM must not compromise any of these virtues. A robust erature, hardware support for transactional memory (TM) system is one that is sturdy in construction, a trait that has finally become a commercial reality, with HTM-enabled does not usually come to mind in respect to HTM systems. chips currently or soon-to-be available from many hardware We structured TM to work in harmony with features that vendors. In this paper we describe architectural support for support the architecture's scalability. Our goal has been to TM provide a comprehensive programming environment includ- TM added to a future version of the Power ISA . Two im- ing support for simple system calls and debug aids, while peratives drove the development: the desire to complement providing a robust (in the sense of "no surprises") execu- our weakly-consistent memory model with a more friendly tion environment with reasonably consistent performance interface to simplify the development and porting of multi- and without unexpected transaction failures.2 TM must be threaded applications, and the need for robustness beyond usable throughout the system stack: in hypervisors, oper- that of some early implementations. -
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. -
Modelling the Armv8 Architecture, Operationally: Concurrency and ISA
Modelling the ARMv8 Architecture, Operationally: Concurrency and ISA Shaked Flur1 Kathryn E. Gray1 Christopher Pulte1 Susmit Sarkar2 Ali Sezgin1 Luc Maranget3 Will Deacon4 Peter Sewell1 1 University of Cambridge, UK 2 University of St Andrews, UK 3 INRIA, France 4 ARM Ltd., UK [email protected] [email protected] [email protected] [email protected] Abstract Keywords Relaxed Memory Models, semantics, ISA In this paper we develop semantics for key aspects of the ARMv8 multiprocessor architecture: the concurrency model and much of the 64-bit application-level instruction set (ISA). Our goal is to 1. Introduction clarify what the range of architecturally allowable behaviour is, and The ARM architecture is the specification of a wide range of pro- thereby to support future work on formal verification, analysis, and cessors: cores designed by ARM that are integrated into devices testing of concurrent ARM software and hardware. produced by many other vendors, and cores designed ab initio by Establishing such models with high confidence is intrinsically ARM architecture partners, such as Nvidia and Qualcomm. The difficult: it involves capturing the vendor’s architectural intent, as- architecture defines the properties on which software can rely on, pects of which (especially for concurrency) have not previously identifying an envelope of behaviour that all these processors are been precisely defined. We therefore first develop a concurrency supposed to conform to. It is thus a central interface in the industry, model with a microarchitectural flavour, abstracting from many between those hardware vendors and software developers. It is also hardware implementation concerns but still close to hardware- a desirable target for software verification and analysis: software designer intuition. -
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. -
POWER10 Processor Chip
POWER10 Processor Chip Technology and Packaging: PowerAXON PowerAXON - 602mm2 7nm Samsung (18B devices) x x 3 SMT8 SMT8 SMT8 SMT8 3 - 18 layer metal stack, enhanced device 2 Core Core Core Core 2 - Single-chip or Dual-chip sockets + 2MB L2 2MB L2 2MB L2 2MB L2 + 4 4 SMP, Memory, Accel, Cluster, PCI Interconnect Cluster,Accel, Memory, SMP, Computational Capabilities: PCI Interconnect Cluster,Accel, Memory, SMP, Local 8MB - Up to 15 SMT8 Cores (2 MB L2 Cache / core) L3 region (Up to 120 simultaneous hardware threads) 64 MB L3 Hemisphere Memory Signaling (8x8 OMI) (8x8 Signaling Memory - Up to 120 MB L3 cache (low latency NUCA mgmt) OMI) (8x8 Signaling Memory - 3x energy efficiency relative to POWER9 SMT8 SMT8 SMT8 SMT8 - Enterprise thread strength optimizations Core Core Core Core - AI and security focused ISA additions 2MB L2 2MB L2 2MB L2 2MB L2 - 2x general, 4x matrix SIMD relative to POWER9 - EA-tagged L1 cache, 4x MMU relative to POWER9 SMT8 SMT8 SMT8 SMT8 Core Core Core Core Open Memory Interface: 2MB L2 2MB L2 2MB L2 2MB L2 - 16 x8 at up to 32 GT/s (1 TB/s) - Technology agnostic support: near/main/storage tiers - Minimal (< 10ns latency) add vs DDR direct attach 64 MB L3 Hemisphere PowerAXON Interface: - 16 x8 at up to 32 GT/s (1 TB/s) SMT8 SMT8 SMT8 SMT8 x Core Core Core Core x - SMP interconnect for up to 16 sockets 3 2MB L2 2MB L2 2MB L2 2MB L2 3 - OpenCAPI attach for memory, accelerators, I/O 2 2 + + - Integrated clustering (memory semantics) 4 4 PCIe Gen 5 PCIe Gen 5 PowerAXON PowerAXON PCIe Gen 5 Interface: Signaling (x16) Signaling