Coprocessor Design to Support MPI Primitives in Configurable
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
X86 Platform Coprocessor/Prpmc (PC on a PMC)
x86 Platform Coprocessor/PrPMC (PC on a PMC) 32b/33MHz PCI bus PN1/PN2 +5V to Kbd/Mouse/USB power Vcore Power +3.3V +2.5v Conditioning +3.3VIO 1K100 FPGA & 512KB 10/100TX TMS2250 PCI BIOS/PLA Ethernet RJ45 Compact to PCI bridge Flash Memory PLA I/O Flash site 8 32b/33MHz Internal PCI bus Analog SVGA Video Pwr Seq AMD SC2200 Signal COM 1 (RXD/TXD only) IDE "GEODE (tm)" Conditioning COM 2 (RXD/TXD only) Processor USB Port 1 Rear I/O PN4 I/O Rear 64 USB Port 2 LPC Keyboard/Mouse Floppy 36 Pin 36 Pin Connector PC87364 128MB SDRAM Status LEDs Super I/O (16MWx64b) PC Spkr This board is a Processor PMC (PrPMC) implementing A PrPMC implements a TMS2250 PCI-to-PCI bridge to an x86-based PC architecture on a single-wide, stan- the host, and a “Coprocessor” cofniguration implements dard height PMC form factor. This delivers a complete back-to-back 16550 compatible COM ports. The “Mon- x86 platform with comprehensive I/O support, a 10/100- arch” signal selects either PrPMC or Co-processor TX Ethernet interface, and disk access (both floppy and mode. IDE drives). The board features an on-board hard drive using Compact Flash. A 512Kbyte FLASH memory holds the 1K100 PLA im- age that is automatically loaded on power up. It also At the heart of the design is an Advanced Micro De- contains the General Software Embedded BIOS 2000™ vices SC2200 GEODE™ processor that integrates video, BIOS code that is included with each board. -
Convey Overview
THE WORLD’S FIRST HYBRID-CORE COMPUTER. CONVEY HYBRID-CORE COMPUTING Hybrid-core Computing Convey HC-1 High Performance of application- specific hardware Heterogenous solutions • can be much more efficient Performance/ • still hard to program Programmability and Power efficiency deployment ease of an x86 server Application Multicore solutions • don’t always scale well • parallel programming is hard Low Difficult Ease of Deployment Easy 1/22/2010 3 Hybrid-Core Computing Application-Specific Personalities Applications • Extend the x86 instruction set • Implement key operations in Convey Compilers hardware Life Sciences x86-64 ISA Custom ISA CAE Custom Financial Oil & Gas Shared Virtual Memory Cache-coherent, shared memory • Both ISAs address common memory *ISA: Instruction Set Architecture 7/12/2010 4 HC-1 Hardware PCI I/O FPGA FPGA Intel Personalities Chipset FPGA FPGA 8 GB/s 80 GB/s Memory Memory Cache Coherent, Shared Virtual Memory 1/22/2010 5 Using Personalities C/C++ Fortran • Personalities are user specifies reloadable personality at instruction sets Convey Software compile time Development Suite • Compiler instruction generates x86 descriptions and coprocessor instructions from Hybrid-Core Executable P ANSI standard x86-64 and Coprocessor Personalities C/C++ & Fortran Instructions • Executable can run on x86 nodes FPGA Convey HC-1 or Convey Hybrid- bitfiles Core nodes Intel x86 Coprocessor personality loaded at runtime by OS 1/22/2010 6 SYSTEM ARCHITECTURE HC-1 Architecture “Commodity” Intel Server Convey FPGA-based coprocessor Direct -
Exploiting Free Silicon for Energy-Efficient Computing Directly
Exploiting Free Silicon for Energy-Efficient Computing Directly in NAND Flash-based Solid-State Storage Systems Peng Li Kevin Gomez David J. Lilja Seagate Technology Seagate Technology University of Minnesota, Twin Cities Shakopee, MN, 55379 Shakopee, MN, 55379 Minneapolis, MN, 55455 [email protected] [email protected] [email protected] Abstract—Energy consumption is a fundamental issue in today’s data A well-known solution to the memory wall issue is moving centers as data continue growing dramatically. How to process these computing closer to the data. For example, Gokhale et al [2] data in an energy-efficient way becomes more and more important. proposed a processor-in-memory (PIM) chip by adding a processor Prior work had proposed several methods to build an energy-efficient system. The basic idea is to attack the memory wall issue (i.e., the into the main memory for computing. Riedel et al [9] proposed performance gap between CPUs and main memory) by moving com- an active disk by using the processor inside the hard disk drive puting closer to the data. However, these methods have not been widely (HDD) for computing. With the evolution of other non-volatile adopted due to high cost and limited performance improvements. In memories (NVMs), such as phase-change memory (PCM) and spin- this paper, we propose the storage processing unit (SPU) which adds computing power into NAND flash memories at standard solid-state transfer torque (STT)-RAM, researchers also proposed to use these drive (SSD) cost. By pre-processing the data using the SPU, the data NVMs as the main memory for data-intensive applications [10] to that needs to be transferred to host CPUs for further processing improve the system energy-efficiency. -
CUDA What Is GPGPU
What is GPGPU ? • General Purpose computation using GPU in applications other than 3D graphics CUDA – GPU accelerates critical path of application • Data parallel algorithms leverage GPU attributes – Large data arrays, streaming throughput Slides by David Kirk – Fine-grain SIMD parallelism – Low-latency floating point (FP) computation • Applications – see //GPGPU.org – Game effects (FX) physics, image processing – Physical modeling, computational engineering, matrix algebra, convolution, correlation, sorting Previous GPGPU Constraints CUDA • Dealing with graphics API per thread per Shader Input Registers • “Compute Unified Device Architecture” – Working with the corner cases per Context • General purpose programming model of the graphics API Fragment Program Texture – User kicks off batches of threads on the GPU • Addressing modes Constants – GPU = dedicated super-threaded, massively data parallel co-processor – Limited texture size/dimension Temp Registers • Targeted software stack – Compute oriented drivers, language, and tools Output Registers • Shader capabilities • Driver for loading computation programs into GPU FB Memory – Limited outputs – Standalone Driver - Optimized for computation • Instruction sets – Interface designed for compute - graphics free API – Data sharing with OpenGL buffer objects – Lack of Integer & bit ops – Guaranteed maximum download & readback speeds • Communication limited – Explicit GPU memory management – Between pixels – Scatter a[i] = p 1 Parallel Computing on a GPU Extended C • Declspecs • NVIDIA GPU Computing Architecture – global, device, shared, __device__ float filter[N]; – Via a separate HW interface local, constant __global__ void convolve (float *image) { – In laptops, desktops, workstations, servers GeForce 8800 __shared__ float region[M]; ... • Keywords • 8-series GPUs deliver 50 to 200 GFLOPS region[threadIdx] = image[i]; on compiled parallel C applications – threadIdx, blockIdx • Intrinsics __syncthreads() – __syncthreads .. -
Comparing the Power and Performance of Intel's SCC to State
Comparing the Power and Performance of Intel’s SCC to State-of-the-Art CPUs and GPUs Ehsan Totoni, Babak Behzad, Swapnil Ghike, Josep Torrellas Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA E-mail: ftotoni2, bbehza2, ghike2, [email protected] Abstract—Power dissipation and energy consumption are be- A key architectural challenge now is how to support in- coming increasingly important architectural design constraints in creasing parallelism and scale performance, while being power different types of computers, from embedded systems to large- and energy efficient. There are multiple options on the table, scale supercomputers. To continue the scaling of performance, it is essential that we build parallel processor chips that make the namely “heavy-weight” multi-cores (such as general purpose best use of exponentially increasing numbers of transistors within processors), “light-weight” many-cores (such as Intel’s Single- the power and energy budgets. Intel SCC is an appealing option Chip Cloud Computer (SCC) [1]), low-power processors (such for future many-core architectures. In this paper, we use various as embedded processors), and SIMD-like highly-parallel archi- scalable applications to quantitatively compare and analyze tectures (such as General-Purpose Graphics Processing Units the performance, power consumption and energy efficiency of different cutting-edge platforms that differ in architectural build. (GPGPUs)). These platforms include the Intel Single-Chip Cloud Computer The Intel SCC [1] is a research chip made by Intel Labs (SCC) many-core, the Intel Core i7 general-purpose multi-core, to explore future many-core architectures. It has 48 Pentium the Intel Atom low-power processor, and the Nvidia ION2 (P54C) cores in 24 tiles of two cores each. -
World's First High- Performance X86 With
World’s First High- Performance x86 with Integrated AI Coprocessor Linley Spring Processor Conference 2020 April 8, 2020 G Glenn Henry Dr. Parviz Palangpour Chief AI Architect AI Software Deep Dive into Centaur’s New x86 AI Coprocessor (Ncore) • Background • Motivations • Constraints • Architecture • Software • Benchmarks • Conclusion Demonstrated Working Silicon For Video-Analytics Edge Server Nov, 2019 ©2020 Centaur Technology. All Rights Reserved Centaur Technology Background • 25-year-old startup in Austin, owned by Via Technologies • We design, from scratch, low-cost x86 processors • Everything to produce a custom x86 SoC with ~100 people • Architecture, logic design and microcode • Design, verification, and layout • Physical build, fab interface, and tape-out • Shipped by IBM, HP, Dell, Samsung, Lenovo… ©2020 Centaur Technology. All Rights Reserved Genesis of the AI Coprocessor (Ncore) • Centaur was developing SoC (CHA) with new x86 cores • Targeted at edge/cloud server market (high-end x86 features) • Huge inference markets beyond hyperscale cloud, IOT and mobile • Video analytics, edge computing, on-premise servers • However, x86 isn’t efficient at inference • High-performance inference requires external accelerator • CHA has 44x PCIe to support GPUs, etc. • But adds cost, power, another point of failure, etc. ©2020 Centaur Technology. All Rights Reserved Why not integrate a coprocessor? • Very low cost • Many components already on SoC (“free” to Ncore) Caches, memory, clock, power, package, pins, busses, etc. • There often is “free” space on complex SOCs due to I/O & pins • Having many high-performance x86 cores allows flexibility • The x86 cores can do some of the work, in parallel • Didn’t have to implement all strange/new functions • Allows fast prototyping of new things • For customer: nothing extra to buy ©2020 Centaur Technology. -
Through the Years… When Did It All Begin?
& Through the years… When did it all begin? 1974? 1978? 1963? 2 CDC 6600 – 1974 NERSC started service with the first Supercomputer… ● A well-used system - Serial Number 1 ● On its last legs… ● Designed and built in Chippewa Falls ● Launch Date: 1963 ● Load / Store Architecture ● First RISC Computer! ● First CRT Monitor ● Freon Cooled ● State-of-the-Art Remote Access at NERSC ● Via 4 acoustic modems, manually answered capable of 10 characters /sec 3 50th Anniversary of the IBM / Cray Rivalry… Last week, CDC had a press conference during which they officially announced their 6600 system. I understand that in the laboratory developing this system there are only 32 people, “including the janitor”… Contrasting this modest effort with our vast development activities, I fail to understand why we have lost our industry leadership position by letting someone else offer the world’s most powerful computer… T.J. Watson, August 28, 1963 4 2/6/14 Cray Higher-Ed Roundtable, July 22, 2013 CDC 7600 – 1975 ● Delivered in September ● 36 Mflop Peak ● ~10 Mflop Sustained ● 10X sustained performance vs. the CDC 6600 ● Fast memory + slower core memory ● Freon cooled (again) Major Innovations § 65KW Memory § 36.4 MHz clock § Pipelined functional units 5 Cray-1 – 1978 NERSC transitions users ● Serial 6 to vector architectures ● An fairly easy transition for application writers ● LTSS was converted to run on the Cray-1 and became known as CTSS (Cray Time Sharing System) ● Freon Cooled (again) ● 2nd Cray 1 added in 1981 Major Innovations § Vector Processing § Dependency -
The Gemini Network
The Gemini Network Rev 1.1 Cray Inc. © 2010 Cray Inc. All Rights Reserved. Unpublished Proprietary Information. This unpublished work is protected by trade secret, copyright and other laws. Except as permitted by contract or express written permission of Cray Inc., no part of this work or its content may be used, reproduced or disclosed in any form. Technical Data acquired by or for the U.S. Government, if any, is provided with Limited Rights. Use, duplication or disclosure by the U.S. Government is subject to the restrictions described in FAR 48 CFR 52.227-14 or DFARS 48 CFR 252.227-7013, as applicable. Autotasking, Cray, Cray Channels, Cray Y-MP, UNICOS and UNICOS/mk are federally registered trademarks and Active Manager, CCI, CCMT, CF77, CF90, CFT, CFT2, CFT77, ConCurrent Maintenance Tools, COS, Cray Ada, Cray Animation Theater, Cray APP, Cray Apprentice2, Cray C90, Cray C90D, Cray C++ Compiling System, Cray CF90, Cray EL, Cray Fortran Compiler, Cray J90, Cray J90se, Cray J916, Cray J932, Cray MTA, Cray MTA-2, Cray MTX, Cray NQS, Cray Research, Cray SeaStar, Cray SeaStar2, Cray SeaStar2+, Cray SHMEM, Cray S-MP, Cray SSD-T90, Cray SuperCluster, Cray SV1, Cray SV1ex, Cray SX-5, Cray SX-6, Cray T90, Cray T916, Cray T932, Cray T3D, Cray T3D MC, Cray T3D MCA, Cray T3D SC, Cray T3E, Cray Threadstorm, Cray UNICOS, Cray X1, Cray X1E, Cray X2, Cray XD1, Cray X-MP, Cray XMS, Cray XMT, Cray XR1, Cray XT, Cray XT3, Cray XT4, Cray XT5, Cray XT5h, Cray Y-MP EL, Cray-1, Cray-2, Cray-3, CrayDoc, CrayLink, Cray-MP, CrayPacs, CrayPat, CrayPort, Cray/REELlibrarian, CraySoft, CrayTutor, CRInform, CRI/TurboKiva, CSIM, CVT, Delivering the power…, Dgauss, Docview, EMDS, GigaRing, HEXAR, HSX, IOS, ISP/Superlink, LibSci, MPP Apprentice, ND Series Network Disk Array, Network Queuing Environment, Network Queuing Tools, OLNET, RapidArray, RQS, SEGLDR, SMARTE, SSD, SUPERLINK, System Maintenance and Remote Testing Environment, Trusted UNICOS, TurboKiva, UNICOS MAX, UNICOS/lc, and UNICOS/mp are trademarks of Cray Inc. -
Introduction to Cpu
microprocessors and microcontrollers - sadri 1 INTRODUCTION TO CPU Mohammad Sadegh Sadri Session 2 Microprocessor Course Isfahan University of Technology Sep., Oct., 2010 microprocessors and microcontrollers - sadri 2 Agenda • Review of the first session • A tour of silicon world! • Basic definition of CPU • Von Neumann Architecture • Example: Basic ARM7 Architecture • A brief detailed explanation of ARM7 Architecture • Hardvard Architecture • Example: TMS320C25 DSP microprocessors and microcontrollers - sadri 3 Agenda (2) • History of CPUs • 4004 • TMS1000 • 8080 • Z80 • Am2901 • 8051 • PIC16 microprocessors and microcontrollers - sadri 4 Von Neumann Architecture • Same Memory • Program • Data • Single Bus microprocessors and microcontrollers - sadri 5 Sample : ARM7T CPU microprocessors and microcontrollers - sadri 6 Harvard Architecture • Separate memories for program and data microprocessors and microcontrollers - sadri 7 TMS320C25 DSP microprocessors and microcontrollers - sadri 8 Silicon Market Revenue Rank Rank Country of 2009/2008 Company (million Market share 2009 2008 origin changes $ USD) Intel 11 USA 32 410 -4.0% 14.1% Corporation Samsung 22 South Korea 17 496 +3.5% 7.6% Electronics Toshiba 33Semiconduc Japan 10 319 -6.9% 4.5% tors Texas 44 USA 9 617 -12.6% 4.2% Instruments STMicroelec 55 FranceItaly 8 510 -17.6% 3.7% tronics 68Qualcomm USA 6 409 -1.1% 2.8% 79Hynix South Korea 6 246 +3.7% 2.7% 812AMD USA 5 207 -4.6% 2.3% Renesas 96 Japan 5 153 -26.6% 2.2% Technology 10 7 Sony Japan 4 468 -35.7% 1.9% microprocessors and microcontrollers -
AI Chips: What They Are and Why They Matter
APRIL 2020 AI Chips: What They Are and Why They Matter An AI Chips Reference AUTHORS Saif M. Khan Alexander Mann Table of Contents Introduction and Summary 3 The Laws of Chip Innovation 7 Transistor Shrinkage: Moore’s Law 7 Efficiency and Speed Improvements 8 Increasing Transistor Density Unlocks Improved Designs for Efficiency and Speed 9 Transistor Design is Reaching Fundamental Size Limits 10 The Slowing of Moore’s Law and the Decline of General-Purpose Chips 10 The Economies of Scale of General-Purpose Chips 10 Costs are Increasing Faster than the Semiconductor Market 11 The Semiconductor Industry’s Growth Rate is Unlikely to Increase 14 Chip Improvements as Moore’s Law Slows 15 Transistor Improvements Continue, but are Slowing 16 Improved Transistor Density Enables Specialization 18 The AI Chip Zoo 19 AI Chip Types 20 AI Chip Benchmarks 22 The Value of State-of-the-Art AI Chips 23 The Efficiency of State-of-the-Art AI Chips Translates into Cost-Effectiveness 23 Compute-Intensive AI Algorithms are Bottlenecked by Chip Costs and Speed 26 U.S. and Chinese AI Chips and Implications for National Competitiveness 27 Appendix A: Basics of Semiconductors and Chips 31 Appendix B: How AI Chips Work 33 Parallel Computing 33 Low-Precision Computing 34 Memory Optimization 35 Domain-Specific Languages 36 Appendix C: AI Chip Benchmarking Studies 37 Appendix D: Chip Economics Model 39 Chip Transistor Density, Design Costs, and Energy Costs 40 Foundry, Assembly, Test and Packaging Costs 41 Acknowledgments 44 Center for Security and Emerging Technology | 2 Introduction and Summary Artificial intelligence will play an important role in national and international security in the years to come. -
Instruction Set Innovations for Convey's HC-1 Computer
Instruction Set Innovations for Convey's HC-1 Computer THE WORLD’S FIRST HYBRID-CORE COMPUTER. Hot Chips Conference 2009 [email protected] Introduction to Convey Computer • Company Status – Second round venture based startup company – Product beta systems are at customer sites – Currently staffing at 36 people – Located in Richardson, Texas • Investors – Four Venture Capital Investors • Interwest Partners (Menlo Park) • CenterPoint Ventures (Dallas) • Rho Ventures (New York) • Braemar Energy Ventures (Boston) – Two Industry Investors • Intel Capital • Xilinx Presentation Outline • Overview of HC-1 Computer • Instruction Set Innovations • Application Examples Page 3 Hot Chips Conference 2009 What is a Hybrid-Core Computer ? A hybrid-core computer improves application performance by combining an x86 processor with hardware that implements application-specific instructions. ANSI Standard Applications C/C++/Fortran Convey Compilers x86 Coprocessor Instructions Instructions Intel® Processor Hybrid-Core Coprocessor Oil & Gas& Oil Financial Sciences Custom CAE Application-Specific Personalities Cache-coherent shared virtual memory Page 4 Hot Chips Conference 2009 What Is a Personality? • A personality is a reloadable set of instructions that augment x86 application the x86 instruction set Processor specific – Applicable to a class of applications instructions or specific to a particular code • Each personality is a set of files that includes: – The bits loaded into the Coprocessor – Information used by the Convey compiler • List of -
Shared-Memory Vector Systems Compared
Shared-Memory Vector Systems Compared Robert Bell, CSIRO and Guy Robinson, Arctic Region Supercomputing Center ABSTRACT: The NEC SX-5 and the Cray SV1 are the only shared-memory vector computers currently being marketed. This compares with at least five models a few years ago (J90, T90, SX-4, Fujitsu and Hitachi), with IBM, Digital, Convex, CDC and others having fallen by the wayside in the early 1990s. In this presentation, some comparisons will be made between the architecture of the survivors, and some performance comparisons will be given on benchmark and applications codes, and in areas not usually presented in comparisons, e.g. file systems, network performance, gzip speeds, compilation speeds, scalability and tools and libraries. KEYWORDS: SX-5, SV1, shared-memory, vector systems averaged 95% on the larger node, and 92% on the smaller 1. Introduction node. HPCCC The SXes are supported by data storage systems – The Bureau of Meteorology and CSIRO in Australia SAM-FS for the Bureau, and DMF on a Cray J90se for established the High Performance Computing and CSIRO. Communications Centre (HPCCC) in 1997, to provide ARSC larger computational facilities than either party could The mission of the Arctic Region Supercomputing acquire separately. The main initial system was an NEC Centre is to support high performance computational SX-4, which has now been replaced by two NEC SX-5s. research in science and engineering with an emphasis on The current system is a dual-node SX-5/24M with 224 high latitudes and the Arctic. ARSC provides high Gbyte of memory, and this will become an SX-5/32M performance computational, visualisation and data storage with 224 Gbyte in July 2001.