HSA Runtime Programmer's Reference Manual Version
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The Importance of Data
The landscape of Parallel Programing Models Part 2: The importance of Data Michael Wong and Rod Burns Codeplay Software Ltd. Distiguished Engineer, Vice President of Ecosystem IXPUG 2020 2 © 2020 Codeplay Software Ltd. Distinguished Engineer Michael Wong ● Chair of SYCL Heterogeneous Programming Language ● C++ Directions Group ● ISOCPP.org Director, VP http://isocpp.org/wiki/faq/wg21#michael-wong ● [email protected] ● [email protected] Ported ● Head of Delegation for C++ Standard for Canada Build LLVM- TensorFlow to based ● Chair of Programming Languages for Standards open compilers for Council of Canada standards accelerators Chair of WG21 SG19 Machine Learning using SYCL Chair of WG21 SG14 Games Dev/Low Latency/Financial Trading/Embedded Implement Releasing open- ● Editor: C++ SG5 Transactional Memory Technical source, open- OpenCL and Specification standards based AI SYCL for acceleration tools: ● Editor: C++ SG1 Concurrency Technical Specification SYCL-BLAS, SYCL-ML, accelerator ● MISRA C++ and AUTOSAR VisionCpp processors ● Chair of Standards Council Canada TC22/SC32 Electrical and electronic components (SOTIF) ● Chair of UL4600 Object Tracking ● http://wongmichael.com/about We build GPU compilers for semiconductor companies ● C++11 book in Chinese: Now working to make AI/ML heterogeneous acceleration safe for https://www.amazon.cn/dp/B00ETOV2OQ autonomous vehicle 3 © 2020 Codeplay Software Ltd. Acknowledgement and Disclaimer Numerous people internal and external to the original C++/Khronos group, in industry and academia, have made contributions, influenced ideas, written part of this presentations, and offered feedbacks to form part of this talk. But I claim all credit for errors, and stupid mistakes. These are mine, all mine! You can’t have them. -
Delivering Heterogeneous Programming in C++
Delivering Heterogeneous Programming in C++ Duncan McBain, Codeplay Software Ltd. About me ● Graduated from Edinburgh University 3 years ago ● Postgrad course got me interested in GPU programming ● Worked at Codeplay since graduating ● Research projects, benchmarking, debuggers ● Most recently on C++ library for heterogeneous systems 2 © 2016 Codeplay Software Ltd. Contents • What are heterogeneous systems? • How can we program them? • The future of heterogeneous systems 3 © 2016 Codeplay Software Ltd. What are heterogeneous systems • By this, I mean devices like GPUs, DSPs, FPGAs… • Generally a bit of hardware that is more specialised than, and fundamentally different to, the host CPU • Specialisation can make it very fast • Can also be harder to program because of specialisation 4 © 2016 Codeplay Software Ltd. Some definitions • Host – The CPU/code that runs on the CPU, controls main memory (RAM), might control many devices • Device – A GPU, DSP, or something more exotic • Heterogeneous system – A host, a device and an API tying them together 5 © 2016 Codeplay Software Ltd. Some definitions • Kernel – Code representing the computation to be performed on the device. • Work group – A collection of many work items executing on a device. Has shared local memory and executes same instructions 6 © 2016 Codeplay Software Ltd. Some definitions ● Work item – A single thread or task on a device that executes in parallel ● Parallel for – Some collection of work items, in many work groups, executing a kernel in parallel. In general, cannot return anything, and must be enqueued asynchronously 7 © 2016 Codeplay Software Ltd. Example heterogeneous device ● CPUs today can execute instructions out-of-order, speculative execution, branch prediction ● Complexity hidden from programmer ● Contrast with e.g. -
Programmers' Tool Chain
Reduce the complexity of programming multicore ++ Offload™ for PlayStation®3 | Offload™ for Cell Broadband Engine™ | Offload™ for Embedded | Custom C and C++ Compilers | Custom Shader Language Compiler www.codeplay.com It’s a risk to underestimate the complexity of programming multicore applications Software developers are now presented with a rapidly-growing range of different multi-core processors. The common feature of many of these processors is that they are difficult and error-prone to program with existing tools, give very unpredictable performance, and that incompatible, complex programming models are used. Codeplay develop compilers and programming tools with one primary goal - to make it easy for programmers to achieve big performance boosts with multi-core processors, but without needing bigger, specially-trained, expensive development teams to get there. Introducing Codeplay Based in Edinburgh, Scotland, Codeplay Software Limited was founded by veteran games developer Andrew Richards in 2002 with funding from Jez San (the founder of Argonaut Games and ARC International). Codeplay introduced their first product, VectorC, a highly optimizing compiler for x86 PC and PlayStation®2, in 2003. In 2004 Codeplay further developed their business by offering services to processor developers to provide them with compilers and programming tools for their new and unique architectures, using VectorC’s highly retargetable compiler technology. Realising the need for new multicore tools Codeplay started the development of the company’s latest product, the Offload™ C++ Multicore Programming Platform. In October 2009 Offload™: Community Edition was released as a free-to-use tool for PlayStation®3 programmers. Experience and Expertise Codeplay have developed compilers and software optimization technology since 1999. -
Methods and Tools for Rapid and Efficient Parallel Implementation Of
Methods and tools for rapid and efficient parallel implementation of computer vision algorithms on embedded multiprocessors Vitor Schwambach To cite this version: Vitor Schwambach. Methods and tools for rapid and efficient parallel implementation of computer vision algorithms on embedded multiprocessors. Computer Vision and Pattern Recognition [cs.CV]. Université Grenoble Alpes, 2016. English. NNT : 2016GREAM022. tel-01523273 HAL Id: tel-01523273 https://tel.archives-ouvertes.fr/tel-01523273 Submitted on 16 May 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITÉ DE GRENOBLE ALPES Spécialité : Informatique Arrêté ministériel du 7 août 2006 Présentée par Vítor Schwambach Thèse dirigée par Stéphane Mancini et codirigée par Sébastien Cleyet-Merle et Alain Issard préparée au sein du Laboratoire TIMA et STMicroelectronics et de l’École Doctorale Mathématiques, Sciences et Technologies de l’Information, Informatique (EDMSTII) Methods and Tools for Rapid and Efficient Parallel Implementation of Computer -
Simplifying Many-Core-Based Heterogeneous Soc Programming
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 11, NO. 4, AUGUST 2015 957 Simplifying Many-Core-Based Heterogeneous SoC Programming With Offload Directives Andrea Marongiu, Member, IEEE, Alessandro Capotondi, Student Member, IEEE, Giuseppe Tagliavini, and Luca Benini, Fellow, IEEE Abstract—Multiprocessor systems-on-chip (MPSoC) are evolv- performance/watt. Unfortunately, these advantages are traded ing into heterogeneous architectures based on one host processor off for an increased programming complexity: extensive and plus many-core accelerators. While heterogeneous SoCs promise time-consuming rewrite of applications is required, using spe- higher performance/watt, they are programmed at the cost of major code rewrites with low-level programming abstractions (e.g, cialized programming paradigms. For example, in the general- OpenCL). We present a programming model based on OpenMP, purpose and high-performance computing (HPC) domains, with additional directives to program the accelerator from a sin- Graphics Processing Unit (GPU)-based systems are pro- gle host program. As a test case, we evaluate an implementation grammed with OpenCL.1 OpenCL aims at providing a stan- of this programming model for the STMicroelectronics STHORM dardized way of programming such accelerators; however, it development board. We obtain near-ideal throughput for most benchmarks, very close performance to hand-optimized OpenCL offers a very low-level programming style. Programmers must codes at a significantly lower programming complexity, and up to write and compile separate programs for the host system and for 30× speedup versus host execution time. the accelerator. Data transfers to/from the many-core and syn- Index Terms—Heterogeneous systems-on-chip (SoC), many chronization must also be manually orchestrated. -
An Introduction to GPU Computing
An Introduction to GPU Computing iVEC Supercomputer Training 5th - 9th November 2012 Introducing the Historical GPU Graphics Processing Unit (GPU) n : A specialised electronic circuit designed to rapidly manipulate and alter memory in such a way as to accelerate the building of images in a frame buffer intended for output to a display. Introducing the Modern GPU Graphics Processing Unit (GPU) n : A general purpose electronic circuit designed to rapidly manipulate and alter memory in such a way as to accelerate computational algorithms that have fine-grained parallelism. GPU Computing Motivation But what about... Central Processing Unit (CPU) n : the portion of a computer system that carries out the instructions of a computer program, to perform the basic arithmetical, logical, and input/output operations of the system GPU Computing Motivation : Arithmetic Performance GPU Computing Motivation How is that possible? - GPUs have less cache and logic, more arithmetic - GPUs execute lots of simple threads - GPUs are physically bigger Nehalem Die (263mm2) Fermi Die (530mm2) GPU Computing Motivation : Power Efficiency GPU Computing Motivation : Memory Bandwidth GPU Computing Motivation : Summary Pros : - high arithmetic performance - high memory bandwidth - power efficient computation Cons : - separated by PCI Express bus - requires fine grain parallelism - requires high arithmetic intensity - requires light weight threads - requires additional software development For the right algorithm, additional time spent on code development can yield significant -
Demand-Based Scheduling Using Noc Probes
Demand-based Scheduling using NoC Probes Kurt Shuler Jonah Probell Monica Tang Arteris, Inc. Sunnyvale, CA, USA www.arteris.com ABSTRACT Contention for shared resources, such as memory in a system-on-chip, is inefficient. It limits performance and requires initiator IPs, such as CPUs, to stay powered up and run at clock speeds higher than they would otherwise. When a system runs multiple tasks simultaneously, contention will vary depending on each task’s demand for shared resources. Scheduling in a conventional system does not consider how much each task will demand shared resources. Proposed is a demand-based method of scheduling tasks that have diverse demand for shared resources in a multi- processor system-on-chip. Tasks with extremes of high and low demand are scheduled to run simultaneously. Where tasks in a system are cyclical, knowledge of period, duty cycle, phase, and magnitude are considered in the scheduling algorithm. Using demand-based scheduling, the time variation of aggregate demand is reduced, as might be its maximum. The average access latency and resulting idle clock cycles are reduced. This allows initiator IPs to run at lower clock frequencies, finish their tasks sooner, and spend more time in powered down modes. Use of probes within a network-on-chip to gather demand statistics, application-specific algorithm considerations, operating system thread scheduler integration, and heterogeneous system middleware integration are discussed. Contents 1. Introduction ...................................................................................................................................................................... -
Open Standards Enable Continuous Software Development in the Automotive Industry
AUTOSENS PROCEEDINGS 1 Open standards enable continuous software development in the automotive industry Markus Glaser, Charles Macfarlane, Benjamin May, Sven Fleck, Lukas Sommer, Jann-Eve Stavesand, Christian Weber, Duong-Van Nguyen, Enda Ward, Illya Rudkin, Stefan Milz, Rainer Oder, Frank Bohm,¨ Benedikt Schonlau, Oliver Hupfeld, Andreas Koch Abstract—Until recently, the automotive industry was primarily focused on design, development of electronics and mechanics, and manufacturing. Nowadays the software component of new vehicles has become a large portion of the development cost, driven by adding numerous new sensors, intelligent algorithms, very powerful and specialized processors and a highly complex user experience to the vehicle. In addition, requirements for high-performance and real-time processing as well as the vehicle’s distributed architecture bring challenges, but moreover supply chains further complicate development. In this article a high-level overview of vehicle development is provided, followed by a deep dive in the different software development processes, languages and tools that are required for efficient development of the next generation of intelligent vehicles. This paper especially explores SYCLTM, an open standard from The KhronosTM Group for high-performance programming of heterogeneous multicore processor system. Index Terms—Automotive, continuous development, open standards, SYCL, CUDA, AI driven, software driven, ADAS, heterogeneous computing, AI, Machine Learning F 1 INTRODUCTION ing open standards (e.g. SYCL). To this end an overview of the automotive landscape is given, where functional safety Modern cars utilize 100+ ECUs and reached 100M lines standards are now the norm and where open standards of code by 2015 [1], [2]. Code complexity is expected to for software are becoming the solution for the automotive increase exponentially, resulting in over 650M lines of code industry, achieving the demands of ADAS developers and by 2025 [3] for every vehicle and over 1bn lines to achieve a overcoming software development challenges. -
SYCL for CUDA
DPC++ on Nvidia GPUs DPC++ on Nvidia GPUs Ruyman Reyes Castro Stuart Adams, CTO Staff Software Engineer IXPUG/TACC Products Markets High Performance Compute (HPC) Integrates all the industry C++ platform via the SYCL™ Automotive ADAS, IoT, Cloud Compute standard technologies needed open standard, enabling vision Smartphones & Tablets to support a very wide range & machine learning e.g. of AI and HPC TensorFlow™ Medical & Industrial Technologies: Artificial Intelligence Vision Processing The heart of Codeplay's Machine Learning compute technology enabling Big Data Compute OpenCL™, SPIR-V™, HSA™ and Vulkan™ Enabling AI & HPC to be Open, Safe & Company Customers Accessible to All Leaders in enabling high-performance software solutions for new AI processing systems Enabling the toughest processors with tools and middleware based on open standards Established 2002 in Scotland with ~80 employees And many more! 3 © 2020 Codeplay Software Ltd. Summary • What is DPC++ and SYCL • Using SYCL for CUDA • Design of SYCL for CUDA • Implementation of SYCL for CUDA • Interoperability with existing libraries • Using oneMKL on CUDA • Conclusions and future work 4 © 2020 Codeplay Software Ltd. What is DPC++? Intel’s DPC++ • Data Parallel C++ (DPC++) is an open, standards-based alternative to single-architecture proprietary languages, part of oneAPI spec. SYCL 2020 • It is based on C++ and SYCL, allowing developers to reuse code across hardware targets (CPUs and accelerators such as GPUs and FPGAs) and also perform custom tuning for SYCL 1.2.1 a specific accelerator. 5 © 2020 Codeplay Software Ltd. Codeplay and SYCL • Codeplay has been part of the SYCL community from the beginning • Our team has helped to shape the SYCL open standard • We implemented the first conformant SYCL product 6 © 2020 Codeplay Software Ltd. -
Comparing SYCL with HPX, Kokkos, Raja and C++ Executors the Future of ISO C++ Heterogeneous Computing
Comparing SYCL with HPX, Kokkos, Raja and C++ Executors The future of ISO C++ Heterogeneous Computing Michael Wong (Codeplay Software, VP of Research and Development), Andrew Richards, CEO ISOCPP.org Director, VP http://isocpp.org/wiki/faq/wg21#michael-wong Head of Delegation for C++ Standard for Canada Vice Chair of Programming Languages for Standards Council of Canada Chair of WG21 SG5 Transactional Memory Chair of WG21 SG14 Games Dev/Low Latency/Financial Trading/Embedded Editor: C++ SG5 Transactional Memory Technical Specification Editor: C++ SG1 Concurrency Technical Specification http:://wongmichael.com/about SC2016 Agenda • Heterogensous Computing for ISO C++ • SYCL • HPX (slides thanks to Hartmut Kaiser) • Kokkos (slides thanks to Carter Edwards, Christian Trott) • Raja (Slides thanks to David Beckingsale) • State of C++ Concurrency and Parallelism • C++ Executors coming in C++20 • C++ simd/vector coming in C++20 2 © 2016 Codeplay Software Ltd. The goal for C++ •Great support for cpu latency computations through concurrency TS- •Great support for cpu throughput through parallelism TS •Great support for Heterogeneous throughput computation in future 3 © 2016 Codeplay Software Ltd. Many alternatives for Massive dispatch/heterogeneous • Programming Languages Usage experience • OpenGL • DirectX • OpenMP/OpenACC • CUDA • HPC • OpenCL • SYCL • OpenMP • OpenCL • OpenACC • CUDA • C++ AMP • HPX • HSA • SYCL • Vulkan 4 © 2016 Codeplay Software Ltd. Not that far away from a Grand Unified Theory •GUT is achievable •What we have is only missing 20% of where we want to be •It is just not designed with an integrated view in mind ... Yet •Need more focus direction on each proposal for GUT, whatever that is, and add a few elements 5 © 2016 Codeplay Software Ltd. -
International Workshop on Opencl and SYCL Call for Submissions
TH International Workshop on OpenCL and SYCL 9 27-29 April 2021 | LRZ Munich, Germany Workshop Chairs Call for Submissions Chair: Simon McIntosh-Smith, Professor of High-Performance Computing and IWOCL & SYCLcon 2021 will be the 9th annual gathering of the international community Head of the HPC Research Group, of developers, researchers, suppliers and Khronos Working Group members to share University of Bristol. Local Co-Chairs: best practice, and to advance the use and evolution of the Open Computing Language Martin Schreiber, Lecturer and Researcher (OpenCL), and the SYCL standard for C++ programming of heterogeneous platforms and at the Chair of Computer Architecture and their associated ecosystems. Parallel Systems, Technical University This pioneering workshop has been attracting an International audience of leading of Munich and Christoph Riesinger, academic and industrial experts since 2013 and is the premier forum for the community Application Engineer, Intel. SYCLcon to present, discuss and learn about applying OpenCL and SYCL to address the issues Chair: Michael Wong, VP Research faced in High Performance Computing across a wide range of application domains. & Development, Codeplay Software. Proceedings Chair: Andrei Poenaru, Researcher, University of Bristol. Topics of Interest Organising Chair: Tim Lewis, IWOCL. You are cordially invited to contribute and participate in this workshop. Topics of interest include but are not limited to: Program Committee Ben Ashbaugh, Intel. Patrick Atkinson, • Scientific and high-performance computing (HPC) applications NVIDIA. David Beckingsale, Lawrence • Machine Learning Training and Inferencing Livermore National Laboratory. Thomasz • Development tools, including debuggers, profilers and reference implementations Bednarz, CSIRO Data61. Ben Bergen , Los • HPC frameworks and supporting libraries developed on top of OpenCL, SYCL, Alamos National Laborator.Alex Bourd, SPIR-V, Vulkan and other parallel C++ paradigms Qualcomm. -
A Multifrequency MAC Specially Designed for Wireless Sensor
HSAemu – A Full System Emulator for HSA Platforms JIUN-HUNG DING, ZHOUDONG GUO, CHUNG-MING KAO, National Tsing Hua University WEI-CHUNG HSU, National Chiao Tung University YEH-CHING CHUNG, National Tsing Hua University Heterogeneous system architecture is an open industry standard designed to support a large variety of data-parallel and task-parallel programming models. Many application processor vendors, including AMD, ARM, Imagination, MediaTek, Texas Instrument, Samsung and Qualcomm are members of the HSA Foundation. This paper presents the design of HSAemu, a full system emulator for the HSA platform. The design of HSAemu is based on PQEMU, a parallel version of QEMU, with supports for HSA defined features such as a) shared virtual memory between CPU and GPU, b) memory based signaling and synchronization, c) multiple user level command queues, d) preemptive GPU context switching, and e) concurrent execution of CPU threads and GPU threads, etc. In addition to the basic requirements of the HSA-compliant features, HSAemu also includes an LLVM based binary translation engine to efficiently support translating multiple different ISAs (e.g. ARM and HSAIL -- HSA Intermediate Language). Categories and Subject Descriptors: C.1.3 [Processor Architectures]: Other Architecture Styles— Heterogeneous (hybrid) Systems; C.1.6 [Simulation and Modeling]: Type of Simulation—Parallel General Terms: Design, Simulation Additional Key Words and Phrases: HSA, GPU simulation, parallel simulation 1. INTRODUCTION Over the last decade, there has been a growing interest in the use of graphics processing units (GPUs), originally designed to perform specialized graphics computations in parallel, to perform general purpose parallel computation tasks traditionally handled by the central processing units (CPUs).