DOCSLIB.ORG

Accelerating a Climate Physics Model with Opencl

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Accelerating a Climate Physics Model with Opencl 1 Accelerating a climate physics model with OpenCL Fahad Zafar, Dibyajyoti Ghosh, Lawrence Sebald, Shujia Zhou {fahad3, dg9, lsebald1, szhou} @ umbc.edu University of Maryland Baltimore County Abstract—Open Computing Language (OpenCL) is fast cross-platform programming framework. Many authors becoming the standard for heterogeneous parallel com- have discussed architectural independence as benefits of puting. It is designed to run on CPUs, GPUs, and OpenCL [3], [1], [4]. Though there are many examples other accelerator architectures. By implementing a real- and code fragments available, cross-platform OpenCL world application, a solar radiation model component implementation examples are hard to find for real-world widely used in climate and weather models, we show that OpenCL multi-threaded programming and execution applications. In addition, to our knowledge there is no model can dramatically increase performance even on CPU report available on the portability of OpenCL in terms of architectures. Our preliminary investigation indicates that code and performance with CPU as the compute device low-level vector instructions and code representations in across platforms. OpenCL contribute to dramatic performance improvement The OpenCL execution model is based on kernel over the serial version when compared with the execution execution where the kernel is a function declared in a of the serial code compiled across various compilers on program and is executed on a compute device. In this multiple platforms with auto vectorization flags. However, paper, we use the term OpenCL compute device and the portability of OpenCL implementations needs to im- OpenCL device interchangeably. On submission of a ker- prove, even for CPU architectures. nel instance called a work-item the host device allocates Index Terms—Multi-threaded programming; Parallel a global ID for the work item. Each work item executes computing; Heterogeneous architectures; Climate Model; the same code but on different datasets and can take a IBM Cell B.E.; OpenCL; Vectorization; Compilers; GCC; different path of execution. Work items are organized ICC; IBM XLC into work-groups. Work-groups are given unique work- group IDs with the same dimensionality as that of the I. INTRODUCTION index space used to define the work-items. Following this The demand to increase forecast predictability has logical assignment of data and tasks, work-items execute been pushing climate and weather models to increase concurrently on the processing elements of a compute model grid resolution and include more physical pro- device. The host maintains a queuing data structure cesses. Accelerating climate simulations with the help called a command-queue to coordinate in-order or out- of emerging multicore computing paradigms provide us of-order execution of kernels on the device. with tools to address these demands. Current trends in In this paper we investigate how OpenCL will perform the computing industry have moved from optimizing in CPUs and its portability among CPUs through par- performance gains on single-core processors to increas- allelization of a real-world climate physics application, ing the overall performance through parallel computing the Goddard solar radiation model component [5] with with many-core processors. This trend has been all too the OpenCL implementations on IBM PowerPC and common in GPUs in the past. Now it is being widely POWER6 blades as well as on the Intel x86 architecture adopted by CPU architectures as well. with Mac OS X. The solar radiation model compo- OpenCL provides a standard heterogeneous parallel nent was originally written in Fortran. Over the years programming environment for applications to execute the structure of this Fortran code has been stable. It on CPUs, GPUs, and other accelerators such as DSPs represents a typical climate physics model component and mobile processors [1], [2]. To support such a wide whose calculations are carried out along the vertical array of processor architectures, OpenCL puts forward a (altitudinal) direction (so-called column physics). This thread-extensive model for programming. OpenCL code particular code was ported to the IBM Cell Broad- typically comprises of multiple kernels that are executed band Engine by Zhou et al [6] where detailed code over a multi-threaded grid. There is tremendous interest structure analysis and performance gains were reported. to develop an OpenCL application and run it on CPUs We implemented the serial version of the C code in as well as GPUs since OpenCL claims to be a standard OpenCL version 1.0, manually optimized all sections 2 of the code to run in a multi-threaded fashion using terms of code. The same OpenCL code runnable in OpenCL kernels and benchmarked the code on IBM IBM JS21 and JS22 blades did not compile or run JS21 and JS22 blades, on a POWER6 AIX system and seamlessly in Mac OS X as one would expect. Some on Mac OS X versions 10.6.4 and 10.6.7. We used code modifications were required to get correct results. the approach of extracting compute-intensive kernels Thread scheduling differences on the platforms caused without changing the overall code structure. This allowed the program to output correct answer in one case (IBM us to have multiple manual optimizations for specific blades) while the incorrect answer in the other case (Mac architectures while maintaining the code structure. We OS X). After successfully porting the first two sections found a 3X∼4X performance improvement per core over of the code with OpenCL, the speedups on Mac OS X the original serial code compiled with GCC. We believe with Intel CPU are similar to those on the IBM platform, these dramatic performance gains on the CPU show that which indicates the OpenCL performance is portable OpenCL provides interface to implement light-weight across platforms to some extent. multi-threading code on the CPU [7]. POSIX Threads The rest of the paper is organized as follows. In Sec- is known to be heavy-weight threads when used in tion II, we discuss the solar radiation code structure and multi-threaded programming, which increases program its OpenCL implementation. In Section III we present memory requirements and adds to context switching the results on IBM blades, POWER6 AIX and Mac costs. OpenCL provides access to a multi-threaded pro- OS X and we discuss potential reasons behind dramatic gramming and execution model as well as a low level performance gain on CPUs with OpenCL. In Section API for memory and thread management. Its relaxed IV we discuss related works and further analysis of memory consistency model is similar to that of CUDA performance gain and conclude in Section V. [8] developed for NVIDIA GPUs. We report our prelim- inary investigation results on OpenCL performance on II. CODE STRUCTURE AND OPENCL CPUs and on serial code performance across platforms IMPLEMENTATION when compiled with GCC, Intel C++ and IBM XLC A production-quality climate or weather model code compilers. We also touch upon vectorization results from can span up to a few hundred thousand lines. Most these three compilers with respect to two reference code of these codes are written in Fortran. We used single- sections in the serial code. precision, C version of a representative, compute- Our findings show that the GCC auto vectorization[9], intensive physics model component code with about [10] implementation fails in certain cases where the 1500 lines as a serial baseline. This baseline is used to OpenCL compiler succeeds in generating fast executable confirm the correctness of the results obtained with the code. This is particularly true for certain nested loop multi-threaded parallel version as well as to calculate the constructs, though the GCC vectorization unit performs performance speedup achieved against it. The selected as expected in many other cases in this model. Similar code is the solar radiation model component, SOLAR, results were obtained from Intel ICC compiler and IBM which has been widely used in climate and weather XLC compiler for these nested loop constructs. We models. This particular version of the code is from compiled the serial version of the solar radiation model the production version of the NASA GEOS-5 climate component code with the GCC for the SSE{2,3,4}[11] physics model. This model component along with the and the Altivec[12] target instruction sets respectively. IRRAD (the infrared radiation component) typically IBM XLC serial code compilation used Altivec[13] as takes 20 percent of the execution time[6]. The exact target architecture. OpenCL kernels for sections of code fraction of SOLAR execution time compared to the comprising of these loop constructs have been compared total model varies among the models. For example, in with the GCC, ICC and XLC vectorization report for NASA GEOS5 atmospheric model, the solar radiation these code sections. The details of these findings will can take 5%∼10%. However that fraction could increase be elaborated in the Performance Analysis subsection significantly if the aerosol effect is included. Reducing under Section III. The increased performance of our the execution time of SOLAR can benefit the climate OpenCL implementation can be attributed to implicit and weather simulations in two ways: allowing a reduced instruction and data parallelism by the OpenCL compiler overall simulation time and an increase in the complexity on Intel[14], [15] and IBM platforms as well as to of models within the same simulation time. The climate the efficient execution pipelining and better memory physics model component executes calculations only management. along the vertical (altitudinal) direction. In a climate or Based on our experience, current implementations weather physics model, most of the physical processes in of OpenCL from IBM and Apple are not portable in a whole or partial globe are modeled with such columns 3 at each point of a horizontal grid such as the latitudinal- in synchronization to break the linear program flow and longitudinal grid.
Load more

Recommended publications
  • Other Apis What’S Wrong with Openmp?
    Threaded Programming Other APIs What’s wrong with OpenMP? • OpenMP is designed for programs where you want a fixed number of threads, and you always want the threads to be consuming CPU cycles. – cannot arbitrarily start/stop threads – cannot put threads to sleep and wake them up later • OpenMP is good for programs where each thread is doing (more-or-less) the same thing. • Although OpenMP supports C++, it’s not especially OO friendly – though it is gradually getting better. • OpenMP doesn’t support other popular base languages – e.g. Java, Python What’s wrong with OpenMP? (cont.) Can do this Can do this Can’t do this Threaded programming APIs • Essential features – a way to create threads – a way to wait for a thread to finish its work – a mechanism to support thread private data – some basic synchronisation methods – at least a mutex lock, or atomic operations • Optional features – support for tasks – more synchronisation methods – e.g. condition variables, barriers,... – higher levels of abstraction – e.g. parallel loops, reductions What are the alternatives? • POSIX threads • C++ threads • Intel TBB • Cilk • OpenCL • Java (not an exhaustive list!) POSIX threads • POSIX threads (or Pthreads) is a standard library for shared memory programming without directives. – Part of the ANSI/IEEE 1003.1 standard (1996) • Interface is a C library – no standard Fortran interface – can be used with C++, but not OO friendly • Widely available – even for Windows – typically installed as part of OS – code is pretty portable • Lots of low-level control over behaviour of threads • Lacks a proper memory consistency model Thread forking #include <pthread.h> int pthread_create( pthread_t *thread, const pthread_attr_t *attr, void*(*start_routine, void*), void *arg) • Creates a new thread: – first argument returns a pointer to a thread descriptor.
    [Show full text]
  • Opencl on Shared Memory Multicore Cpus
    OpenCL on shared memory multicore CPUs Akhtar Ali, Usman Dastgeer and Christoph Kessler Book Chapter N.B.: When citing this work, cite the original article. Part of: Proceedings of the 5th Workshop on MULTIPROG -2012. E. Ayguade, B. Gaster, L. Howes, P. Stenström, O. Unsal (eds), 2012. Copyright: HiPEAC Network of Excellence Available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93951 Published in: Proc. Fifth Workshop on Programmability Issues for Multi-Core Computers (MULTIPROG-2012) at HiPEAC-2012 conference, Paris, France, Jan. 2012. OpenCL for programming shared memory multicore CPUs Akhtar Ali, Usman Dastgeer, and Christoph Kessler PELAB, Dept. of Computer and Information Science, Linköping University, Sweden [email protected] {usman.dastgeer,christoph.kessler}@liu.se Abstract. Shared memory multicore processor technology is pervasive in mainstream computing. This new architecture challenges programmers to write code that scales over these many cores to exploit the full compu- tational power of these machines. OpenMP and Intel Threading Build- ing Blocks (TBB) are two of the popular frameworks used to program these architectures. Recently, OpenCL has been defined as a standard by Khronos group which focuses on programming a possibly heteroge- neous set of processors with many cores such as CPU cores, GPUs, DSP processors. In this work, we evaluate the effectiveness of OpenCL for programming multicore CPUs in a comparative case study with OpenMP and Intel TBB for five benchmark applications: matrix multiply, LU decomposi- tion, 2D image convolution, Pi value approximation and image histogram generation. The evaluation includes the effect of compiler optimizations for different frameworks, OpenCL performance on different vendors’ plat- forms and the performance gap between CPU-specific and GPU-specific OpenCL algorithms for execution on a modern GPU.
    [Show full text]
  • Heterogeneous Task Scheduling for Accelerated Openmp
    Heterogeneous Task Scheduling for Accelerated OpenMP ? ? Thomas R. W. Scogland Barry Rountree† Wu-chun Feng Bronis R. de Supinski† ? Department of Computer Science, Virginia Tech, Blacksburg, VA 24060 USA † Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94551 USA [email protected] [email protected] [email protected] [email protected] Abstract—Heterogeneous systems with CPUs and computa- currently requires a programmer either to program in at tional accelerators such as GPUs, FPGAs or the upcoming least two different parallel programming models, or to use Intel MIC are becoming mainstream. In these systems, peak one of the two that support both GPUs and CPUs. Multiple performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the models however require code replication, and maintaining majority of programming models for heterogeneous computing two completely distinct implementations of a computational focus on only one of these. With the development of Accelerated kernel is a difficult and error-prone proposition. That leaves OpenMP for GPUs, both from PGI and Cray, we have a clear us with using either OpenCL or accelerated OpenMP to path to extend traditional OpenMP applications incrementally complete the task. to use GPUs. The extensions are geared toward switching from CPU parallelism to GPU parallelism. However they OpenCL’s greatest strength lies in its broad hardware do not preserve the former while adding the latter. Thus support. In a way, though, that is also its greatest weak- computational potential is wasted since either the CPU cores ness. To enable one to program this disparate hardware or the GPU cores are left idle.
    [Show full text]
  • AMD Accelerated Parallel Processing Opencl Programming Guide
    AMD Accelerated Parallel Processing OpenCL Programming Guide November 2013 rev2.7 © 2013 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, AMD Accelerated Parallel Processing, the AMD Accelerated Parallel Processing logo, ATI, the ATI logo, Radeon, FireStream, FirePro, Catalyst, and combinations thereof are trade- marks of Advanced Micro Devices, Inc. Microsoft, Visual Studio, Windows, and Windows Vista are registered trademarks of Microsoft Corporation in the U.S. and/or other jurisdic- tions. Other names are for informational purposes only and may be trademarks of their respective owners. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos. The contents of this document are provided in connection with Advanced Micro Devices, Inc. (“AMD”) products. AMD makes no representations or warranties with respect to the accuracy or completeness of the contents of this publication and reserves the right to make changes to specifications and product descriptions at any time without notice. The information contained herein may be of a preliminary or advance nature and is subject to change without notice. No license, whether express, implied, arising by estoppel or other- wise, to any intellectual property rights is granted by this publication. Except as set forth in AMD’s Standard Terms and Conditions of Sale, AMD assumes no liability whatsoever, and disclaims any express or implied warranty, relating to its products including, but not limited to, the implied warranty of merchantability, fitness for a particular purpose, or infringement of any intellectual property right. AMD’s products are not designed, intended, authorized or warranted for use as compo- nents in systems intended for surgical implant into the body, or in other applications intended to support or sustain life, or in any other application in which the failure of AMD’s product could create a situation where personal injury, death, or severe property or envi- ronmental damage may occur.
    [Show full text]
  • Implementing FPGA Design with the Opencl Standard
    Implementing FPGA Design with the OpenCL Standard WP-01173-3.0 White Paper Utilizing the Khronos Group’s OpenCL™ standard on an FPGA may offer significantly higher performance and at much lower power than is available today from hardware architectures such as CPUs, graphics processing units (GPUs), and digital signal processing (DSP) units. In addition, an FPGA-based heterogeneous system (CPU + FPGA) using the OpenCL standard has a significant time-to-market advantage compared to traditional FPGA development using lower level hardware description languages (HDLs) such as Verilog or VHDL. 1 OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos. Introduction The initial era of programmable technologies contained two different extremes of programmability. As illustrated in Figure 1, one extreme was represented by single core CPU and digital signal processing (DSP) units. These devices were programmable using software consisting of a list of instructions to be executed. These instructions were created in a manner that was conceptually sequential to the programmer, although an advanced processor could reorder instructions to extract instruction-level parallelism from these sequential programs at run time. In contrast, the other extreme of programmable technology was represented by the FPGA. These devices are programmed by creating configurable hardware circuits, which execute completely in parallel. A designer using an FPGA is essentially creating a massively- fine-grained parallel application. For many years, these extremes coexisted with each type of programmability being applied to different application domains. However, recent trends in technology scaling have favored technologies that are both programmable and parallel. Figure 1.
    [Show full text]
  • Opencl SYCL 2.2 Specification
    SYCLTM Provisional Specification SYCL integrates OpenCL devices with modern C++ using a single source design Version 2.2 Revision Date: – 2016/02/15 Khronos OpenCL Working Group — SYCL subgroup Editor: Maria Rovatsou Copyright 2011-2016 The Khronos Group Inc. All Rights Reserved Contents 1 Introduction 13 2 SYCL Architecture 15 2.1 Overview . 15 2.2 The SYCL Platform and Device Model . 15 2.2.1 Platform Mixed Version Support . 16 2.3 SYCL Execution Model . 17 2.3.1 Execution Model: Queues, Command Groups and Contexts . 18 2.3.2 Execution Model: Command Queues . 19 2.3.3 Execution Model: Mapping work-items onto an nd range . 21 2.3.4 Execution Model: Execution of kernel-instances . 22 2.3.5 Execution Model: Hierarchical Parallelism . 24 2.3.6 Execution Model: Device-side enqueue . 25 2.3.7 Execution Model: Synchronization . 25 2.4 Memory Model . 26 2.4.1 Access to memory . 27 2.4.2 Memory consistency . 28 2.4.3 Atomic operations . 28 2.5 The SYCL programming model . 28 2.5.1 Basic data parallel kernels . 30 2.5.2 Work-group data parallel kernels . 30 2.5.3 Hierarchical data parallel kernels . 30 2.5.4 Kernels that are not launched over parallel instances . 31 2.5.5 Synchronization . 31 2.5.6 Error handling . 32 2.5.7 Scheduling of kernels and data movement . 32 2.5.8 Managing object lifetimes . 34 2.5.9 Device discovery and selection . 35 2.5.10 Interfacing with OpenCL . 35 2.6 Anatomy of a SYCL application .
    [Show full text]
  • An Introduction to Gpus, CUDA and Opencl
    An Introduction to GPUs, CUDA and OpenCL Bryan Catanzaro, NVIDIA Research Overview ¡ Heterogeneous parallel computing ¡ The CUDA and OpenCL programming models ¡ Writing efficient CUDA code ¡ Thrust: making CUDA C++ productive 2/54 Heterogeneous Parallel Computing Latency-Optimized Throughput- CPU Optimized GPU Fast Serial Scalable Parallel Processing Processing 3/54 Why do we need heterogeneity? ¡ Why not just use latency optimized processors? § Once you decide to go parallel, why not go all the way § And reap more benefits ¡ For many applications, throughput optimized processors are more efficient: faster and use less power § Advantages can be fairly significant 4/54 Why Heterogeneity? ¡ Different goals produce different designs § Throughput optimized: assume work load is highly parallel § Latency optimized: assume work load is mostly sequential ¡ To minimize latency eXperienced by 1 thread: § lots of big on-chip caches § sophisticated control ¡ To maXimize throughput of all threads: § multithreading can hide latency … so skip the big caches § simpler control, cost amortized over ALUs via SIMD 5/54 Latency vs. Throughput Specificaons Westmere-EP Fermi (Tesla C2050) 6 cores, 2 issue, 14 SMs, 2 issue, 16 Processing Elements 4 way SIMD way SIMD @3.46 GHz @1.15 GHz 6 cores, 2 threads, 4 14 SMs, 48 SIMD Resident Strands/ way SIMD: vectors, 32 way Westmere-EP (32nm) Threads (max) SIMD: 48 strands 21504 threads SP GFLOP/s 166 1030 Memory Bandwidth 32 GB/s 144 GB/s Register File ~6 kB 1.75 MB Local Store/L1 Cache 192 kB 896 kB L2 Cache 1.5 MB 0.75 MB
    [Show full text]
  • Opencl in Scientific High Performance Computing
    OpenCL in Scientific High Performance Computing —The Good, the Bad, and the Ugly Matthias Noack [email protected] Zuse Institute Berlin Distributed Algorithms and Supercomputing 2017-05-18, IWOCL 2017 https://doi.org/10.1145/3078155.3078170 1 / 28 Systems: • 2× Cray XC40 (#118 and #150 in top500, year 4/5 in lifetime) with Xeon CPUs • 80-node Xeon Phi (KNL) Cray XC40 test-and-development system • 2× 32-node Infiniband cluster with Nvidia K40 • 2 test-and-development systems with AMD FirePro W8100 What I do: • computer science research (methods) • development of HPC codes • evaluation of upcoming technologies • consulting/training with system users Context ZIB hosts part of the HLRN (Northern German Supercomputing Alliance) facilities. 2 / 28 What I do: • computer science research (methods) • development of HPC codes • evaluation of upcoming technologies • consulting/training with system users Context ZIB hosts part of the HLRN (Northern German Supercomputing Alliance) facilities. Systems: • 2× Cray XC40 (#118 and #150 in top500, year 4/5 in lifetime) with Xeon CPUs • 80-node Xeon Phi (KNL) Cray XC40 test-and-development system • 2× 32-node Infiniband cluster with Nvidia K40 • 2 test-and-development systems with AMD FirePro W8100 2 / 28 Context ZIB hosts part of the HLRN (Northern German Supercomputing Alliance) facilities. Systems: • 2× Cray XC40 (#118 and #150 in top500, year 4/5 in lifetime) with Xeon CPUs • 80-node Xeon Phi (KNL) Cray XC40 test-and-development system • 2× 32-node Infiniband cluster with Nvidia K40 • 2 test-and-development systems with AMD FirePro W8100 What I do: • computer science research (methods) • development of HPC codes • evaluation of upcoming technologies • consulting/training with system users 2 / 28 • intra-node parallelism dominated by OpenMP (e.g.
    [Show full text]
  • The Continuing Renaissance in Parallel Programming Languages
    The continuing renaissance in parallel programming languages Simon McIntosh-Smith University of Bristol Microelectronics Research Group [email protected] © Simon McIntosh-Smith 1 Didn’t parallel computing use to be a niche? © Simon McIntosh-Smith 2 When I were a lad… © Simon McIntosh-Smith 3 But now parallelism is mainstream Samsung Exynos 5 Octa: • 4 fast ARM cores and 4 energy efficient ARM cores • Includes OpenCL programmable GPU from Imagination 4 HPC scaling to millions of cores Tianhe-2 at NUDT in China 33.86 PetaFLOPS (33.86x1015), 16,000 nodes Each node has 2 CPUs and 3 Xeon Phis 3.12 million cores, $390M, 17.6 MW, 720m2 © Simon McIntosh-Smith 5 A renaissance in parallel programming Metal C++11 OpenMP OpenCL Erlang Unified Parallel C Fortress XC Go Cilk HMPP CHARM++ CUDA Co-Array Fortran Chapel Linda X10 MPI Pthreads C++ AMP © Simon McIntosh-Smith 6 Groupings of || languages Partitioned Global Address Space GPU languages: (PGAS): • OpenCL • Fortress • CUDA • X10 • HMPP • Chapel • Metal • Co-array Fortran • Unified Parallel C Object oriented: • C++ AMP CSP: XC • CHARM++ Message passing: MPI Multi-threaded: • Cilk Shared memory: OpenMP • Go • C++11 © Simon McIntosh-Smith 7 Emerging GPGPU standards • OpenCL, DirectCompute, C++ AMP, … • Also OpenMP 4.0, OpenACC, CUDA… © Simon McIntosh-Smith 8 Apple's Metal • A "ground up" parallel programming language for GPUs • Designed for compute and graphics • Potential to replace OpenGL compute shaders, OpenCL/GL interop etc. • Close to the "metal" • Low overheads • "Shading" language based
    [Show full text]
  • SYCL – Introduction and Hands-On
    SYCL – Introduction and Hands-on Thomas Applencourt + people on next slide - [email protected] Argonne Leadership Computing Facility Argonne National Laboratory 9700 S. Cass Ave Argonne, IL 60349 Book Keeping Get the example and the presentation: 1 git clone https://github.com/alcf-perfengr/sycltrain 2 cd sycltrain/presentation/2020_07_30_ATPESC People who are here to help: • Collen Bertoni - [email protected] • Brian Homerding - [email protected] • Nevin ”:)” Liber [email protected] • Ben Odom - [email protected] • Dan Petre - [email protected]) 1/17 Table of contents 1. Introduction 2. Theory 3. Hands-on 4. Conclusion 2/17 Introduction What programming model to use to target GPU? • OpenMP (pragma based) • Cuda (proprietary) • Hip (low level) • OpenCL (low level) • Kokkos, RAJA, OCCA (high level, abstraction layer, academic projects) 3/17 What is SYCL™?1 1. Target C++ programmers (template, lambda) 1.1 No language extension 1.2 No pragmas 1.3 No attribute 2. Borrow lot of concept from battle tested OpenCL (platform, device, work-group, range) 3. Single Source (two compilations passes) 4. High level data-transfer 5. SYCL is a Specification developed by the Khronos Group (OpenCL, SPIR, Vulkan, OpenGL) 1SYCL Doesn’t mean ”Someone You Couldn’t Love”. Sadly. 4/17 SYCL Implementations 2 2Credit: Khronos groups (https://www.khronos.org/sycl/) 5/17 Goal of this presentation 1. Give you a feel of SYCL 2. Go through code examples (and make you do some homework) 3. Teach you enough so that can search for the rest if you interested 4. Question are welcomed! 3 3Please just talk, or use slack 6/17 Theory A picture is worth a thousand words4 4and this is a UML diagram so maybe more! 7/17 Memory management: SYCL innovation 1.
    [Show full text]
  • Opencl – the Open Standard for Parallel Programming of Heterogeneous Systems J
    OpenCL – The Open Standard for Parallel Programming of Heterogeneous Systems J. Y. Xu Institute of Information & Mathematical Sciences Massey University at Albany, Auckland, New Zealand [email protected] OpenCL, or Open Computing Language, is an open source API that allows for the development of portable code that take advantage of the parallel computing power of modern electronic hardwares, and has the support of a number of industrial heavy weights, making it an idea starting point for any parallel application from consumer software to HPC programs. Keywords: OpenCL, parallel computing, GPGPU 1. Introduction Today's computers are becoming increasing reliant on parallel computing. Processors are shipping with multiple cores, and new ways of computing are being discovered such as GPGPU (General Purpose Graphics Processing Unit) programming [1][3]. Each of these new processing techniques present advantages of their own, thus making the combined use of these technologies empirical. For example GPGPU allows for massive data parallel computations with improved floating point precision [2], while CPU allows for task parallel computation. Combining these two results in applications that are capable of massively parallel vector computations, while remain interactive to end users. While presenting unique opportunities, these emerging trends also present a number of challenges. First of all, GPGPU programming evolved from graphical APIs, and as a result the language constructs are different to those of multi-core CPU programming. This means that a program written for a dual core CPU cannot be made to run on GPGPUs without extensive modification. Worse still, different vendors have their own proprietary technologies. OpenCL, or Open Computing Language, is a prominent project aimed to bring uniformity to this new area.
    [Show full text]
  • Opencl: a Hands-On Introduction
    OpenCL: A Hands-on Introduction Tim Mattson Alice Koniges Intel Corp. Berkeley Lab/NERSC Simon McIntosh-Smith University of Bristol Acknowledgements: In addition to Tim, Alice and Simon … Tom Deakin (Bristol) and Ben Gaster (Qualcomm) contributed to this content. Agenda Lectures Exercises An Introduction to OpenCL Logging in and running the Vadd program Understanding Host programs Chaining Vadd kernels together Kernel programs The D = A + B + C problem Writing Kernel Programs Matrix Multiplication Lunch Working with the OpenCL memory model Several ways to Optimize matrix multiplication High Performance OpenCL Matrix multiplication optimization contest The OpenCL Zoo Run your OpenCL programs on a variety of systems. Closing Comments Course materials In addition to these slides, C++ API header files, a set of exercises, and solutions, we provide: OpenCL C 1.2 Reference Card OpenCL C++ 1.2 Reference Card These cards will help you keep track of the API as you do the exercises: https://www.khronos.org/files/ opencl-1-2-quick-reference-card.pdf The v1.2 spec is also very readable and recommended to have on-hand: https://www.khronos.org/registry/ cl/specs/opencl-1.2.pdf AN INTRODUCTION TO OPENCL Industry Standards for Programming Heterogeneous Platforms GPUs CPUs Emerging Increasingly general Multiple cores driving Intersection purpose data-parallel performance increases computing Graphics Multi- Heterogeneous APIs and processor Computing Shading programming – Languages e.g. OpenMP OpenCL – Open Computing Language Open, royalty-free standard for portable, parallel programming of heterogeneous parallel computing CPUs, GPUs, and other processors The origins of OpenCL ARM AMD Merged, needed Nokia commonality IBM ATI across products Sony Wrote a rough draft Qualcomm GPU vendor – straw man API Imagination wants to steal TI NVIDIA market share from CPU + many more Khronos Compute CPU vendor – group formed wants to steal Intel market share from GPU Was tired of recoding for many core, GPUs.
    [Show full text]