A Study of Replacing CUDA by Opencl in KGPU
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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. -
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. -
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. -
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. -
The Intro to GPGPU CPU Vs
12/12/11! The Intro to GPGPU . Dr. Chokchai (Box) Leangsuksun, PhD! Louisiana Tech University. Ruston, LA! ! CPU vs. GPU • CPU – Fast caches – Branching adaptability – High performance • GPU – Multiple ALUs – Fast onboard memory – High throughput on parallel tasks • Executes program on each fragment/vertex • CPUs are great for task parallelism • GPUs are great for data parallelism Supercomputing 20082 Education Program 1! 12/12/11! CPU vs. GPU - Hardware • More transistors devoted to data processing CUDA programming guide 3.1 3 CPU vs. GPU – Computation Power CUDA programming guide 3.1! 2! 12/12/11! CPU vs. GPU – Memory Bandwidth CUDA programming guide 3.1! What is GPGPU ? • General Purpose computation using GPU in applications other than 3D graphics – GPU accelerates critical path of application • Data parallel algorithms leverage GPU attributes – Large data arrays, streaming throughput – Fine-grain SIMD parallelism – Low-latency floating point (FP) computation © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007! ECE 498AL, University of Illinois, Urbana-Champaign! 3! 12/12/11! Why is GPGPU? • Large number of cores – – 100-1000 cores in a single card • Low cost – less than $100-$1500 • Green computing – Low power consumption – 135 watts/card – 135 w vs 30000 w (300 watts * 100) • 1 card can perform > 100 desktops 12/14/09!– $750 vs 50000 ($500 * 100) 7 Two major players 4! 12/12/11! Parallel Computing on a GPU • NVIDIA GPU Computing Architecture – Via a HW device interface – In laptops, desktops, workstations, servers • Tesla T10 1070 from 1-4 TFLOPS • AMD/ATI 5970 x2 3200 cores • NVIDIA Tegra is an all-in-one (system-on-a-chip) ATI 4850! processor architecture derived from the ARM family • GPU parallelism is better than Moore’s law, more doubling every year • GPGPU is a GPU that allows user to process both graphics and non-graphics applications. -
System-On-A-Chip (Soc) & ARM Architecture
System-on-a-Chip (SoC) & ARM Architecture EE2222 Computer Interfacing and Microprocessors Partially based on System-on-Chip Design by Hao Zheng 2020 EE2222 1 Overview • A system-on-a-chip (SoC): • a computing system on a single silicon substrate that integrates both hardware and software. • Hardware packages all necessary electronics for a particular application. • which implemented by SW running on HW. • Aim for low power and low cost. • Also more reliable than multi-component systems. 2020 EE2222 2 Driven by semiconductor advances 2020 EE2222 3 Basic SoC Model 2020 EE2222 4 2020 EE2222 5 SoC vs Processors System on a chip Processors on a chip processor multiple, simple, heterogeneous few, complex, homogeneous cache one level, small 2-3 levels, extensive memory embedded, on chip very large, off chip functionality special purpose general purpose interconnect wide, high bandwidth often through cache power, cost both low both high operation largely stand-alone need other chips 2020 EE2222 6 Embedded Systems • 98% processors sold annually are used in embedded applications. 2020 EE2222 7 Embedded Systems: Design Challenges • Power/energy efficient: • mobile & battery powered • Highly reliable: • Extreme environment (e.g. temperature) • Real-time operations: • predictable performance • Highly complex • A modern automobile with 55 electronic control units • Tightly coupled Software & Hardware • Rapid development at low price 2020 EE2222 8 EECS222A: SoC Description and Modeling Lecture 1 Design Complexity Challenge Design• Productivity Complexity -
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. -
Comparative Study of Various Systems on Chips Embedded in Mobile Devices
Innovative Systems Design and Engineering www.iiste.org ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online) Vol.4, No.7, 2013 - National Conference on Emerging Trends in Electrical, Instrumentation & Communication Engineering Comparative Study of Various Systems on Chips Embedded in Mobile Devices Deepti Bansal(Assistant Professor) BVCOE, New Delhi Tel N: +919711341624 Email: [email protected] ABSTRACT Systems-on-chips (SoCs) are the latest incarnation of very large scale integration (VLSI) technology. A single integrated circuit can contain over 100 million transistors. Harnessing all this computing power requires designers to move beyond logic design into computer architecture, meet real-time deadlines, ensure low-power operation, and so on. These opportunities and challenges make SoC design an important field of research. So in the paper we will try to focus on the various aspects of SOC and the applications offered by it. Also the different parameters to be checked for functional verification like integration and complexity are described in brief. We will focus mainly on the applications of system on chip in mobile devices and then we will compare various mobile vendors in terms of different parameters like cost, memory, features, weight, and battery life, audio and video applications. A brief discussion on the upcoming technologies in SoC used in smart phones as announced by Intel, Microsoft, Texas etc. is also taken up. Keywords: System on Chip, Core Frame Architecture, Arm Processors, Smartphone. 1. Introduction: What Is SoC? We first need to define system-on-chip (SoC). A SoC is a complex integrated circuit that implements most or all of the functions of a complete electronic system. -
System-On-Chip Design with Virtual Components
past designs can a huge chip be com- pleted within a reasonable time. This FEATURE solution usually entails reusing designs from previous generations of products ARTICLE and often leverages design work done by other groups in the same company. Various forms of intercompany cross licensing and technology sharing Thomas Anderson can provide access to design technol- ogy that may be reused in new ways. Many large companies have estab- lished central organizations to pro- mote design reuse and sharing, and to System-on-Chip Design look for external IP sources. One challenge faced by IP acquisi- tion teams is that many designs aren’t well suited for reuse. Designing with with Virtual Components reuse in mind requires extra time and effort, and often more logic as well— requirements likely to be at odds with the time-to-market goals of a product design team. Therefore, a merchant semiconduc- tor IP industry has arisen to provide designs that were developed specifically for reuse in a wide range of applications. These designs are backed by documen- esign reuse for tation and support similar to that d semiconductor provided by a semiconductor supplier. Here in the Recycling projects has evolved The terms “virtual component” from an interesting con- and “core” commonly denote reusable Age, designing for cept to a requirement. Today’s huge semiconductor IP that is offered for system-on-a-chip (SOC) designs rou- license as a product. The latter term is reuse may sound like tinely require millions of transistors. promoted extensively by the Virtual Silicon geometry continues to shrink Socket Interface (VSI) Alliance, a joint a great idea. -
Lecture Notes
Lecture #4-5: Computer Hardware (Overview and CPUs) CS106E Spring 2018, Young In these lectures, we begin our three-lecture exploration of Computer Hardware. We start by looking at the different types of computer components and how they interact during basic computer operations. Next, we focus specifically on the CPU (Central Processing Unit). We take a look at the Machine Language of the CPU and discover it’s really quite primitive. We explore how Compilers and Interpreters allow us to go from the High-Level Languages we are used to programming to the Low-Level machine language actually used by the CPU. Most modern CPUs are multicore. We take a look at when multicore provides big advantages and when it doesn’t. We also take a short look at Graphics Processing Units (GPUs) and what they might be used for. We end by taking a look at Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC). Stanford President John Hennessy won the Turing Award (Computer Science’s equivalent of the Nobel Prize) for his work on RISC computing. Hardware and Software: Hardware refers to the physical components of a computer. Software refers to the programs or instructions that run on the physical computer. - We can entirely change the software on a computer, without changing the hardware and it will transform how the computer works. I can take an Apple MacBook for example, remove the Apple Software and install Microsoft Windows, and I now have a Window’s computer. - In the next two lectures we will focus entirely on Hardware. -
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 . -
EE Concentration: System-On-A-Chip (Soc)
EE Concentration: System-on-a-Chip (SoC) Requirements: Complete ESE350, ESE370, CIS371, ESE532 Requirement Flow: Impact: The chip at the heart of your smartphone, tablet, or mp3 player (including the Apple A11, A12) is an SoC. The chips that run almost all of your gadgets today are SoCs. These are the current culmination of miniaturization and part count reduction that allows such systems to built inexpensively and from small part counts. These chips democratize innovation, by providing a platform for the deployment of novel ideas without requiring hundreds of millions of dollars to build new custom ICs. Description: Modern computational and control chips contain billions of transistors and run software that has millions of lines of code. They integrate complete systems including multiple, potentially heterogeneous, processing elements, sophisticated memory hierarchies, communications, and rich interfaces for inputs and outputs including sensing and actuations. To design these systems, engineers must understand IC technology, digital circuits, processor and accelerator architectures, networking, and composition and interfacing and be able to manage hardware/software trade-offs. This concentration prepares students both to participate in the design of these SoC architectures and to use SoC architectures as implementation vehicles for novel embedded computing tasks. Sample industries and companies: ● Integrated Circuit Design: ARM, IBM, Intel, Nvidia, Samsung, Qualcomm, Xilinx ● Consumer Electronics: Apple, Samsung, NEST, Hewlett Packard ● Systems: Amazon, CISCO, Google, Facebook, Microsoft ● Automotive and Aerospace: Boeing, Ford, Space-X, Tesla, Waymo ● Your startup Sample Job Titles: ● Hardware Engineer, Chip Designer, Chip Architect, Architect, Verification Engineer, Software Engineering, Embedded Software Engineer, Member of Technical Staff, VP Engineering, CTO Graduate research in: computer systems and architecture .