计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 5-16.doi: 10.11896/jsjkx.200600045

• 高性能计算 • 上一篇    下一篇

异构混合并行计算综述

阳王东, 王昊天, 张宇峰, 林圣乐, 蔡沁耘   

  1. 湖南大学信息科学与工程学院 长沙410082
  • 出版日期:2020-08-15 发布日期:2020-08-10
  • 通讯作者: 阳王东(yangwangdong@163.com)
  • 基金资助:
    国家重点研发计划(2019YFB2103004);国家自然科学基金面上项目(61872127)

Survey of Heterogeneous Hybrid Parallel Computing

YANG Wang-dong, WANG Hao-tian, ZHANG Yu-feng, LIN Sheng-le , CAI Qin-yun   

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
  • Online:2020-08-15 Published:2020-08-10
  • Supported by:
    This word was supported by the National Key R&D Program of China (2019YFB2103004) and National Natural Science Foundation of China (61872127).

摘要: 随着人工智能和大数据等计算机应用对算力需求的迅猛增长以及应用场景的多样化, 异构混合并行计算成为了研究的重点。文中介绍了当前主要的异构计算机体系结构, 包括CPU/协处理器、CPU/众核处理器、CPU/ASCI和CPU/FPGA等;简述了异构混合并行编程模型随着各类异构混合结构的发展而做出的改变, 异构混合并行编程模型可以是对现有的一种语言进行改造和重新实现, 或者是现有异构编程语言的扩展, 或者是使用指导性语句异构编程, 或者是容器模式协同编程。分析表明, 异构混合并行计算架构会进一步加强对AI的支持, 同时也会增强软件的通用性。文中还回顾了异构混合并行计算中的关键技术, 包括异构处理器之间的并行任务划分、任务映射、数据通信、数据访问, 以及异构协同的并行同步和异构资源的流水线并行等。根据这些关键技术, 文中指出了异构混合并行计算面临的挑战, 如编程困难、移植困难、数据通信开销大、数据访问复杂、并行控制复杂以及资源负载不均衡等。最后分析了异构混合并行计算面临的挑战, 指出目前关键的核心技术需要从通用与AI专用异构计算的融合、异构架构的无缝移植、统一编程模型、存算一体化、智能化任务划分和分配等方面进行突破。

关键词: 异构计算, 并行计算, 异构并行编程, 异构混合编程, 异构架构

Abstract: With the rapid increase in computing power demand of computer applications such as artificial intelligence and big data and the diversification of application scenarios, the research of heterogeneous hybrid parallel computing has become the focus of research.This paper introduces the current main heterogeneous computer architecture, including CPU/coprocessor, CPU/many-core processor, CPU/ASCI and CPU/FPGA heterogeneous architectures.The changes made by the heterogeneous hybrid parallel programming model with the development of various heterogeneous hybrid structures are briefly described, which is a transformation and re-implementation of an existing language, or an extension of an existing heterogeneous programming language, or heterogeneous programming using instructional statements, or container pattern collaborative programming.The analysis shows that the heterogeneous hybrid parallel computing architecture will further strengthen the support for AI, and will also enhance the versatility of the software.This paper reviewes the key technologies in heterogeneous hybrid parallel computing, including parallel task partitioning, task mapping, data communication, data access between heterogeneous processors, parallel synchronization of heterogeneous collaboration, and pipeline parallelism of heterogeneous resources.Based on these key technologies, this paper points out the challenges faced by heterogeneous hybrid parallel computing, such as programming difficulties, portability difficulties, large data communication overhead, complex data access, complex parallel control, and uneven resource load.The challenges faced by heterogeneous hybrid parallel computing are analyzed, and this paper concludes that the current key core technologies need to be integrated from general-purpose and AI-specific heterogeneous computing, seamless migration of heterogeneous architectures, unified programming model, integration of storage and computing, and intelligence breakthroughs in task division and allocation

Key words: Heterogeneous computing, Parallel computing, Heterogeneous parallel programming, Heterogeneous hybrid programming, Heterogeneous architecture

中图分类号: 

  • TP301
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