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面向异构融合处理器的性能分析、优化及应用综述
引用本文:张峰,翟季冬,陈政,林甲灶,杜小勇. 面向异构融合处理器的性能分析、优化及应用综述[J]. 软件学报, 2020, 31(8): 2603-2624
作者姓名:张峰  翟季冬  陈政  林甲灶  杜小勇
作者单位:数据工程与知识工程教育部重点实验室(中国人民大学),北京 100872;中国人民大学信息学院,北京 100872;清华大学计算机科学与技术系,北京 100084;北京大学信息管理系,北京 100871
基金项目:国家重点研发计划(2016YFB0200100);国家自然科学基金项目(61732014,61722208,61802412)
摘    要:随着异构计算技术的不断进步,CPU和GPU等设备相集成的异构融合处理器在近些年得到了充分的发展,并引起了学术界和工业界的关注.将多种设备进行集成带来了许多好处,例如,多种设备可以访问同样的内存,可以进行细粒度的交互.然而,这也带来了系统编程和优化方面的巨大挑战.充分发挥异构融合处理器的性能,需要充分利用集成体系结构中共享内存等特性;同时,还需结合具体应用特征对异构融合处理器上的不同设备进行优化.首先对目前涉及异构融合处理器的研究工作进行了分析,之后介绍了异构融合处理器的性能分析工作,并进一步介绍了相关优化技术,随后对异构融合处理器的应用进行了总结.最后,对异构融合处理器未来的研究方向进行展望,并进行了总结.

关 键 词:CPU  GPU  异构融合处理器  性能分析  性能优化
收稿时间:2019-01-31
修稿时间:2020-04-09

Survey on Performance Analysis, Optimization, and Applications of Heterogeneous Fusion Processors
ZHANG Feng,ZHAI Ji-Dong,CHEN Zheng,LIN Jia-Zao,DU Xiao-Yong. Survey on Performance Analysis, Optimization, and Applications of Heterogeneous Fusion Processors[J]. Journal of Software, 2020, 31(8): 2603-2624
Authors:ZHANG Feng  ZHAI Ji-Dong  CHEN Zheng  LIN Jia-Zao  DU Xiao-Yong
Affiliation:Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), MOE, Beijing 100872, China;School of Information, Renmin University of China, Beijing 100872, China;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;Department of Information Management, Peking University, Beijing 100871, China
Abstract:With the development of heterogeneous computing technology, heterogeneous fusion processors, such as CPU-GPU integrated processors, have been fully developed in recent years, and arouse attention from both academia and industry. The fusion of different devices has several advantages. For example, all devices share the same memory and can have fine-grained cooperation. However, many system programming challenges and optimization challenges have emerged. To take full advantage of the capacity of heterogeneous fusion processors, we need to utilize features of heterogeneous fusion processors such as shared memory, and perform architecture optimizations to different devices according to different applications. We first analyze and summarize the research work related to heterogeneous fusion processors. Second, we introduce the related work about performance analysis. Third, we summarize the optimizations on heterogeneous fusion processors. We also provide a summarization for the applications that utilize heterogeneous fusion processors. At last, we provide the future directions on heterogeneous fusion processors and give conclusion.
Keywords:CPU  GPU  heterogeneous fusion processors  performance optimization  performance analysis
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