A Survey on Task Scheduling of CPU-GPU Heterogeneous Cluster  

作　　者：ZHOU Yiheng ZENG Wei ZHENG Qingfang LIU Zhilong CHEN Jianping 

机构地区：[1]University of Shanghai for Science and Technology,Shanghai 200093,China [2]National Key Laboratory for Multimedia Information Processing,School of Computer Science,Peking University,Beijing 100871,China [3]State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518055,China [4]ZTE Corporation,Shenzhen 518057,China

出　　处：《ZTE Communications》2024年第3期83-90,共8页中兴通讯技术（英文版）

基　　金：supported by ZTE‑University‑Institute Fund Project under Grant No.IA20230629009.

摘　　要：This paper reviews task scheduling frameworks,methods,and evaluation metrics of central processing unit-graphics processing unit(CPU-GPU)heterogeneous clusters.Task scheduling of CPU-GPU heterogeneous clusters can be carried out on the system level,nodelevel,and device level.Most task-scheduling technologies are heuristic based on the experts’experience,while some technologies are based on statistic methods using machine learning,deep learning,or reinforcement learning.Many metrics have been adopted to evaluate and compare different task scheduling technologies that try to optimize different goals of task scheduling.Although statistic task scheduling has reached fewer research achievements than heuristic task scheduling,the statistic task scheduling still has significant research potential.

关 键 词：CPU-GPU heterogeneous cluster task scheduling heuristic task scheduling statistic task scheduling PARALLELIZATION 

分 类 号：TP332[自动化与计算机技术—计算机系统结构]