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 ...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.展开更多
大量工程应用问题可建模为结构化非线性规划,且这类问题的系数矩阵可分为稀疏型和稠密型两种类型.利用原始-对偶内点法(primal dual interior point method,PD-IPM),并结合分布式并行技术可高效求解此类问题.经典工程问题-机组组合(unit...大量工程应用问题可建模为结构化非线性规划,且这类问题的系数矩阵可分为稀疏型和稠密型两种类型.利用原始-对偶内点法(primal dual interior point method,PD-IPM),并结合分布式并行技术可高效求解此类问题.经典工程问题-机组组合(unit commitment,UC)为稀疏系数矩阵的结构化非线性规划,本文根据PD-IPM原理,对UC模型进行连续松弛预处理,结合快速解耦技术解耦牛顿修正方程并设计CPU-GPU协同并行算法求解子问题,最后将结果与带稠密型子问题的结构化非线性规划的求解结果进行比较和分析.实验结果显示,本文所设计的算法对于两种不同类型的结构化非线性规划求解均能获得较好的加速比.展开更多
In recent years,with the development of processor architecture,heterogeneous processors including Center processing unit(CPU)and Graphics processing unit(GPU)have become the mainstream.However,due to the differences o...In recent years,with the development of processor architecture,heterogeneous processors including Center processing unit(CPU)and Graphics processing unit(GPU)have become the mainstream.However,due to the differences of heterogeneous core,the heterogeneous system is now facing many problems that need to be solved.In order to solve these problems,this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies.To improve the performance of the system,this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks.The combination strategy consists of two sub-strategies,the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure.The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems.The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples.Based on this information,an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme,which fully utilizes the characteristics of heterogeneous cores.The experimental results show that the combination strategy reduces task execution time by 29.13%on average.In the case of processing multiple tasks,the multi-task scheduling strategy reduces the execution time by up to 23.38%based on the combined strategy.Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.展开更多
基金supported by ZTE‑University‑Institute Fund Project under Grant No.IA20230629009.
文摘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.
基金This work is supported by Beijing Natural Science Foundation[4192007]the National Natural Science Foundation of China[61202076]Beijing University of Technology Project No.2021C02.
文摘In recent years,with the development of processor architecture,heterogeneous processors including Center processing unit(CPU)and Graphics processing unit(GPU)have become the mainstream.However,due to the differences of heterogeneous core,the heterogeneous system is now facing many problems that need to be solved.In order to solve these problems,this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies.To improve the performance of the system,this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks.The combination strategy consists of two sub-strategies,the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure.The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems.The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples.Based on this information,an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme,which fully utilizes the characteristics of heterogeneous cores.The experimental results show that the combination strategy reduces task execution time by 29.13%on average.In the case of processing multiple tasks,the multi-task scheduling strategy reduces the execution time by up to 23.38%based on the combined strategy.Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.
