Peta-scale high-performance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to co...Peta-scale high-performance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains (e.g., physics and biology) rather than MPI and OpenMP. This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-1A, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.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
化学工程研究涉及到新化合物和材料的设计与生产、化学反应分析、流体力学模拟等等方面,传统的试验方法不但过程复杂,需要消耗昂贵的化学材料,而且耗时、耗能。为了提高效率、节能减排,计算机模拟逐渐用于化学工程研究中,而且随着网格...化学工程研究涉及到新化合物和材料的设计与生产、化学反应分析、流体力学模拟等等方面,传统的试验方法不但过程复杂,需要消耗昂贵的化学材料,而且耗时、耗能。为了提高效率、节能减排,计算机模拟逐渐用于化学工程研究中,而且随着网格计算的普及,大大提高了化学工程研究的效率。本文提出了一个基于China Grid Support Platform(CGSP)搭建的网格协作计算平台——化工网格(ChemGrid),该平台实现了异构资源集成共享,为各种化工应用提供了模拟计算环境,并且提供了针对化工领域的语义知识检索服务。化工研究者借助上述服务可以实现计算机模拟研究、知识获取和共享,从而起到了工程领域优化与节能减排的作用。本文详细介绍了该平台的架构及其上的应用。展开更多
基金Project(61170049) supported by the National Natural Science Foundation of ChinaProject(2012AA010903) supported by the National High Technology Research and Development Program of China
文摘Peta-scale high-performance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains (e.g., physics and biology) rather than MPI and OpenMP. This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-1A, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.
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.
文摘化学工程研究涉及到新化合物和材料的设计与生产、化学反应分析、流体力学模拟等等方面,传统的试验方法不但过程复杂,需要消耗昂贵的化学材料,而且耗时、耗能。为了提高效率、节能减排,计算机模拟逐渐用于化学工程研究中,而且随着网格计算的普及,大大提高了化学工程研究的效率。本文提出了一个基于China Grid Support Platform(CGSP)搭建的网格协作计算平台——化工网格(ChemGrid),该平台实现了异构资源集成共享,为各种化工应用提供了模拟计算环境,并且提供了针对化工领域的语义知识检索服务。化工研究者借助上述服务可以实现计算机模拟研究、知识获取和共享,从而起到了工程领域优化与节能减排的作用。本文详细介绍了该平台的架构及其上的应用。
