On July 6,2009,Qualcomm Incorporated,a leading developer and innovator of advanced wireless technologies,products and services,and ZTE Corporation,a leading global provider of telecommunications equipment and network
Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications runni...Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing(HPC)clusters,aiming to improve application performance without breaching peak power constraints and total energy consumption.Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance.It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so.We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge(HPCC)benchmark suites and evaluate it on a representative HPC cluster.Experimental results show that our approach can effectively mitigate memory contention to improve application performance,and it achieves this without significantly increasing the peak power and overall energy consumption.Our approach obtains on average 12.69%performance improvement over the default resource allocation strategy,but uses 7.06%less total power,which translates into 17.77%energy savings.展开更多
文摘On July 6,2009,Qualcomm Incorporated,a leading developer and innovator of advanced wireless technologies,products and services,and ZTE Corporation,a leading global provider of telecommunications equipment and network
基金supported in part by the Advanced Research Project of China(No.31511010203)the Research Program of NUDT(No.ZK18-03-10)。
文摘Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing(HPC)clusters,aiming to improve application performance without breaching peak power constraints and total energy consumption.Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance.It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so.We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge(HPCC)benchmark suites and evaluate it on a representative HPC cluster.Experimental results show that our approach can effectively mitigate memory contention to improve application performance,and it achieves this without significantly increasing the peak power and overall energy consumption.Our approach obtains on average 12.69%performance improvement over the default resource allocation strategy,but uses 7.06%less total power,which translates into 17.77%energy savings.
