水准点(测量)
排名(信息检索)
公制(单位)
计算机科学
共轭梯度法
度量(数据仓库)
计算
绩效改进
超级计算机
性能指标
绩效衡量
数据挖掘
计算机工程
机器学习
算法
并行计算
工程类
地理
业务
管理
营销
经济
运营管理
大地测量学
作者
Michael A. Heroux,Jack Dongarra
摘要
The High Performance Linpack (HPL), or Top 500, benchmark [1] is the most widely recognized and discussed metric for ranking high performance computing systems. However, HPL is increasingly unreliable as a true measure of system performance for a growing collection of important science and engineering applications. In this paper we describe a new high performance conjugate gradient (HPCG) benchmark. HPCG is composed of computations and data access patterns more commonly found in applications. Using HPCG we strive for a better correlation to real scientific application performance and expect to drive computer system design and implementation in directions that will better impact performance improvement.
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