SWAPP: A Framework for Performance Projections of HPC Applications Using Benchmarks

S. Sharkawi, Don DeSota, R. Panda, Stephen Stevens, V. Taylor, Xingfu Wu
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引用次数: 14

Abstract

Surrogate-based Workload Application Performance Projection (SWAPP) is a framework for performance projections of High Performance Computing (HPC) applications using benchmark data. Performance projections of HPC applications onto various hardware platforms are important for hardware vendors and HPC users. The projections aid hardware vendors in the design of future systems and help HPC users with system procurement. SWAPP assumes that one has access to a base system and only benchmark data for a target system, the target system is not available for running the HPC application. Projections are developed using the performance profiles of the benchmarks and application on the base system and the benchmark data for the target system. SWAPP projects the performances of compute and communication components separately then combine the two projections to get the full application projection. In this paper SWAPP was used to project the performance of three NAS Multi-Zone benchmarks onto three systems (an IBM POWER6 575 cluster and an IBM Intel West mere x5670 both using an Infiniband interconnect and an IBM Blue Gene/P with a 3D Torus and Collective Tree interconnects), the base system is an IBM POWER5+ 575 cluster. The projected performance of the three benchmarks was within 11.44% average error magnitude and standard deviation of 2.64% for the three systems.
SWAPP:一个使用基准的高性能计算应用程序的性能预测框架
基于代理的工作负载应用程序性能预测(SWAPP)是一个使用基准数据对高性能计算(HPC)应用程序进行性能预测的框架。高性能计算应用程序在各种硬件平台上的性能预测对硬件供应商和高性能计算用户来说非常重要。这些预测可以帮助硬件供应商设计未来的系统,并帮助HPC用户进行系统采购。SWAPP假设可以访问基本系统,并且只能访问目标系统的基准数据,目标系统不可用于运行HPC应用程序。使用基准测试和应用程序在基本系统上的性能概况以及目标系统的基准测试数据来开发预测。SWAPP分别对计算组件和通信组件的性能进行投影,然后将这两个投影结合起来得到完整的应用程序投影。在本文中,SWAPP用于将三个NAS Multi-Zone基准测试的性能投射到三个系统上(一个IBM POWER6 575集群和一个IBM Intel West mere x5670,两者都使用Infiniband互连和一个IBM Blue Gene/P与3D Torus和Collective Tree互连),基本系统是一个IBM POWER5+ 575集群。三个系统的预测性能在11.44%的平均误差量级和2.64%的标准差范围内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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