Predicting parallel applications performance using signatures: The workload effect

J. M. Canillas, Alvaro Wong, Dolores Rexachs, E. Luque
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引用次数: 4

Abstract

Being able to accurately estimate how an application will perform in a specific computational system provides many useful benefits and can result in smarter decisions. In this work we present a novel approach to model the behavior of message passing parallel applications. Based in the concept of signatures, which are the most relevant parts of an application (phases), we are able to build a model that allows us to predict the application execution time in different systems with variable input data size. Executing these signatures with different input data sizes defines a program's behavior partial function. Using regression we can generalize this behavior function to predict an application performance in a target system with other input data size within a predefined range. We explain our methodology and in order to validate the proposal present results using a synthetic program and well known applications.
使用签名预测并行应用程序性能:工作负载效应
能够准确地估计应用程序在特定计算系统中的执行情况,可以提供许多有用的好处,并可以产生更明智的决策。在这项工作中,我们提出了一种新的方法来模拟消息传递并行应用程序的行为。基于签名的概念(签名是应用程序(阶段)中最相关的部分),我们能够构建一个模型,该模型允许我们预测具有可变输入数据大小的不同系统中的应用程序执行时间。用不同的输入数据大小执行这些签名定义了程序的行为部分函数。使用回归,我们可以推广此行为函数,以在预定义范围内的其他输入数据大小的目标系统中预测应用程序性能。我们解释了我们的方法,并为了验证使用合成程序和众所周知的应用程序提出的建议结果。
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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