MpAssign:用于解决多核平台映射问题的框架

Y. Bouchebaba, A. Özcan, P. Paulin, G. Nicolescu
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引用次数: 5

摘要

提供大量并行执行资源的多核平台作为对嵌入式应用程序日益增长的计算需求的响应而出现。这种趋势带来的一个主要挑战是并行资源上应用程序的有效映射。这是一个非常重要的问题,因为在描述应用程序和底层平台体系结构时需要考虑许多参数。近年来,一些学者提出使用多目标进化算法(MOEA)来解决片上网络(NoC)映射应用中的这一问题。然而,这些建议有几个局限性:(1)只探索了很少的元启发式(主要是NSGAII和SPEA2),(2)只提供了很少的成本函数,(3)它们只处理少量的应用和架构约束。在本文中,我们提出了一个新的框架,避免了上述所有问题。我们的框架允许设计人员(1)探索几个新的元启发式,(2)轻松添加一个新的成本函数(或使用一个现有的),(3)考虑任何数量的架构和应用程序约束。本文还通过实验说明了我们的框架如何应用于基于NoC的多核平台上映射流应用程序的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MpAssign: A framework for solving the many-core platform mapping problem
Many-core platforms, providing large numbers of parallel execution resources, emerge as a response to the increasing computation needs of embedded applications. A major challenge raised by this trend is the efficient mapping of applications on parallel resources. This is a nontrivial problem because of the number of parameters to be considered for characterizing both the applications and the underlying platform architectures. Recently, several authors have proposed to use Multi-Objective Evolutionary Algorithm (MOEA) to solve this problem within the context of mapping applications on Network-on-Chips (NoC). However, these proposals have several limitations: (1) only few meta-heuristics are explored (mainly NSGAII and SPEA2), (2) only few cost functions are provided, and (3) they only deal with a small number of the application and architecture constraints. In this paper, we propose a new framework which avoids all of the problems cited above. Our framework allows designers to (1) explore several new meta-heuristics, (2) easily add a new cost function (or to use an existing one) and (3) take into account any number of architecture and application constraints. The paper also presents experiments illustrating how our framework is applied to the problem of mapping streaming applications on a NoC based many-core platform.
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