MpAssign: A framework for solving the many-core platform mapping problem

Abstract

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.