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dc.contributor.authorde Kruijf, Marcen_US
dc.contributor.authorSankaralingam, Karthikeyanen_US
dc.description.abstractMapReduce is a simple and flexible parallel programming model proposed by Google for large scale data processing in a distributed computing environment [4]. In this paper, we present a design and implementation of MapReduce for the Cell architecture. This model provides a simple machine abstraction to users, hiding parallelization and hardware primitives. Our runtime automatically manages parallelization, scheduling, partitioning and memory transfers. We study the basic characteristics of the model and evaluate our runtime�s performance, scalability, and efficiency for micro-benchmarks and complete applications.We show that the model is well suited for many applications that map well to the Cell architecture, and that the runtime sustains high performance on these applications. For other applications, we analyze runtime performance and describe why performance is less impressive. Overall, we find that the simplicity of the model and the efficiency of our MapReduce implementationmake it an attractive choice for the Cell platform specifically and more generally to distributed memory systems and software-exposed memories.en_US
dc.publisherUniversity of Wisconsin-Madison Department of Computer Sciencesen_US
dc.titleMapReduce for the Cell B.E. Architectureen_US
dc.typeTechnical Reporten_US

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    Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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