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Evaluating MapReduce for Multi-core and Multiprocessor Systems

10 years 11 months ago
Evaluating MapReduce for Multi-core and Multiprocessor Systems
This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce was created by Google for application development on data-centers with thousands of servers. It allows programmers to write functional-style code that is automatically parallelized and scheduled in a distributed system. We describe Phoenix, an implementation of MapReduce for shared-memory systems that includes a programming API and an efficient runtime system. The Phoenix runtime automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across processor nodes. We study Phoenix with multi-core and symmetric multiprocessor systems and evaluate its performance potential and error recovery features. We also compare MapReduce code to code written in lower-level APIs such as P-threads. Overall, we establish that, given a careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simpl...
Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, G
Added 01 Dec 2009
Updated 01 Dec 2009
Type Conference
Year 2007
Where HPCA
Authors Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary R. Bradski, Christos Kozyrakis
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