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Evaluating MapReduce for Multi-core and Multiprocessor Systems

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5 Author(s)
Ranger, C. ; Comput. Syst. Lab., Stanford Univ., CA ; Raghuraman, R. ; Penmetsa, A. ; Bradski, G.
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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 simple parallel code

Published in:

High Performance Computer Architecture, 2007. HPCA 2007. IEEE 13th International Symposium on

Date of Conference:

10-14 Feb. 2007

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