Evaluating MapReduce for Multi-core and Multiprocessor Systems

  • Authors:

  • Colby Ranger;Ramanan Raghuraman;Arun Penmetsa;Gary Bradski;Christos Kozyrakis

  • Affiliations:

  • Computer Systems Laboratory, Stanford University. Email: cranger@stanford.edu;Computer Systems Laboratory, Stanford University. Email: ramananr@stanford.edu;Computer Systems Laboratory, Stanford University. Email: penmetsa@stanford.edu;Computer Systems Laboratory, Stanford University. Email: garybradski@gmail.com;Computer Systems Laboratory, Stanford University. Email: christos@ee.stanford.edu.

  • Venue:
  • HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
  • Year:
  • 2007

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Abstract

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 automaticatlly 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 run-time 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.