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...

• Type: Conference
• Year: 2007
• Where: HPCA
• Authors: Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary R. Bradski, Christos Kozyrakis
• Keywords: Computer Architecture, Efficient Runtime System, HPCA 2007, Shared-memory Systems, Symmetric Multiprocessor Systems