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Evaluating MapReduce for multi-core and multiprocessor systems (2007)
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| Venue: | In HPCA ’07: Proceedings of the 13th International Symposium on High-Performance Computer Architecture |
| Citations: | 256 - 3 self |
BibTeX
@INPROCEEDINGS{Ranger07evaluatingmapreduce,
author = {Colby Ranger and Ramanan Raghuraman and Arun Penmetsa and Gary Bradski and Christos Kozyrakis},
title = {Evaluating MapReduce for multi-core and multiprocessor systems},
booktitle = {In HPCA ’07: Proceedings of the 13th International Symposium on High-Performance Computer Architecture},
year = {2007},
pages = {13--24},
publisher = {IEEE Computer Society}
}
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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 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. 1
Keyphrases
multiprocessor system shared-memory system thread creation dynamic task scheduling fault tolerance efficient runtime system careful implementation multi-processor system promising model functional-style code distributed system scalable performance mapreduce model performance potential symmetric multiprocessor system processor node error recovery feature programming api phoenix runtime simple parallel code data partitioning mapreduce code lower-level apis application development
