Evaluating MapReduce for Multi-core and Multiprocessor Systems

Posted in Conferences, Companies, Science, Development on March 09, 2007


Evaluating MapReduce for Multi-core and Multiprocessor Systems
Google Video tech Talks
February 27, 2007

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.

Watch Video

Tags: Techtalks, Google, Conferences, High Performance, Science, Lectures, Computer Science, Broadcasting, Development, Companies