This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce, originally developed by Google, allows programmers to write functional-style code that is automatically parallelized and scheduled in a distributed system. The authors describe Phoenix, an implementation of MapReduce for shared-memory systems, which includes a programming API and an efficient runtime system. Phoenix automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across processor nodes. The paper evaluates Phoenix on multi-core and symmetric multiprocessor systems, demonstrating its performance potential and error recovery features. It also compares MapReduce code to code written in lower-level APIs such as P-threads. Overall, the paper establishes that, with careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simple parallel code.This paper evaluates the suitability of the MapReduce model for multi-core and multi-processor systems. MapReduce, originally developed by Google, allows programmers to write functional-style code that is automatically parallelized and scheduled in a distributed system. The authors describe Phoenix, an implementation of MapReduce for shared-memory systems, which includes a programming API and an efficient runtime system. Phoenix automatically manages thread creation, dynamic task scheduling, data partitioning, and fault tolerance across processor nodes. The paper evaluates Phoenix on multi-core and symmetric multiprocessor systems, demonstrating its performance potential and error recovery features. It also compares MapReduce code to code written in lower-level APIs such as P-threads. Overall, the paper establishes that, with careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simple parallel code.