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.

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The semiconductor industry as a whole is growing increasingly concerned about the possible presence of delay-inducing defects. There exist structured test generation and application techniques which can detect them, but there are many practical issues associated with their use. These problems are particularly acute when using low cost test equipment. In this paper, we describe an overall approach for implementing scan-based delay testing with emphasis on low-cost test.

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