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	SCHEDULE: NOV 11-17, 2006 Statistical Inference for Efficient Microarchitectural and Application Analysis Session: ACM Student Research Competition Finalists Event Type: ACM SRC Poster, Poster Time: 11:00am - 11:30am Session Chair: Jeffrey J. Evans Author(s): Benjamin C Lee Location: 24-25 Abstract: Microarchitectural design exploration is often inefficient and ad hoc due to computational costs of simulators. Trends toward multi-core, multi-threading lead to diversity in viable core designs, thereby requiring comprehensive design exploration while exponentially increasing design space size. Similarly, application performance topology is a function of input parameters, but models to optimize performance and/or predict scalability are increasingly difficult to derive analytically due to system complexity. We collect measurements sampled sparsely, uniformly at random from the space of interest and formulate non-linear regression models. We demonstrate the broad effectiveness of regression for predicting (1) the power and performance of a microarchitectural design space with median error rates of 5.5 to 7.5 percent using 1K samples from a 1B point space and (2) the performance of parallel applications, Semicoarsening Multigrid and High-Performance Linpack, with median error rates of 2.5 to 5.0 percent using 500 samples from more than 3K points. Chair/Author Details: Jeffrey J. Evans (Chair) Purdue University Benjamin C Lee Harvard University
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