We apply a scalable approach for practical, comprehensive design space evaluation and optimization. This approach combines design space sampling and statistical inference to identify trends from a sparse simulation of the space. The computational efficiency of sampling and inference enables new capabilities in design space exploration. We illustrate these new capabilities using performance and power models for three studies of a 260,000 point design space: (1) pareto frontier analysis, (2) pipeline depth analysis, and (3) multiprocessor heterogeneity analysis. For each study, we provide an assessment of predictive error and sensitivity of observed trends to such error.

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In: International Symposium on High-Performance Computer Architecture (HPCA)

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