Statistical inference for efficient microarchitectural and application analysis

Journal Article

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. © 2006 IEEE.

Full Text

Duke Authors

Cited Authors

  • Lee, BC

Published Date

  • December 1, 2006

Published In

  • Proceedings of the 2006 Acm/Ieee Conference on Supercomputing, Sc'06

Digital Object Identifier (DOI)

  • 10.1145/1188455.1188591

Citation Source

  • Scopus