Chip Multiprocessor Design Space Exploration through Statistical Simulation

Abstract

Developing fast chip multiprocessor simulation techniques is a challenging problem. Solving this problem is especially valuable for design space exploration purposes during the early stages of the design cycle where a large number of design points need to be evaluated quickly. This paper studies statistical simulation as a fast simulation technique for chip multiprocessor (CMP) design space exploration. The idea of statistical simulation is to measure a number of program execution characteristics from a real program execution through profiling, to generate a synthetic trace from it, and simulate that synthetic trace as a proxy for the original program. The important benefit is that the synthetic trace is much shorter compared to a real program trace, which leads to substantial simulation speedups. This paper enhances state-of-the-art statistical simulation: 1) by modeling the memory address stream behavior in a more microarchitecture-independent way and 2) by modeling a program's time-varying execution behavior. These two enhancements enable accurately modeling resource conflicts in shared resources as observed in the memory hierarchy of contemporary chip multiprocessors when multiple programs are coexecuting on the CMP. Our experimental evaluation using the SPEC CPU benchmarks demonstrates average prediction error of 7.3 percent across a range of CMP configurations while varying the number of cores and memory hierarchy configurations.