Hybrid Techniques for Fast Multicore Simulation

Abstract

One of the challenges in the design of multicore architectures concerns the fast evaluation of hardware design-tradeoffs using simulation techniques. Simulation tools for multicore architectures tend to have long execution times that grow linearly with the number of cores simulated. In this paper, we present two hybrid techniques for fast and accurate multicore simulation. Our first method, the Monte Carlo Co-Simulation (MCCS) scheme, considers application phases, and within each phase, interleaves a Monte Carlo modeling scheme with a traditional simulator, such as Simics. Our second method, the Curve Fitting Based Simulation (CFBS) scheme, is tailored to evaluate the behavior of applications with multiple iterations, such as scientific applications that have consistent cycles per instruction (CPI) behavior within a subroutine over different iterations. In our CFBS method, we represent the CPI profile of a subroutine as a signature using curve fitting and represent the entire application execution as a set of signatures to predict performance metrics. Our results indicate that MCCS can reduce simulation time by as much as a factor of 2.37, with a speedup of 1.77 on average compared to Simics. We also observe that CFBS can reduce simulation time by as much as a factor of 13.6, with a speedup of 6.24 on average. The observed average relative errors in CPI compared to Simics are 32% for MCCS and significantly lower, at 2%, for CFBS.

This work was funded in part through grants 0720749, 0444345, 0811687, 0720645, and 0702519 from the National Science Foundation.

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