LAUSR

Detailed cycle-accurate simulation of multicore systems is naturally slow. Statistical simulation is one alternative that permits trading off simulation speed for accuracy. However, there is a lack of effective memory locality models for multicore applications. Hence, existing statistical simulators neglect data-sharing between threads. Additionally, the standard method to speed up statistical simulations is to blindly reduce the trace length to be synthesized. While this gives good control over the speedup, it leaves the simulation error unbounded. In this work, we introduce a novel statistical simulation methodology for exploration of shared-memory multicore systems. It includes a new sharing-locality model (Shalom) that captures and reproduces data-sharing in multithread applications. Furthermore, we propose a method to bound the simulation error for a particular metric while maximizing speedup. The technique works by monitoring the convergence of the statistical synthesis. It is referred to as convergence-deterministic simulation (Condens). The combination of Shalom and Condens is around 130x faster than cycle-accurate simulations with reasonable accuracy loss. Our approach is also 5x faster than state-of-the-art sampling simulation under the same accuracy level. Compared to previous statistical simulators ignoring sharing, our approach is 2x more accurate for performance metrics and 8x more accurate for cache miss estimations.