Picking statistically valid and early simulation points

Abstract

Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months to complete. To address this issue we have recently proposed using simulation points (found by only examining basic block execution frequency profiles) to increase the efficiency and accuracy of simulation. Simulation points are a small set of execution samples that when combined represent the complete execution of the program. We present a statistically driven algorithm for forming clusters from which simulation points are chosen, and examine algorithms for picking simulation points earlier in a program's execution-in order to significantly reduce fast-forwarding time during simulation. In addition, we show that simulation points can be used independent of the underlying architecture. The points are generated once for a program/input pair by only examining the code executed. We show the points accurately track hardware metrics (e.g., performance and cache miss rates) between different architecture configurations. They can therefore be used across different architecture configurations to allow a designer to make accurate trade-off decisions between different configurations.