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A computer system simulator is a fundamental tool for computer architects to try out brand new ideas or explore the system’s response to different configurations when executing different program codes. However, even simulating the CPU core in detail is time-consuming as the execution rate slows down by several orders of magnitude compared to native execution. To solve this problem, previous work, namely SMARTS, demonstrates a statistical sampling methodology that records measurements only from tiny samples throughout the simulation. It spends only a fraction of the full simulation time on these sample measurements. In-between detailed sample simulations, SMARTS fast-forwards in the simulation using a greatly simplified and much faster simulation model (compared to full detail), which maintains only necessary parts of the architecture, such as cache memory. This maintenance process is called warming. While warming is mandatory to keep the simulation accuracy high, caches may be sufficiently warm for an accurate simulation long before reaching the sample. In other words, much time may be wasted on warming in SMARTS.

In this work, we show that caches can be kept in an accurate state with much less time spent on warming. The first paper presents Adaptive Cache Warming, a methodology for identifying the minimum amount of warming in an iterative process for every SMARTS sample. The rest of the simulation time, previously spent on warming, can be skipped by fast-forwarding between samples using native hardware execution of the code. Doing so will thus result in significantly faster statistically sampled simulation while maintaining accuracy. The second paper presents Cache Merging, which mitigates the redundant warmings introduced in Adaptive Cache Warming. We solve this issue by going back in time and merging the existing warming with a cache warming session that comes chronologically before the existing warming. By removing the redundant warming, we yield even more speedup. Together, Adaptive Cache Warming and Cache Merging is a powerful boost for statistically sampled simulations.