Directed Statistical Warming through Time Traveling

Improving the speed of computer architecture evaluation is of paramount importance to shorten the time-to-market when developing new platforms. Sampling is a widely used methodology to speed up workload analysis and performance evaluation by extrapolating from a set of representative detailed regions. Installing an accurate cache state for each detailed region is critical to achieving high accuracy. Prior work requires either huge amounts of storage (checkpoint-based warming), an excessive number of memory accesses to warm up the cache (functional warming), or the collection of a large number of reuse distances (randomized statistical warming) to accurately predict cache warm-up effects. This work proposes DeLorean, a novel statistical warming and sampling methodology that builds upon two key contributions: directed statistical warming and time traveling. Instead of collecting a large number of randomly selected reuse distances as in randomized statistical warming, directed statistical warming collects a select number of key reuse distances, i.e., the most recent reuse distance for each unique memory location referenced in the detailed region. Time traveling leverages virtualized fast-forwarding to quickly 'look into the future' --- to determine the key cachelines --- and then 'go back in time' --- to collect the reuse distances for those key cachelines at near-native hardware speed through virtualized directed profiling. Directed statistical warming reduces the number of warm-up references by 30× compared to randomized statistical warming. Time traveling translates this reduction into a 5.7× simulation speedup. In addition to improving simulation speed, DeLorean reduces the prediction error from around 9% to around 3% on average. We further demonstrate how to amortize warm-up cost across multiple parallel simulations in design space exploration studies. Implementing DeLorean in gem5 enables detailed cycle-accurate simulation at a speed of 126 MIPS.