Directed Statistical Warming through Time Traveling
Nikos Nikoleris, Lieven Eeckhout, Erik Hägersten, Trevor E. Carlson · 2019
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.