LoopPoint: Checkpoint-driven Sampled Simulation for Multi-threaded Applications

Generic multi-threaded sampled simulation has been a long-standing, challenging problem with the potential to help change how researchers study modern, complex computing systems. Yet, a practical solution for reducing complex multithreaded applications into a representative sample has been elusive. Existing techniques either do not provide significant speedups to be useful (Time-based Sampling techniques can show less than a 10× speedup compared to a fully-detailed simulation) or apply only to particular synchronization types (BarrierPoint for barrier-based workloads). In addition, workload-specific solutions can be rigid with respect to region selection, which can limit the overall simulation speedup when regions are large. A solution is needed that both supports generic multi-threaded applications, no matter the synchronization primitives used, as well as allows for ease of deployment and fast evaluation.

In this work, we aim to solve these challenges and propose a novel sampling technique for multi-threaded applications, called LoopPoint, that is both agnostic to the type of synchronization primitives used and scales by the similarity exhibited by the application. The proposed methodology combines several vital features, including (1) repeatable, up-front application analysis, (2) a novel clustering approach to take into account run-time parallelism, and (3) the use of loop-based simulation markers to divide the work into measurable chunks, even in the presence of spin-loops. LoopPoint identifies representative simulation regions that can be simulated in parallel to achieve speedups of up to 801× for the train input set of the multi-threaded SPEC CPU2017 benchmarks with an average simulation error of just 2.33%. For the ref inputs of CPU2017, we calculate the speedup with LoopPoint to be 11,587× on average (for parallel simulation), and up to 31,253×, demonstrating how the identification of application regularity and loops can lead to significant simulation improvements compared to state-of-the-art solutions.