Follow the Leader: Alternating CPU/GPU Computations in PDES

Despite the successes of graphics processing units (GPUs) in accelerating simulations in several research fields, their use is largely restricted to domain-specific workloads that consistently offer the large degree of inherent parallelism and computational intensity at which GPUs excel. When targeting generic discrete-event simulations, whose dynamics can vary wildly over time, a static choice between a GPU-based and traditional CPU-based execution is likely to be suboptimal. Here, we explore a parallel discrete-event (PDES) execution scheme for CPU-GPU platforms that aims to approximate an optimal dynamic device choice. Starting from an intermediate model state, a current "leader"device running the simulation is periodically challenged by a brief concurrent run on another device starting from an intermediate model state. Based on the gathered performance measurements, a forecasting scheme determines the leader for the next period. The execution time and power consumption of this scheme hinge on 1) an efficient mechanism for providing the "follower"device with a consistent model state, and 2) robust performance forecasting to justify the device choices. We present these building blocks, their implementation combining the existing CPU and GPU simulators ROOT-Sim and GPUTW, and measurement results demonstrating substantially reduced execution time without increasing energy consumption over a static device choice.