Task Scheduling for Heterogeneous Multicore Systems

In recent years, as the demand for low energy and high performance computing has steadily increased, heterogeneous computing has emerged as an important and promising solution. Because most w orkloads can typically run most eff iciently on certain types of cores, mapping tasks on the best available resources can not only save energy but also deliver high performance. How ever, optimal task scheduling for performance and/or energy is yet to be solved for heterogeneous platforms. The w ork presented herein mathematically formulates the optimal heterogeneous system task scheduling as an optimization problem using queueing theory. We analytically solve for the common case of tw o processor types, e.g., CPU+GPU, and give an optimal policy (CAB). We design the GrIn heuristic to eff iciently solve for near-optimal policy for any number of processor types (within 1.6% of the optimal). Both policies w ork for any task size distribution and processing order, and are therefore, general and practical. We extensively simulate and validate the theory, and implement the proposed policy in a CPU-GPU real platform to show the optimal throughput and energy improvement. Comparing to classic policies like load-balancing, our results range from 1.08x~2.24x better performance or 1.08x~2.26x better energy ef f iciency in simulations, and 2.37x~9.07x better performance in experiments.