Task Scheduling for Heterogeneous Multicore Systems

TL;DR

This paper formulates and solves the optimal task scheduling problem for heterogeneous multicore systems, introducing policies that significantly improve performance and energy efficiency over traditional methods.

Contribution

It presents a mathematical optimization framework for task scheduling in heterogeneous systems, with analytical solutions for two processor types and a heuristic for multiple types.

Findings

Optimal policies outperform classic load-balancing by up to 2.24x in performance.

Proposed policies achieve up to 2.26x better energy efficiency.

Experimental results show up to 9.07x performance improvement on real platforms.

Abstract

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 workloads can typically run most efficiently on certain types of cores, mapping tasks on the best available resources can not only save energy but also deliver high performance. However, optimal task scheduling for performance and/or energy is yet to be solved for heterogeneous platforms. The work 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 two processor types, e.g., CPU+GPU, and give an optimal policy (CAB). We design the GrIn heuristic to efficiently solve for near-optimal policy for any number of processor types (within 1.6% of the optimal). Both policies work 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 efficiency in simulations, and 2.37x~9.07x better performance in experiments.