Communication-Aware Task Partition and Voltage Scaling for Energy Minimization on Heterogeneous Parallel Systems

Abstract

Heterogeneous parallel systems have become popular in general purpose computing and even high performance computing fields. There are many studies focused on harnessing heterogeneous parallel processing for better performance. However the energy optimization for heterogeneous system has not been well studied. Owing to the differences in performance and energy consumption, the energy optimization technique for heterogeneous system is different from the existing methods designed for homogeneous system. Besides typical voltage scaling method, reasonable task partitioning is also an essential method for optimizing energy consumption on heterogeneous systems. Through partitioning a data parallel task and mapping sub-tasks onto several processors, one could achieve better performance and reduced energy consumption. As the computation cost reduces with specific accelerators, the communication overhead becomes more prominent. Therefore, the task partition optimization should holistically consider the computation improvement and communication overhead to achieve higher energy efficiency. Typically, task partition and voltage scaling are not orthogonal and influence the effect of each other in the energy optimization problem. In order to harness both two knobs efficiently, this paper proposes an integer linear programming (ILP) based energy-optimal solution designed for heterogeneous system. We present a case study of optimizing MGRID benchmark on a typical CPU-GPU heterogeneous system. The experimental results demonstrate that the proposed method could exploit the heterogeneity in different processors and achieve improved energy efficiency.