Activity-Driven Task Allocation in Energy-Constrained Heterogeneous GPUs Systems

Abstract

As computing systems continue to increase in complexity, energy optimization plays a key role in the design and implementation of heterogeneous systems. Although the energy consumed by off-chip memory accounts for a large proportion of the total power consumed by the system as a whole, current research on energy optimization mainly focuses on optimizing the energy consumed by the processors. This article explores the coordinated optimization of the holistic performance of the processors and memory system for heterogeneous systems with energy constraints. A communication–computing pipeline model for parallel executions is characterized to optimize program performance by simultaneously scaling the voltage and frequency of the processors and memory using task allocation strategies. A synergistic load-balancing optimization approach is presented to resolve the load imbalance among graphics processing units. Our experimental results substantiate the effectiveness of the approach in terms of execution times and throughputs with the energy constraints.