Hap: A heterogeneity-conscious runtime system for adaptive pipeline parallelism

Abstract

Heterogeneous multiprocessing (HMP) is a promising solution for energy-efficient computing. While pipeline parallelism is an effective technique to accelerate various workloads (e.g., streaming), relatively little work has been done to investigate efficient runtime support for adaptive pipeline parallelism in the context of HMP. To bridge this gap, we propose a heterogeneity-conscious runtime system for adaptive pipeline parallelism (HAP). HAP dynamically controls the full HMP system resources to improve the energy efficiency of the target pipeline application. We demonstrate that HAP achieves significant energyefficiency gains over the Linux HMP scheduler and a state-of-the-art runtime system and incurs a low performance overhead.