Variable intra-task threading for power-constrained performance and energy optimization in DAG scheduling

Abstract

Task-parallel programming models have alleviated the gap between software and hardware complexity in high-performance computing. However, the developer is still in charge of complex decisions that have a significant impact in the overall efficiency and affect the application development. Specifically, in a context in which a set of heterogeneous and interdependent tasks share resources, there is a complex interplay between different factors such as task granularity, task criticality, problem size, application inter-task and intra-task parallelism and available hardware concurrency. In this paper, we explore the effects of this mix from a static scheduling perspective, by exposing a mixed-integer linear program in which the amount of inter- and intra-task parallelism can be adapted as the execution evolves. We solve a set of instances simulating a dense Cholesky factorization on a 20-core Xeon multiprocessor in a power-constrained scenario targeting makespan and energy minimization. The model reveals performance gains up to 17.9% in terms of performance and 4.1% in terms of energy by discovering a set of high-quality scheduling solutions.