The Performance Implication of Task Size for Applications on the HPX Runtime System

As High Performance Computing moves toward Exascale, where parallel applications will be expected to run on millions of cores concurrently, every component of the computational model must perform optimally. One such component, the task scheduler, can potentially be optimized to runtime application requirements. We focus our study using a task-based runtime system, one possible solution towards Exascale computation. Based on task size and scheduler, the overheads associated with task scheduling vary. Therefore, to minimize overheads and optimize performance, either the task size or the scheduler must adapt. In this paper, we focus on adapting the task size, which can be easily done statically and potentially done dynamically. To this end, we first show how scheduling overheads change with task size or granularity. We then propose and execute a methodology to characterize these overheads and dynamically measure the effects of task granularity. The HPX runtime system [1] employs asynchronous fine-grained task scheduling and incorporates a dynamic performance modeling capability, providing an ideal experimental platform. Using the performance counter capabilities in HPX, we characterize task scheduling overheads and show metrics to determine optimal task size. This is the first step toward the goal of dynamically adapting task size to optimize parallel performance.