Visualization Aided Performance Tuning of Irregular Task-Parallel Computations

Abstract

This paper deals with a visualization-based approach to performance analyzing and tuning of highly irregular task-parallel applications. At its core lies a novel automatic layout algorithm for execution graphs which is based on Sugiyama's framework. Our visualization enables the application designer to reliably detect manifestations of parallel overhead and to investigate on their individual root causes. We particularly focus on structural properties of task-parallel computations which are hard to detect in a more analytical way, for example, false sharing and false parallelism. In addition, we discuss embedding our visualization into an integrated development environment, realizing a seamless work-flow for implementation, execution, analysis, and tuning of parallel programs.