Providing In-depth Performance Analysis for Heterogeneous Task-based Applications with StarVZ

Abstract

Task-based parallelism has adequately addressed the coding complexity required to fully exploit the processing power offered by omnipresent hybrid CPU/GPU supercomputers. However, its performance highly depends on the proper runtime system setup. Analyzing and tuning the performance of task-based applications running on hybrid platforms is challenging since they present unstructured communication and computation overlap, with finer granularity, dynamic scheduling, and inherent irregularity. This paper discusses the StarVZ approach to enable a comprehensive performance analysis in such a heterogeneous context. StarVZ is built on top of modern data analysis tools and is publicly available as an R package. We collect traces from five diverse task-based applications running on top of the StarPU runtime system on a set of multi-node platforms enhanced with GPUs. We demonstrate how it can highlight disturbances that are particularly hard to identify or explain with traditional analysis tools. Additionally, we provide a detailed performance evaluation of StarVZ with different workloads and setups.