As we reach exascale, production High Performance Computing (HPC) systems are increasing in complexity. These systems now comprise multiple heterogeneous computing components (CPUs and GPUs) utilized through diverse, often vendor-specific programming models. As application developers and programming models experts develop higher-level, portable programming models for these systems, debugging and performance optimization requires understanding how multiple programming models stacked on top of each other interact with one another. This paper discusses THAPI (Tracing Heterogeneous APIs), a portable, programming model-centric tracing framework: by capturing comprehensive API call details across layers of the HPC software stack, THAPI enables fine-grained understanding and analysis of how applications interact with programming models and heterogeneous hardware. Leveraging state of the art tracing f ramework like the Linux Trace Toolkit Next Generation (LTTng) and tracing much more than other tracing toolkits, focused on function names and timestamps, this approach enables us to diagnose performance bottlenecks across the software stack, optimize application behavior, and debug programming model implementation issues.
@article{arxiv.2504.03683,
title = {THAPI: Tracing Heterogeneous APIs},
author = {Solomon Bekele and Aurelio Vivas and Thomas Applencourt and Servesh Muralidharan and Bryce Allen and Kazutomo Yoshiiinst and Swann Perarnau and Brice Videau},
journal= {arXiv preprint arXiv:2504.03683},
year = {2025}
}