Heterogeneous computing integrates diverse processing elements, such as CPUs, GPUs, and FPGAs, within a single system, aiming to leverage the strengths of each architecture to optimize performance and energy consumption. In this context, efficient performance analysis plays a critical role in determining the most suitable platform for dispatching tasks, ensuring that workloads are allocated to the processing units where they can execute most effectively. Adaptyst is a novel ongoing effort at CERN, with the aim to develop an open-source, architecture-agnostic performance analysis for scientific workloads. This study explores the performance and implementation complexity of two built-in eBPF-based methods such as Uprobes and USDT, with the aim of outlining a roadmap for future integration into Adaptyst and advancing toward heterogeneous performance analysis capabilities.
@article{arxiv.2511.13928,
title = {Enabling Heterogeneous Performance Analysis for Scientific Workloads},
author = {Maksymilian Graczyk and Vincent Desbiolles and Stefan Roiser and Andrea Guerrieri},
journal= {arXiv preprint arXiv:2511.13928},
year = {2025}
}
Comments
Accepted for publication as a short paper at IEEE HPEC'25 and got the Outstanding Short Paper Award there