Experience with the alpaka performance portability library in the CMS software

¹ CERN, Geneva, Switzerland
² Rice University, Houston, Texas, USA
³ UCSD, San Diego, California, USA
⁴ MIT, Cambridge, Massachusetts, USA
⁵ University of Benin, Benin City, Nigeria
⁶ Fermilab, Batavia, Illinois, USA
⁷ RWTH Aachen University, Aachen, Germany
⁸ Vilnius University, Vilnius, Lithuania

^* Corresponding author e-mail: andrea.bocci@cern.ch
^** M. J. Kortelainen is supported by FermiForward Discovery Group, LLC under Contract No. 89243024CSC000002 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.
^*** This work has been sponsored by the Wolfgang Gentner Programme of the German Federal Ministry of Education and Research (grant no. 13E18CHA)

Published online: 7 October 2025

Abstract

To achieve better computational efficiency and exploit a wider range of computing resources, the CMS software framework (CMSSW) has been extended to offload part of the physics reconstruction to NVIDIA GPUs. To support additional back-ends, as well to avoid the need to write, validate and maintain a separate implementation of the reconstruction algorithms for each back-end, CMS has adopted the alpaka performance portability library.

Alpaka (Abstraction Library for Parallel Kernel Acceleration) is a header-only C++ library that provides performance portability across different back-ends, abstracting the underlying levels of parallelism. It supports serial and parallel execution on CPUs, and extremely parallel execution on NVIDIA, AMD and Intel GPUs.

This contribution will show how alpaka is used in the CMS software to develop and maintain a single code base; to use different toolchains to build the code for each supported back-end, and link them into a single application; to seamlessly select the best backend at runtime, and implement portable reconstruction algorithms that run efficiently on CPUs and GPUs from different vendors. It will describe the validation and deployment of the alpaka-based implementation in the CMS High Level Trigger, and highlight how it achieves near-native performance.