Evaluating Performance Portability with the CMS Heterogeneous Pixel Reconstruction code

¹ Barcelona Supercomputing Center, Spain
² CERN, Geneva, Switzerland
³ INFN Bologna, Italy
⁴ Center for Advanced Systems Understanding (CASUS), Görlitz, Germany
⁵ University of Geneva, Switzerland
⁶ University of Bologna, Italy
⁷ Fermi National Accelerator Laboratory, Batavia, IL, USA
⁸ Institute of Technology and Higher Studies of Monterrey, Mexico
⁹ University of Milano Bicocca, Italy
¹⁰ RWTH Aachen University, Germany
¹¹ Argonne National Laboratory, Lemont, IL, USA
¹² Lawrence Berkeley National Laboratory, Berkeley, CA, USA

^* e-mail: matti@fnal.gov

Published online: 6 May 2024

Abstract

In the past years the landscape of tools for expressing parallel algorithms in a portable way across various compute accelerators has continued to evolve significantly. There are many technologies on the market that provide portability between CPU, GPUs from several vendors, and in some cases even FPGAs. These technologies include C++ libraries such as Alpaka and Kokkos, compiler directives such as OpenMP, the SYCL open specification that can be implemented as a library or in a compiler, and standard C++ where the compiler is solely responsible for the offloading. Given this developing landscape, users have to choose the technology that best fits their applications and constraints. For example, in the CMS experiment the experience so far in heterogeneous reconstruction algorithms suggests that the full application contains a large number of relatively short computational kernels and memory transfer operations. In this work we use a stand-alone version of the CMS heterogeneous pixel reconstruction code as a realistic use case of HEP reconstruction software that is capable of leveraging GPUs effectively. We summarize the experience of porting this code base from CUDA to Alpaka, Kokkos, SYCL, std::par, and OpenMP offloading. We compare the event processing throughput achieved by each version on NVIDIA and AMD GPUs as well as on a CPU, and compare those to what a native version of the code achieves on each platform.