Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Conference · Thu Jan 25 00:00:00 EST 2024

OSTI ID:2283705

Kwok, Ka Martin  ^[1]; Kortelainen, Matti ^[1]; Cerati, Giuseppe ^[1]; Strelchenko, Alexei ^[1]; Gutsche, Oliver ^[1]; Reinsvold Hall, Allison ^[2]; Lantz, Steve ^[3]; Reid, Michael ^[3]; Riley, Daniel ^[3]; Berkman, Sophie ^[1]; Lee, Seyong ^[4]; Ather, Hammad ^[5]; Norris, Boyana ^[5]; Wang, Cong ^[1]

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1. Fermilab
2. Naval Academy, Annapolis
3. Cornell U.
4. Oak Ridge
5. Oregon U.

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par and Alpaka, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 2283705
• Report Number(s): FERMILAB-CONF-23-535-CMS-CSAID; arXiv:2401.14221; oai:inspirehep.net:2751450
• Country of Publication: United States
• Language: English