Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels
arxiv(2024)
摘要
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.
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