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Celeritas: Using GPUs to accelerate HEP detector simulation

ORAL

Abstract

Celeritas is a new Monte Carlo (MC) detector simulation code that allows experiment workflows to achieve higher performance (events/s) and efficiency (events/W) than current High Energy Physics (HEP) MC tools by running on GPUs, which are increasingly common in high performance computing (HPC). The latest release of Celeritas implements standard electromagnetic (EM) physics for photons, electrons, and positrons on GPU and CPU. On the Perlmutter HPC system, an EM-only idealized tracker test problem shows a single Nvidia A100 GPU to have the same throughput as 166 AMD EPYC CPU cores and is estimated to be about 3 times more power efficient than CPU. To maximize adoption by experimental collaborations, Celeritas integrates easily into existing Geant4 applications, "offloading" EM particles to accelerate the simulation. We will review these capabilities and EM performance results, and introduce new optical physics simulation capabilities being added to Celeritas to support dark matter and neutrino experiments.

* OLCF: This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.SciDAC: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program.NERSC: This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP-0023868.

Presenters

  • Stefano C. Tognini

    Oak Ridge National Laboratory

Authors

  • Stefano C. Tognini

    Oak Ridge National Laboratory

  • Seth R Jonhson

    Oak Ridge National Laboratory

  • Elliot D Biondo

    Oak Ridge National Laboratory

  • Philippe Canal

    Fermi National Accelerator Laboratory

  • Julien Esseiva

    Lawrence Berkeley National Laboratory

  • Thomas M Evans

    Oak Ridge National Laboratory

  • Hayden Hollenbeck

    Virginia Tech

  • Soon Yung Jun

    Fermi National Accelerator Laboratory

  • Guilherme Lima

    Fermi National Accelerator Laboratory

  • Amanda L Lund

    Argonne National Laboratory

  • Benjamin Morgan

    University of Warwick