Please visit Jefferson Lab Event Policies and Guidance before planning your next event:
May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

Porting ATLAS FastCaloSim to GPUs with OpenMP Target Offloading

Not scheduled
Hampton Roads Ballroom and Foyer Area (Norfolk Waterside Marriott)

Hampton Roads Ballroom and Foyer Area

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Poster Poster Poster Session


Mohammad Atif, FNU (Brookhaven National Laboratory)


OpenMP is a directive based shared-memory parallel programming model traditionally used for multicore CPUs. In its recent versions, OpenMP was extended to enable GPU computing via its “target
offloading” model. The architecture agnostic compiler directives can in principle offload to multiple types of GPUs and FPGAs, and its compiler support is under active development.

In this work, we investigate the performance of OpenMP’s GPU offloading capability by porting the ATLAS FastCaloSim code. FastCaloSim is a relatively self-contained parametrized calorimeter
simulation, and is used as a testbed for our investigations of different portable programming models. We find the OpenMP GPU offloading easy to implement and that it does not require major changes to the C++ code. However, the performance varies from compiler
to compiler and the specialized operations (e.g. atomic) are currently less performant than CUDA. We compare the performance with the existing CUDA port across hardware (NVIDIA, AMD) and compilers (LLVM Clang, AMD Clang, gcc, nvc)., SYCL) comparing them to the results obtained with native implementations of the FCS code in corresponding GPU programming languages.

Consider for long presentation No

Primary authors

Leggett, Charles (Lawrence Berkeley National Laboratory) Lin, Meifeng (Brookhaven National Laboratory) Dong, Zhihua (Brookhaven National Laboratory) Mohammad Atif, FNU (Brookhaven National Laboratory)

Presentation materials