Sessions

XPU Support for the NAMD Molecular Dynamics Application with oneAPI

Abstract

Molecular dynamics (MD) provides an indispensable computational methodology for understanding biomolecular systems at atomic level detail, accessing spatial and temporal resolutions that are not available to purely experimental approaches. As computational power increases, MD techniques are ever more useful for tackling significant biomedically relevant challenges, such as illuminating details of the infection mechanism by the SARS-CoV-2 virus that causes COVID-19 and guiding computational approaches for the development of new pharmaceuticals and therapeutics. NAMD is an important MD application to the biomedical community, offering excellent performance on a range of platforms from desktop workstations up through the largest leadership class supercomputers. Although NAMD has been GPU-accelerated for more than a decade, it is important to introduce cross-platform support to leverage the capabilities of the upcoming DOE supercomputers, in particular, the Argonne Aurora supercomputer which is to be accelerated by Intel GPUs. This presentation discusses the ongoing NAMD development efforts to improve its cross-platform support by adopting oneAPI, which involves porting its many CUDA kernels to DPC++ with the Intel DPC++ Compatibility Tool and using the Intel VTune profiler to improve GPU utilization and overall performance of the new DPC++ kernels.

Speakers

Dr. David J. Hardy, Theoretical and Computational Biophysics Group, Beckman Institute, University of Illinois at Urbana-Champaign

Dr. Hardy received a B.S. in Mathematics and Computer Science in 1994 from Truman State University, an M.S. in Computer Science in 1997 from the Missouri University of Science and Technology, and a Ph.D. in Computer Science in 2006 from the University of Illinois at Urbana-Champaign. He is the lead developer of the NAMD parallel molecular dynamics application and is using the Intel oneAPI to prepare NAMD to run on the upcoming Aurora supercomputer at Argonne National Laboratory. His research interests include fast methods for calculating electrostatics, numerical methods for time integration, and GPU computing.