  For many years, systems running Nvidia-based GPU architectures have dominated
the heterogeneous supercomputer landscape. However, recently GPU chipsets
manufactured by Intel and AMD have cut into this market and can now be found in
some of the worlds fastest supercomputers. The June 2023 edition of the TOP500
list of supercomputers ranks the Frontier supercomputer at the Oak Ridge
National Laboratory in Tennessee as the top system in the world. This system
features AMD Instinct 250 X GPUs and is currently the only true exascale
computer in the world.The first framework that enabled support for
heterogeneous platforms across multiple hardware vendors was OpenCL, in 2009.
Since then a number of frameworks have been developed to support vendor
agnostic heterogeneous environments including OpenMP, OpenCL, Kokkos, and SYCL.
SYCL, which combines the concepts of OpenCL with the flexibility of
single-source C++, is one of the more promising programming models for
heterogeneous computing devices. One key advantage of this framework is that it
provides a higher-level programming interface that abstracts away many of the
hardware details than the other frameworks. This makes SYCL easier to learn and
to maintain across multiple architectures and vendors. In n recent years, there
has been growing interest in using heterogeneous computing architectures to
accelerate molecular dynamics simulations. Some of the more popular molecular
dynamics simulations include Amber, NAMD, and Gromacs. However, to the best of
our knowledge, only Gromacs has been successfully ported to SYCL to date. In
this paper, we compare the performance of GROMACS compiled using the SYCL and
CUDA frameworks for a variety of standard GROMACS benchmarks. In addition, we
compare its performance across three different Nvidia GPU chipsets, P100, V100,
and A100.
