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A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models

Leonard Apanasevich, Yogesh Kale, Himanshu Sharma, Ana Marija Sokovic
University of Illinois Chicago, USA
27th Annual IEEE High Performance Extreme Computing Virtual Conference, 2023

@article{apanasevich2023comparison,

   title={A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models},

   author={Apanasevich, Leonard and Kale, Yogesh and Sharma, Himanshu and Sokovic, Ana Marija},

   year={2023}

}

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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 world’s 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 250X GPUs and is currently the only true exascale computer in the world. In the near future, another exascale system, Aurora, equipped with Intel Delta GPUs, is expected to come online at Argonne National Laboratory in Illinois. As the use of different GPU architectures becomes more prevalent in today’s supercomputing centers, it is becoming crucial to have a programming model that could support different platforms without the need for separate codebases (Pascuzzi, 2021). 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 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.
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