The IDEAS Productivity project, in partnership with the DOE Computing Facilities of the ALCF, OLCF, and NERSC and the DOE Exascale Computing Project (ECP) has resumed the webinar series on Best Practices for HPC Software Developers, which we began in 2016.
As part of this series, we offer one-hour webinars on topics in scientific software development and high-performance computing, approximately once a month. The next webinar is titled Refactoring EXAALT MD for Emerging Architectures, and will be presented Aidan Thompson (Sandia National Laboratories), Stan Moore (Sandia National Laboratories), and Rahulkumar Gayatri (NERSC). The webinar will take place on Wednesday, January 15, 2020 at 1:00 pm ET.
As part of the DOE Exascale Computing Project, members of the EXAALT project are working to increase the accuracy, time, and length scales of molecular dynamics simulations of materials for fusion energy. Simulations rely on the SNAP machine-learning interatomic potential to accurately capture material properties. The SNAP kernel recursively evaluates a set of complex polynomial functions, requiring many deeply nested loops with irregular loop bounds. Last year, a worrisome trend in the SNAP force kernel was identified. With each new generation of emerging architectures, performance relative to theoretical peak was decreasing, particularly on GPUs. This webinar will discuss the approach used to rewrite the SNAP kernel from the ground up, using more compact memory representation, refactoring the main loop, using sub-kernels to reduce pressure on GPU threads, and improving coalesced memory accesses on the GPU. This work has enabled a spectacular increase of roughly 10x in performance over the baseline implementation of the SNAP benchmark running on NVIDIA V100 GPUs. Extrapolated to the full machine, this predicts an increase of over 100x in the Figure of Merit over the baseline on the ALCF/Mira system, putting EXAALT on track to meeting, and even exceeding performance targets on exascale systems. The webinar will emphasize key strategies and lessons learned in code transitions for emerging architectures.