Presentation
Scaling the Memory Wall Using Mixed-Precision — HPG-MxP on an Exascale Machine
DescriptionMixed-precision algorithms have been proposed as a way for scientific computing to benefit from some of the gains seen for AI on recent high performance computing (HPC) platforms. A few applications dominated by dense matrix operations have seen substantial speedups by utilizing low-precision formats such as FP16. However, a majority of scientific simulation applications are memory bandwidth limited. Beyond preliminary studies, the practical gain from using mixed-precision algorithms on a given HPC system is largely unclear.
The High Performance GMRES Mixed Precision (HPG-MxP) benchmark has been proposed to measure the useful performance of an HPC system on sparse matrix-based mixed-precision applications. In this work, we present a highly optimized implementation of the HPG-MxP benchmark for an exascale system and describe our algorithm enhancements. We show for the first time a speedup of 1.6x using a combination of double and single precision on modern GPU-based supercomputers.
The High Performance GMRES Mixed Precision (HPG-MxP) benchmark has been proposed to measure the useful performance of an HPC system on sparse matrix-based mixed-precision applications. In this work, we present a highly optimized implementation of the HPG-MxP benchmark for an exascale system and describe our algorithm enhancements. We show for the first time a speedup of 1.6x using a combination of double and single precision on modern GPU-based supercomputers.
Event Type
Paper
TimeTuesday, 18 November 202510:52am - 11:15am CST
Location263-264
Performance Measurement, Modeling, & Tools