Submitted by scrawford on
Title | Scalable Data Generation for Evaluating Mixed-Precision Solvers |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Luszczek, P., Y. Tsai, N. Lindquist, H. Anzt, and J. Dongarra |
Conference Name | 2020 IEEE High Performance Extreme Computing Conference (HPEC) |
Date Published | 2020-09 |
Publisher | IEEE |
Conference Location | Waltham, MA, USA |
Abstract | We present techniques of generating data for mixed precision solvers that allows to test those solvers in a scalable manner. Our techniques focus on mixed precision hardware and software where both the solver and the hardware can take advantage of mixing multiple floating precision formats. This allows taking advantage of recently released generation of hardware platforms that focus on ML and DNN workloads but can also be utilized for HPC applications if a new breed of algorithms is combined with the custom floating-point formats to deliver performance levels beyond the standard IEEE data types while delivering a comparable accuracy of the results. |
DOI | 10.1109/HPEC43674.2020.9286145 |