The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Reo Nagashima (Meiji University), Akeru Nakamura (Meiji University), Kai Murakami (Meiji University), Ryunosuke Matsuzaki (Meiji University), Daichi Mukunoki (Nagoya University), Takaaki Miyajima (Meiji University)
Supervisor:
Abstract: Recently, there have been attempts to utilize AI accelerators for scientific computing; however, these devices generally lack hardware support for double-precision floating-point arithmetic, which is essential for many scientific applications.
The Cerebras CS-2 system (CS-2) delivers extremely high single-precision performance of 1.06 PFlops/s but does not support native double-precision arithmetic. To overcome this limitation and enable scientific computations requiring double precision, a software-based approach is essential.
We propose csDF, a double-float (DF) arithmetic library for the CS-2 that provides DF numeric types and arithmetic operations. To demonstrate the capability of csDF, we implemented a naive pseudo-double-precision matrix multiplication using DF addition and multiplication, and measured its strong scaling performance. Our result shows 8.09 Tera DF-Flops/s, which shows the feasibility of software-based double-precision arithmetic and enables previously infeasible scientific computations.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF