학술논문
Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture
Document Type
Periodical
Author
Source
Computing in Science & Engineering Comput. Sci. Eng. Computing in Science & Engineering. 23(2):114-119 Apr, 2021
Subject
Language
ISSN
1521-9615
1558-366X
1558-366X
Abstract
Our exploratory work finds that the SambaNova Reconfigurable Dataflow Architecture (RDA) along with the SambaFlow software stack provides for an attractive system and solution to accelerate AI for science workloads. We have observed the efficacy of using the system with a diverse set of science applications and reasoned their suitability for performance gains over traditional hardware. As the Data-Scale system provides for a very large memory capacity, the system can be used to train models that typically do not fit in a GPU. The architecture also provides for deeper integration with upcoming supercomputers at the Argonne Leadership Computing Facility (ALCF), a US Department of Energy Office of Science user facility, to help advance science insights.