FPGAs-as-a-Service Toolkit (FaaST)
Abstract
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.
Cite
@article{arxiv.2010.08556,
title = {FPGAs-as-a-Service Toolkit (FaaST)},
author = {Dylan Sheldon Rankin and Jeffrey Krupa and Philip Harris and Maria Acosta Flechas and Burt Holzman and Thomas Klijnsma and Kevin Pedro and Nhan Tran and Scott Hauck and Shih-Chieh Hsu and Matthew Trahms and Kelvin Lin and Yu Lou and Ta-Wei Ho and Javier Duarte and Mia Liu},
journal= {arXiv preprint arXiv:2010.08556},
year = {2021}
}
Comments
10 pages, 7 figures, to appear in proceedings of the 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing