Workshop:H2RC 2020: Sixth International Workshop on Heterogeneous High-Performance Reconfigurable Computing
Authors: Dylan Rankin, Jeffrey Krupa, and Philip Harris (Massachusetts Institute of Technology (MIT)); Maria Acosta, Burt Holzman, Thomas Klijnsma, Kevin Pedro, and Nhan Tran (Fermi National Accelerator Laboratory); Scott Hauck, Shih-Chieh Hsu, Matthew Trahms, Kelvin Lin, and Yu Lou (University of Washington); Ta-Wei Ho (National Tsing Hua University, Taiwan); Javier Duarte (University of California, San Diego); and Mia Liu (Purdue University)
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.