FPGAs-as-a-Service Toolkit (FaaST)
FPGAs-as-a-Service Toolkit (FaaST)
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DOI：
10.1109/h2rc51942.2020.00010
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
D. Rankin;J. Krupa;P. Harris;M. A. Flechas;B. Holzman;T. Klijnsma;K. Pedro;N. Tran;S. Hauck;Shih-Chieh Hsu;Matthew Trahms;Kelvin Lin;Yu Lou;Ta-Wei Ho;Javier Mauricio Duarte;Miaoyuan Liu
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文献类型：
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作者：
D. Rankin;J. Krupa;P. Harris;M. A. Flechas;B. Holzman;T. Klijnsma;K. Pedro;N. Tran;S. Hauck;Shih-Chieh Hsu;Matthew Trahms;Kelvin Lin;Yu Lou;Ta-Wei Ho;Javier Mauricio Duarte;Miaoyuan Liu
高能物理的计算需求已经很密集，预计在未来几年将急剧增加。在这种情况下，异构计算，特别是作为一个服务计算，有可能显着收益超过传统的计算模型。虽然以前在异构计算领域的研究和软件包都集中在GPU作为加速器上，但FPGA也是一个非常有前途的选择。开发了一系列工作流程来建立FPGA作为服务的性能。研究了用于高能物理的多种不同的设备和一系列算法。对于小型密集网络，吞吐量可以相对于GPU作为服务提高一个数量级。对于大型卷积网络，吞吐量与GPU作为服务相当。这项工作代表了第一个开源的FPGA即服务工具包。
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.