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FPGAs-as-a-Service Toolkit (FaaST)

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arxiv 2010.08556 v1 pith:ALDGD56F submitted 2020-10-16 physics.comp-ph cs.DChep-exphysics.data-anphysics.ins-det

classification physics.comp-phcs.DChep-exphysics.data-anphysics.ins-det
keywords computinggpusserviceenergyfpgasfpgas-as-a-serviceheterogeneoushigh
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Track reconstruction as a service for collider physics

    physics.ins-det 2025-01 conditional novelty 4.0 of 10

    Running the Patatrack and Exa.TrkX tracking algorithms through NVIDIA Triton as a remote service gives near-local GPU throughput while letting one GPU serve many more CPU clients.

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