High Performance in the Cloud with FPGA Groups

Anca Iordache, G. Pierre, P. Sanders, J. Coutinho, Mark Stillwell
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引用次数: 29

Abstract

Field-programmable gate arrays (FPGAs) can offer invaluable computational performance for many compute-intensive algorithms. However, to justify their purchase and administration costs it is necessary to maximize resource utilization over their expected lifetime. Making FPGAs available in a cloud environment would make them attractive to new types of users and applications and help democratize this increasingly popular technology. However, there currently exists no satisfactory technique for offering FPGAs as cloud resources and sharing them between multiple tenants. We propose FPGA groups, which are seen by their clients as a single virtual FPGA, and which aggregate the computational power of multiple physical FPGAs. FPGA groups are elastic, and they may be shared among multiple tenants. We present an autoscaling algorithm to maximize FPGA groups' resource utilization and reduce user-perceived computation latencies. FPGA groups incur a low overhead in the order of 0.09ms per submitted task. When faced with a challenging workload, the autoscaling algorithm increases resource utilization from 52% to 61% compared to a static resource allocation, while reducing task execution latencies by 61%.
使用FPGA组实现云中的高性能
现场可编程门阵列(fpga)可以为许多计算密集型算法提供宝贵的计算性能。然而,为了证明它们的购买和管理成本是合理的,有必要在它们的预期生命周期内最大限度地利用资源。使fpga在云环境中可用将使它们对新型用户和应用程序具有吸引力,并有助于使这种日益流行的技术民主化。然而,目前还没有令人满意的技术来提供fpga作为云资源并在多个租户之间共享它们。我们提出FPGA组,它们被客户视为单个虚拟FPGA,并聚集了多个物理FPGA的计算能力。FPGA组是弹性的,可以在多个租户之间共享。我们提出了一种自动缩放算法,以最大限度地提高FPGA组的资源利用率,并减少用户感知的计算延迟。FPGA组的开销很低,每个提交的任务大约为0.09ms。当面对具有挑战性的工作负载时,与静态资源分配相比,自动缩放算法将资源利用率从52%提高到61%,同时将任务执行延迟减少61%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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