开发实现复杂异构计算应用程序的生产力

Anthony Milton, D. Kearney, S. Wong, S. Lemmo
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引用次数: 0

摘要

FPGA平台越来越多地面临着gpu和各种多核变体等众多并行计算架构的竞争。这些竞争对手的并行平台很有吸引力,因为它们涉及到基于软件的开发流程,从而提高了开发人员的生产力。虽然有人认为用传统硬件描述语言(hdl)编写的FPGA应用程序可能需要比多核CPU或GPU的相应并行软件开发(PSD)多一个数量级的开发时间,但有一些现代的硬件设计方法可以大大提高开发效率,并开始获得吸引力。在这项工作中采用的一种方法是使用高级HDL Bluespec。本文通过详细介绍一个项目的经验,比较了Bluespec FPGA与PSD在多核CPU和GPU上的开发,该项目涉及为每个平台开发复杂的多目标视觉跟踪算法的各个组件。我们发现,使用Bluespec的开发时间与CPU和GPU版本的联合开发时间具有竞争力,但Bluespec开发链的局限性(例如缺乏原生浮点支持)和FPGA设计的组件集成问题是FPGA平台的重大弱点。最后,我们展示了在这项工作中开发的视觉跟踪算法的各种实现的性能结果,并表明FPGA平台在可以克服该应用的实现问题时具有超过CPU和GPU平台性能的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development productivity in implementing a complex heterogeneous computing application
The FPGA platform is increasingly faced with a multitude of competitor parallel computing architectures such as GPUs and various multicore variants. These competitor parallel platforms are attractive because they involve a software based development flow, resulting in greater developer productivity. While it has been argued that FPGA applications written in traditional hardware description languages (HDLs) may require nearly an order of magnitude more development time than corresponding parallel software development (PSD) for multi-core CPU or GPU, there are modern approaches to hardware design that drastically increase development productivity that are beginning to gain traction. One approach adopted in this work is use of the high-level HDL Bluespec. This paper compares Bluespec FPGA development with PSD for multi-core CPU and GPU, by detailing the experiences of a project that involved developing various components of a complex multi-object visual tracking algorithm for each of these platforms. We found that the development time using Bluespec was competitive with the combined development time for the CPU and GPU versions, but that limitations with the Bluespec development chain (such as lack of native floating-point support) and component integration issues with the FPGA design were areas of significant weakness for the FPGA platform. Finally, we present performance results for the various implementations of the visual tracking algorithm developed in this work, and show that the FPGA platform has the potential to exceed the performance of the CPU and GPU platforms when implementation issues can be overcome for this application.
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