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SHF: Small: Collaborative Research: Coupling Computation and Communication in FPGA-Enhanced Clouds and Clusters

SHF: Small: Collaborative Research: Coupling Computation and Communication in FPGA-Enhanced Clouds and Clusters
SHF:小型:协作研究:FPGA 增强型云和集群中的耦合计算和通信
批准号:
1618303
负责人:
Martin Herbordt
金额:
$22.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-06-15 至 2020-05-31

项目摘要

项目成果

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中文摘要
翻译
现场可编程门阵列(fpga)的引入加速了数据中心和云中的服务器集群,为在高端计算中利用新技术提供了一个巨大的、直接的机会。fpga具有灵活的逻辑和原生的大规模通信能力,是后摩尔时代高性能计算的理想选择。这是法律世界。由于硬件适应应用,可以实现更高的效率,并且由于fpga是混合通信/计算处理器,它们可以直接连接芯片到芯片。因此,大规模通信可以以更高的带宽、更低的延迟和更少的处理器影响进行。这些特性对于在当前水平之上提高性能至关重要。所建议的设计允许在数据在网络中传输时进行有用的处理,从而减少并行中间件的软件开销并减少网络拥塞。该研究的关键原则是实现应用程序的可编程、智能加速,同时强调在低延迟下通信和计算的重叠,同时也大幅削减软件开销。研究项目FC5(用于云和集群中耦合通信和计算的FPGA框架)有几个重点。首先,研究了FC5的硬件支持,并研究了FC5的可配置性方法,以减少通信延迟和支持网络计算。第二个结果是MPI-3.1并行中间件的Open MPI开源版本的原型版本,它利用FC5来提供涉及服务器之间和服务器内部的数据移动、数学数据减少和批量数据重组的特性和性能增强。第三,多个FC5软件模型的概念验证版本,包括直接硬件访问、透明的opencl中的mpi和基于api的暴露基本功能的机制。最后,由于FC5正在快速发展,预计即将发布重要的新公告,因此持续的改进是必不可少的。至少两个模型应用程序,分子动力学和Map-Reduce,将被用作测试用例。随着计算服务不断整合到云中,潜在的更广泛的影响是增加并行应用程序的规模和可用性。商业、政府和学术应用程序对云和集群计算的广泛使用意味着所提供的加速将对许多部门产生广泛的影响。在工业中对高性能计算(例如,快速机器学习)的日益接受是一个特殊的潜在商业领域,将通过该项目得到加强。
英文摘要
The introduction of Field Programmable Gate Arrays (FPGAs) to accelerate clusters of servers in datacenters and clouds provides a great, immediate opportunity to leverage a new technology in high-end computing. With their flexible logic and native massive communication capability, FPGAs are ideal for high-performance computing in the post-Moore?s Law world. Since the hardware adapts to the application higher efficiency can be achieved, and since FPGAs are hybrid communication/computation processors, they can be interconnected directly chip-to-chip. Large-scale communication can consequently proceed with both higher bandwidth, lower latency, and less processor impact. These features are crucial to enhancing performance beyond current levels. The proposed design allows for useful processing while data is in flight in the network resulting in reduced software overhead in parallel middleware and reduced network congestion. The key tenets of the research are to achieve programmable, intelligent acceleration of applications while emphasizing overlap of communication and computation at low latency, while also cutting substantially software overhead. The research project, FC5 (an FPGA framework for coupling communication and computation in clouds and clusters) has several thrusts. First, hardware support for FC5 and investigation of methods of configurability in FC5 to reduce communication latency and support computing in the network are studied. A second outcome is a prototype version of the Open MPI open source version of MPI-3.1 parallel middleware that utilizes FC5 to deliver the features and performance enhancements involving data movement between and within servers, mathematical data reductions, and bulk data reorganizations. Third, proof-of-concept versions of multiple FC5 software models, including direct hardware access, a transparent MPI-in-OpenCL, and an API-based mechanism that exposes essential functionality. Finally, because FC5 is evolving rapidly with major new announcements expected imminently, continued refinement is essential. At least two model applications, Molecular Dynamics and Map-Reduce, will be used as test cases. With the continued consolidation of computing services into the cloud, the potential broader impact is to increase both the scale and availability of parallel applications. The broad range of uses of cloud and cluster computing for commercial, government, and academic applications means that acceleration offered will have a widespread impact applicable across many sectors. The growing acceptance of high performance computing in industry (e.g., fast machine learning) is one particular potential commercial sector that will be enhanced by this project.
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Collaborative Research: EAGER: Real-time Strategies and Synchronized Time Distribution Mechanisms for Enhanced Exascale Performance-Portability and Predictability
  • 批准号:
    2151021
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  • 资助金额:
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  • 项目类别:
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  • 负责人:
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