现场可编程门阵列和量子蒙特卡罗：用于可扩展高性能计算的节能协同处理
Field-Programmable Gate Arrays and Quantum Monte Carlo: Power Efficient Co-processing for Scalable High-Performance Computing
摘要：
大规模并行体系结构提供了相对于串行体系结构显著加速应用程序的潜力。然而，并不是所有的应用程序都表现出足够的数据和/或任务并行性来利用这样的平台。此外，与这些形式的计算相关联的功耗使得“向外扩展”以获得与现代可持续能源政策不相容的兆兆级性能。在这项工作中，我们调查的潜力，现场可编程门阵列（FPGA）的功能，在未来的亿级平台，以及他们的能力，以提高性能的每单位功率测量的科学计算的目的。我们把精力集中在变分蒙特卡罗上，并报告了与FPGA协同处理相对于纯多核系统的好处。
英文摘要：
Massively parallel architectures offer the potential to significantly accelerate an application relative to their serial counterparts. However, not all applications exhibit an adequate level of data and/or task parallelism to exploit such platforms. Furthermore, the power consumption associated with these forms of computation renders "scaling out" for exascale levels of performance incompatible with modern sustainable energy policies. In this work, we investigate the potential for field-programmable gate arrays (FPGAs) to feature in future exascale platforms, and their capacity to improve performance per unit power measurements for the purposes of scientific computing. We have focussed our efforts on Variational Monte Carlo, and report on the benefits of co-processing with an FPGA relative to a purely multicore system.
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