用数据流引擎加速分子力学中感应偶极子的计算

F. Pratas, D. Oriato, O. Pell, R. Mata, L. Sousa
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引用次数: 12

摘要

在分子力学模拟中,静电的处理是计算量最大的任务。现代力场,如阿米巴,包含明显的极化效应,对计算的要求特别高。我们提出了一种用于加速极化力场的静态数据流架构。使用Maxeler的MaxCompiler获得的结果显示,与使用一半的数据流引擎容量的12核CPU节点相比,Maxeler 1U MaxNode的加速因子约为14倍。对全芯片实现的预测表明,每个节点的加速结果可达到29倍。此外,我们在Maxeler系统上的实现显示,与基于NVIDIA费米的gpu相比,改进了2.5到4倍。目前的工作显示了数据流引擎在加速这一领域应用方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating the Computation of Induced Dipoles for Molecular Mechanics with Dataflow Engines
In Molecular Mechanics simulations, the treatment of electrostatics is the most computational intensive task. Modern force fields, such as the AMOEBA, which include explicit polarization effects, are particularly computationally demanding. We propose a static dataflow architecture for accelerating polarizable force fields. Results, obtained with Maxeler's MaxCompiler, show a speed-up factor of about 14x on a Maxeler 1U MaxNode, when compared to a 12-core CPU node while using half of the dataflow engine capacity. Projections for a full chip implementation indicate that speed-up results of up to 29x per node can be reached. Moreover, our implementation on the Maxeler system shows improvements between 2.5x and 4x compared to NVIDIA Fermibased GPUs. The current work shows the potential of dataflow engines in accelerating this field of applications.
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