High performance in silico virtual drug screening on many-core processors.

Simon McIntosh-Smith, James Price, Richard B Sessions, Amaurys A Ibarra
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引用次数: 89

Abstract

Drug screening is an important part of the drug development pipeline for the pharmaceutical industry. Traditional, lab-based methods are increasingly being augmented with computational methods, ranging from simple molecular similarity searches through more complex pharmacophore matching to more computationally intensive approaches, such as molecular docking. The latter simulates the binding of drug molecules to their targets, typically protein molecules. In this work, we describe BUDE, the Bristol University Docking Engine, which has been ported to the OpenCL industry standard parallel programming language in order to exploit the performance of modern many-core processors. Our highly optimized OpenCL implementation of BUDE sustains 1.43 TFLOP/s on a single Nvidia GTX 680 GPU, or 46% of peak performance. BUDE also exploits OpenCL to deliver effective performance portability across a broad spectrum of different computer architectures from different vendors, including GPUs from Nvidia and AMD, Intel's Xeon Phi and multi-core CPUs with SIMD instruction sets.

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在多核处理器上的高性能硅虚拟药物筛选。
药物筛选是制药行业药物开发管线的重要组成部分。传统的、基于实验室的方法正越来越多地被计算方法所增强,从简单的分子相似性搜索到更复杂的药效团匹配,再到计算更密集的方法,如分子对接。后者模拟药物分子与其靶标(通常是蛋白质分子)的结合。在这项工作中,我们描述了BUDE,布里斯托尔大学对接引擎,它已被移植到OpenCL行业标准并行编程语言,以利用现代多核处理器的性能。我们高度优化的OpenCL BUDE实现在单个Nvidia GTX 680 GPU上维持1.43 TFLOP/s,或峰值性能的46%。BUDE还利用OpenCL在不同厂商的不同计算机架构上提供有效的性能可移植性,包括Nvidia和AMD的gpu, Intel的Xeon Phi和带有SIMD指令集的多核cpu。
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