dRMSD计算的加速和GPU缓存的有效使用

J. Filipovič, Jan Plhak, D. Střelák
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引用次数: 0

摘要

本文介绍了dRMSD算法的GPU加速,用于比较分子的不同结构。与多线程CPU实现相比,使用中端GPU,我们在集群方面的加速达到13.4倍,在I:I dRMSD计算方面的加速达到62.7倍。dRMSD计算暴露了强内存局部性,因此受计算约束。除了使用共享内存的保守实现外,我们还决定使用GPU缓存来实现算法的变体,以维护内存局部性。我们使用缓存的实现在Fermi和Maxwell上分别达到96.5%和91.6%的共享内存性能。我们已经确定了与计算绑定代码中的缓存阻塞相关的几个性能缺陷,并提出了改进性能的优化技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acceleration of dRMSD calculation and efficient usage of GPU caches
In this paper, we introduce the GPU acceleration of dRMSD algorithm, used to compare different structures of a molecule. Comparing to multithreaded CPU implementation, we have reached 13.4× speedup in clustering and 62.7× speedup in I:I dRMSD computation using mid-end GPU. The dRMSD computation exposes strong memory locality and thus is compute-bound. Along with conservative implementation using shared memory, we have decided to implement variants of the algorithm using GPU caches to maintain memory locality. Our implementation using cache reaches 96.5% and 91.6% of shared memory performance on Fermi and Maxwell, respectively. We have identified several performance pitfalls related to cache blocking in compute-bound codes and suggested optimization techniques to improve the performance.
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