Efficient SDH Computation In Molecular Simulations Data.
Efficient SDH Computation In Molecular Simulations Data.
复制标题
分子模拟数据中的高效 SDH 计算。
DOI：
10.1145/2382936.2383010
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Grupcev,Vladimir
中科院分区：
文献类型：
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作者：
Tu,Yi-Cheng;Chen,Shaoping;Pandit,Sagar;Kumar,Anand;Grupcev,Vladimir
大颗粒或分子模拟数据的分析是基础科学研究界不可或缺的一部分。它通常涉及计算功能，如粒子的点对点相互作用。空间距离直方图（SDH）是科学发现中至关重要的计算方法之一。SDH常用于径向分布函数（RDF）的计算，采用朴素方法计算需要二次元时间。单纯的SDH计算甚至更昂贵，因为它是在一定的时间内连续计算来分析仿真系统。在本文中，我们研究了不同的基于树的SDH计算技术，并简要讨论了它们的性能。我们提出了不同的策略来提高这些技术的性能。具体来说，我们研究了基于密度图（DM）的SDH计算技术。DM本质上是将模拟空间划分为大小（体积）相等的单元（3D立方体）的网格，这可以通过增加四叉树（或Oct-tree）索引轻松实现。dm在各种配置中用于通过仿真系统的快照连续计算SDH。本文介绍了使用其中一些配置的性能改进。我们还讨论了利用图形处理单元（gpu）的计算能力计算SDH的影响。
Analysis of large particle or molecular simulation data is integral part of the basic-science research community. It often involves computing functions such as point-to-point interactions of particles. Spatial distance histogram (SDH) is one such vital computation in scientific discovery. SDH is frequently used to compute Radial Distribution Function (RDF), and it takes quadratic time to compute using naive approach. Naive SDH computation is even more expensive as it is computed continuously over certain period of time to analyze simulation systems.In this paper we look at different tree-based SDH computation techniques and briefly discuss about their performance. We present different strategies to improve the performance of these techniques. Specifically, we study thedensity map(DM) based SDH computation techniques. A DM is essentially a grid dividing simulated space into cells (3D cubes) of equal size (volume), which can be easily implemented by augmenting a Quad-tree (or Oct-tree) index. DMs are used in various configurations to compute SDH continuously over snapshots of the simulation system. The performance improvements using some of these configurations is presented in this paper. We also discuss the effect of utilizing computation power of Graphics Processing Units (GPUs) in computing SDH.