Tracking Nanostructural Evolution in Alloys: Large-Scale Analysis of Atom Probe Tomography Data on Blue Gene/L
Tracking Nanostructural Evolution in Alloys: Large-Scale Analysis of Atom Probe Tomography Data on Blue Gene/L
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追踪合金中的纳米结构演化：Blue Gene/L 上原子探针断层扫描数据的大规模分析
DOI：
10.1109/icpp.2008.73
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
S. Aluru
中科院分区：
文献类型：
--
作者：
S. Seal;M. Moody;A. Ceguerra;S. Ringer;K. Rajan;S. Aluru
局部电极原子探头(LEAP)层析成像技术的出现使材料的近原子尺度成像成为可能，从而使材料科学发生了革命性的变化。对三维原子探测断层扫描(APT)数据的分析有望将原子的组合排列与材料性质联系起来，从而能够更好地设计和合成复杂材料。现有的技术是连续的，需要对n个原子进行O(N2)操作，不能扩展到当代原子探针显微镜产生的数亿个大数据集。在本文中，我们提出了一种基于O(N)功自相关的技术，它揭示了组成原子的聚集性和它们之间的空间关联。我们提出了一种有效的并行化方法，并在1,024节点的Blue gene/L上进行了缩放。据我们所知，这是第一个用于分析APT数据的并行算法，与我们的线性工作自相关技术一起，很容易扩展到预计在不久的将来达到数十亿原子数据集的规模。
The advent of Local Electrode Atom Probe (LEAP) tomography is revolutionizing materials science by enabling near atomic scale imaging of materials. Analysis of three-dimensional atom probe tomography (APT) data holds the promise of relating combinatorial arrangement of atoms to material properties and enable better design and synthesis of complex materials. Existing techniques, which are serial and require O(n2) work for n atoms, do not scale to the hundred million large data sets produced by current generation atom probe microscopes. In this paper, we present an O(n) work autocorrelation based technique that reveals clustering of constituent atoms and spatial associations between them. We present an efficient parallelization of this method and show scaling on a 1,024 node Blue Gene/L. To our knowledge, this is the first parallel algorithm for the analysis of APT data, and together with our linear work autocorrelation technique, is demonstrated to easily scale to billion atom data sets expected in the very near future.