Tracking Nanostructural Evolution in Alloys: Large-Scale Analysis of Atom Probe Tomography Data on Blue Gene/L

Abstract

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.