skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data

Abstract

Powered by recent advances in data acquisition technologies, today's state-of-the-art atom probe microscopes yield data sets with sizes ranging from a few million atoms to billions of atoms. Analysis of these atomic data sets within rea-sonable turnaround times is a pressing data analysis challenge for material scientists currently equipped with software systems that do not scale to these massive data sets. Here, we present the shared memory component of a larger ongoing effort to develop a multi-feature data analysis framework capable of analyzing atom probe data of all sizes and scales from desktop multicore machines to large-scale high-performance computing platforms with hybrid (shared and distributed memory) architectures. Our focus here is on a broad class of popular atom probe data analysis methods that rely on core time-consuming k-NN queries. We present a scalable, heuristic algorithm for k-NN queries using three-dimensional range trees. To demonstrate its efficacy, the k-NN algorithm is integrated with two use cases of atom probe data analysis methods and the resulting analysis times are shown to speedup by over 20X on a 32-core Cray XC40 node using workloads up to 8 million atoms, which is already beyond the at-scale capabilities of existing atom probe software. Using thismore » k-NN algorithm, we also introduce a novel parameter estimation method for a class of cluster finding methods, called friends-of-friends (FoF) methods, to completely bypass their expensive pre-processing steps. In each case, we validate the results on a variety of control data sets.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1651414
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 25TH IEEE International Conference on High Performance Computing (HiPC) - Bangalore, , India - 12/17/2018 10:00:00 AM-12/20/2018 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Lu, Hao, Seal, Sudip, and Poplawsky, Jonathan D. Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data. United States: N. p., 2018. Web. doi:10.1109/HiPC.2018.00034.
Lu, Hao, Seal, Sudip, & Poplawsky, Jonathan D. Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data. United States. doi:10.1109/HiPC.2018.00034.
Lu, Hao, Seal, Sudip, and Poplawsky, Jonathan D. Sat . "Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data". United States. doi:10.1109/HiPC.2018.00034. https://www.osti.gov/servlets/purl/1651414.
@article{osti_1651414,
title = {Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data},
author = {Lu, Hao and Seal, Sudip and Poplawsky, Jonathan D.},
abstractNote = {Powered by recent advances in data acquisition technologies, today's state-of-the-art atom probe microscopes yield data sets with sizes ranging from a few million atoms to billions of atoms. Analysis of these atomic data sets within rea-sonable turnaround times is a pressing data analysis challenge for material scientists currently equipped with software systems that do not scale to these massive data sets. Here, we present the shared memory component of a larger ongoing effort to develop a multi-feature data analysis framework capable of analyzing atom probe data of all sizes and scales from desktop multicore machines to large-scale high-performance computing platforms with hybrid (shared and distributed memory) architectures. Our focus here is on a broad class of popular atom probe data analysis methods that rely on core time-consuming k-NN queries. We present a scalable, heuristic algorithm for k-NN queries using three-dimensional range trees. To demonstrate its efficacy, the k-NN algorithm is integrated with two use cases of atom probe data analysis methods and the resulting analysis times are shown to speedup by over 20X on a 32-core Cray XC40 node using workloads up to 8 million atoms, which is already beyond the at-scale capabilities of existing atom probe software. Using this k-NN algorithm, we also introduce a novel parameter estimation method for a class of cluster finding methods, called friends-of-friends (FoF) methods, to completely bypass their expensive pre-processing steps. In each case, we validate the results on a variety of control data sets.},
doi = {10.1109/HiPC.2018.00034},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {12}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: