TY - GEN
T1 - Efficient, parallel at-scale correlation analysis for atom probe tomography on hybrid architectures
AU - Lu, Hao
AU - Seal, Sudip K.
AU - Muzyn, Gregory
AU - Guo, Wei
AU - Poplawsky, Jonathan D.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/3
Y1 - 2018/8/3
N2 - Atom probe tomography (APT) is a material probing technique that has undergone dramatic improvements in its capability to map individual atoms within a material sample resulting in data files with hundreds of millions of atoms. Understanding the nano-structural features hidden in these massive amounts of atomic data is a crucial analysis task for materials scientists. However, fast analysis capabilities for large APT workloads remains a critical bottleneck. In this paper, we present the design, implementation and detailed performance evaluations of a parallel software capable of efficiently performing extremely time-consuming correlation analyses of massive high density APT data. Starting with shared memory implementations to motivate our design choices, we extend the implementation to hybrid architectures keeping realistic APT workloads in mind. Detailed performance analyses of three different parallel implementations of the software are supported by empirical results on a Cray XC30 and a Cray XC40 architecture. Its usefulness is demonstrated by reducing the turnaround time of an end-To-end APT correlation analysis on 100 millions atoms by three orders of magnitude using 2048 MPI ranks on 1024 nodes (24 cores per node) of a Cray XC30. The software reported here equips material scientists for the first time with a high-speed scalable capability for efficient and timely analyses of massive APT data.
AB - Atom probe tomography (APT) is a material probing technique that has undergone dramatic improvements in its capability to map individual atoms within a material sample resulting in data files with hundreds of millions of atoms. Understanding the nano-structural features hidden in these massive amounts of atomic data is a crucial analysis task for materials scientists. However, fast analysis capabilities for large APT workloads remains a critical bottleneck. In this paper, we present the design, implementation and detailed performance evaluations of a parallel software capable of efficiently performing extremely time-consuming correlation analyses of massive high density APT data. Starting with shared memory implementations to motivate our design choices, we extend the implementation to hybrid architectures keeping realistic APT workloads in mind. Detailed performance analyses of three different parallel implementations of the software are supported by empirical results on a Cray XC30 and a Cray XC40 architecture. Its usefulness is demonstrated by reducing the turnaround time of an end-To-end APT correlation analysis on 100 millions atoms by three orders of magnitude using 2048 MPI ranks on 1024 nodes (24 cores per node) of a Cray XC30. The software reported here equips material scientists for the first time with a high-speed scalable capability for efficient and timely analyses of massive APT data.
KW - Atom probe tomography
KW - Hybrid architectures
KW - Kd trees
KW - Large scale data analysis
KW - Parallel algorithms
KW - Radial autocorrelation function
KW - Range trees
KW - Scalability analysis
KW - Spatial neighborhood queries
KW - Spherical region queries
UR - http://www.scopus.com/inward/record.url?scp=85052192317&partnerID=8YFLogxK
U2 - 10.1109/IPDPS.2018.00016
DO - 10.1109/IPDPS.2018.00016
M3 - Conference contribution
AN - SCOPUS:85052192317
SN - 9781538643686
T3 - Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
SP - 54
EP - 63
BT - Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018
Y2 - 21 May 2018 through 25 May 2018
ER -