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Global‐view coefficients: a data management solution for parallel quantum Monte Carlo applications
Author(s) -
Niu Qingpeng,
Dinan James,
Tirukkovalur Sravya,
Benali Anouar,
Kim Jeongnim,
Mitas Lubos,
Wagner Lucas,
Sadayappan P.
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3748
Subject(s) - computer science , scalability , monte carlo method , node (physics) , quantum monte carlo , quantum , data management , table (database) , distributed computing , parallel computing , computational science , mathematics , data mining , structural engineering , quantum mechanics , database , engineering , statistics , physics
Summary Quantum Monte Carlo (QMC) applications perform simulation with respect to an initial state of the quantum mechanical system, which is often captured by using a cubic B‐spline basis. This representation is stored as a read‐only table of coefficients and accesses to the table are generated at random as part of the Monte Carlo simulation. Current QMC applications, such as QWalk and QMCPACK, replicate this table at every process or node, which limits scalability because increasing the number of processors does not enable larger systems to be run. We present a partitioned global address space approach to transparently managing this data using Global Arrays in a manner that allows the memory of multiple nodes to be aggregated. We develop an automated data management system that significantly reduces communication overheads, enabling new capabilities for QMC codes. Experimental results with QWalk and QMCPACK demonstrate the effectiveness of the data management system. Copyright © 2016 John Wiley & Sons, Ltd.