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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2014 Vol.6(3): 200-205 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2014.V6.862

Adaptive Computing Library for Quantum Monte Carlo Simulations

Zeki Bozkus, Ahmad Anbar, and Tarek El-Ghazawi
Abstract—Quantum Monte Carlo (QMC) methods are used in many scientific computer simulation as their core kernels. The implementation of QMC for distributed NUMA clusters may have load balancing issues at petascale level because of its random nature. We are studying on a simulation for inhomogeneous ultra-cold atoms on optical lattice, for which we developed a QMC algorithm with hybrid MPI+OpenMP programming model. This hybrid model uses the nested parallelism such that the outer loops are parallelized by MPI, while the inner loop relies on OpenMP parallelism. In this work, we presented an adaptive computing approach which learns the system work load dynamically by using our Adaptive Computing Library at run-time and then creates sufficient amount of OpenMP threads based on the availability of the system resources during the execution. The implementation shows that our adaptive approach can get very good load balancing without unnecessary overheads and can significantly provide performance increases up to 20% increases in comparison to MPI-only implementation on a XE6m Cray super computer.

Index Terms—Hybrid parallel programming, load balancing, QMC simulation.

The authors are with the Department of Electrical and Computer Engineering and the George Washington University (e-mail: {zbozkus, anbar, tarek}@gwu.edu).


Cite:Zeki Bozkus, Ahmad Anbar, and Tarek El-Ghazawi, "Adaptive Computing Library for Quantum Monte Carlo Simulations," International Journal of Computer Theory and Engineering vol. 6, no. 3, pp. 200-205, 2014.

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