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).
[PDF]
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.
