We describe how a recently published algorithm, which addresses the sign problem within the context of the Green's function Monte Carlo method, can be implemented in a parallel distributed environment. The method of parallelization maintains large granularity and therefore low overhead. Despite the stochastic na ture of the algorithm, good load-balancing can be ac complished and reproducibility is ensured.
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References
1.
Ceperley, D.M., and Kalos, M.H.1979. In Monte Carlo methods in statistical physics, edited by K. Binder.New York : Springer Verlag.
2.
Kalos, M.H.1962. Phys. Rev.128:1791.
3.
Kalos, M.H.1967. J. Comp. Phys.2:257.
4.
Kalos, M.H.1989. In Computational atomic and nuclear physics, edited by C. Bottcher, M. R. Strayer, and J. B. McGrorySingapore : World Scientific.
5.
Kalos, M.H.1991. J. Stat. Phys.63:1269.
6.
Kalos, M.H., Levesque, D., and Verlet, L.1974. Phys. Rev. A 9:2178.
7.
Kalos, M.H., and Whitlock, P.A.1986. Monte Carlo methods. New York : Wiley.
8.
Kalos, M.H., and Zhang, S.1992. In Recent progress in many-body theories, vol. 3, edited by T. L. Ainsworth , C. E. Campbell, B. E. Clements, and E. Krotschek.New York: Plenum Press, p. 411.
9.
Percus, O., and Kalos, M.H.1989. J. Parallel Distributed Comput.6:477.
10.
Schmidt, K.E., and Kalos, M.H.1984. In Applications of the Monte Carlo method in statistical physics, edited by K. Binder.New York: Springer Verlag.
Umrigar, C.J., Wilson, K.G., and Wilkins, J.W.1988b. In Computer simulation studies in condensed matter physics: recent developments, edited by D. P. Landau , K. K. Mon, and H. B. Schuttler.New York: Springer Verlag.
13.
Zhang, S., and Kalos, M.H.1991. Phys. Rev. Lett.67:3074.
