Abstract
Parallel Tempering (PT), also known as Replica Exchange, is a powerful Markov Chain Monte Carlo sampling approach which aims at reducing the relaxation time in simulations of physical systems. In this paper, we present a novel implementation of PT, so-called decentralized replica exchange PT, using MPI and the Scalable Parallel Random Number Generators (SPRNG) libraries. By adjusting the replica exchange operations in the original PT algorithm, and taking advantage of the characteristics of pseudorandom number generators, this implementation minimizes the overhead caused by interprocessor communication in replica exchange in PT. This enables one to efficiently apply PT to large-scale massively parallel systems. The efficiency of this implementation has been demonstrated in the context of various benchmark energy functions, such as the high-dimensional Rosenbrock function, and a rugged funnel-like function.
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Li, Y., Mascagni, M., Gorin, A. (2007). Decentralized Replica Exchange Parallel Tempering: An Efficient Implementation of Parallel Tempering Using MPI and SPRNG. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_44
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DOI: https://doi.org/10.1007/978-3-540-74484-9_44
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