Speedup of Particle Transport Problems with a Beowulf Cluster

Abstract

The MCNP code is a general Monte Carlo N-Particle Transport program that is widely used in health physics, medical physics and nuclear engineering for problems involving neutron, photon and electron transport^[1]. However, due to the stochastic nature of the algorithms employed to solve the Boltzmann transport equation, MCNP generally exhibits a slow rate of convergence. In fact, engineers and scientists can quickly identify intractable versions of their most challenging and CPU-intensive problems. For example, despite the latest advancements in personal computers (PCs) and quantum leaps in their computational capabilities, an ordinary electron transport problem could require up to several CPU-days or even CPU-weeks on a typical desktop PC of today. One common contemporary approach to help address these performance limitations is by taking advantage of parallel processing. In fact, the very nature of the Monte Carlo approach embedded within MCNP is inherently parallel because, at least in principle, every particle history can potentially be tracked individually in an independent processor. In practice, however, there are many issues that must be confronted to achieve a reasonable level of parallelization. First, of course, a suitable parallel computing platform is required. Next, the computer program itself should exploit parallelism from within by combining such tools as Fortran-90 and PVM, for example. This article describes the installation and performance testing of the latest release of MCNP, Version 5 (MCNP5), compiled with PGI Fortran-90 and with PVM on a recently assembled 22-node Beowulf cluster that is now a dedicated platform for the faculty and students of the University of Cincinnati’s Nuclear and Radiological Engineering (UCNRE) Program. The performance of a neutron transport problem and that of a more challenging gamma-electron (coupled) problem are both highlighted. The results show that the PVM-compiled MCNP5 version with 20 tasks can execute roughly 12 times faster than the sequential version of the tested neutron transport problem, whereas another increasingly challenging gamma-electron (coupled) dose problem executed nearly 18 times faster than its serial counterpart, this performance on 20 processors is an impressive result relative to a linear (ideal) speedup.