A performance model of non-deterministic particle transport on large-scale systems

In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of a nondeterministic particle transport application, MCNP (Monte Carlo N-Particle), that represents part of the Advanced Simulation and Computing (ASC) workload. MCNP can be used for the simulation of neutron, photon, electron, or coupled transport, and has found uses in many problem areas including nuclear reactors, radiation shielding, and medical physics. Monte Carlo methods in general and MCNP specifically do not solve an explicit equation, but rather obtain answers by simulating the interactions between individual particles and a predefined geometry. This is in contrast to deterministic transport methods, the most common of which is the discrete ordinates method, that solve the transport equation directly for the average particle behavior. Previous studies on the scalability of parallel Monte Carlo calculations have been rather general in nature. The performance mod...

• Type: Journal
• Year: 2006
• Where: FGCS
• Authors: Mark M. Mathis, Darren J. Kerbyson, Adolfy Hoisie
• Keywords: FGCS 2006, Monte Carlo, Performance, Predictive Analytical Model