An Alternative to Stride-Based RNG for Monte Carlo Transport

TL;DR

This paper introduces a hash-based pseudo-random number generator as an alternative to stride-based RNGs for Monte Carlo simulations, improving scalability and maintaining result reliability.

Contribution

It proposes a scalable hash-based RNG method that enhances parallelism and reproducibility in Monte Carlo transport simulations.

Findings

Hash-based RNG maintains tally normality similar to stride-based RNG.

The new method scales better with increased concurrency.

Reproducibility is achieved without compromising result quality.

Abstract

The techniques used to generate pseudo-random numbers for Monte Carlo (MC) applications bear many implications on the quality and speed of that programs work. As a random number generator (RNG) slows, the production of random numbers begins to dominate runtime. As RNG output grows in correlation, the final product becomes less reliable. These difficulties are further compounded by the need for reproducibility and parallelism. For reproducibility, the numbers generated to determine any outcome must be the same each time a simulation is run. However, the concurrency that comes with most parallelism introduces race conditions. To have both reproducibility and concurrency, separate RNG states must be tracked for each independently schedulable unit of simulation, forming independent random number streams. We propose an alternative to the stride-based parallel LCG seeding approach that scales more practically with increased concurrency and workload by generating seeds through hashing and allowing for repeated outputs. Data gathered from normality tests of tally results from simple MC transport benchmark calculations indicates that the proposed hash-based RNG does not significantly affect the tally result normality property as compared to the conventional stride-based RNG.