Random number generators for massively parallel simulations on GPU

Abstract

High-performance streams of (pseudo) random numbers are crucial for the efficient implementation for countless stochastic algorithms, most importantly, Monte Carlo simulations and molecular dynamics simulations with stochastic thermostats. A number of implementations of random number generators has been discussed for GPU platforms before and some generators are even included in the CUDA supporting libraries. Nevertheless, not all of these generators are well suited for highly parallel applications where each thread requires its own generator instance. For this specific situation encountered, for instance, in simulations of lattice models, most of the high-quality generators with large states such as Mersenne twister cannot be used efficiently without substantial changes. We provide a broad review of existing CUDA variants of random-number generators and present the CUDA implementation of a new massively parallel high-quality, high-performance generator with a small memory load overhead.

to appear in Eur. Phys. J. Spec. Top. issue "Computer simulations on GPU"

References in corpus (12)

• A Portable High-Quality Random Number Generator for Lattice Field Theory Simulations
• Harvesting graphics power for MD simulations
• Extremely large scale simulation of a Kardar-Parisi-Zhang model using graphics cards
• A practical guide to computer simulations
• Performance potential for simulating spin models on GPU
• Simulating spin systems on IANUS, an FPGA-based computer
• Simulating spin models on GPU
• Phase diagram and critical behavior of the square-lattice Ising model with competing nearest- and next-nearest-neighbor interactions
• Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units
• q-State Potts model metastability study using optimized GPU-based Monte Carlo algorithms
• Connected component identification and cluster update on GPU
• Optimized GPU simulation of continuous-spin glass models

Cited by in corpus (11)

• GPU accelerated population annealing algorithm
• GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model
• PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs
• Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU
• Massively parallel multicanonical simulations
• Optimized GPU simulation of continuous-spin glass models
• Highly optimized simulations on single- and multi-GPU systems of 3D Ising spin glass
• Ageing at the Spin-Glass/Ferromagnet Transition: Monte Carlo Simulation using GPUs
• Bayesian naturalness, simplicity, and testability applied to the $B-L$ MSSM GUT
• Dynamical heterogeneities as fingerprints of a backbone structure in Potts models
• Massively parallel simulations for disordered systems