Good random number generators are (not so) easy to find
Selected papers from the 2nd IMACS symposium on Mathematical modelling---2nd MATHMOD
Building an artificial brain using an FPGA based CAM-Brain machine
Applied Mathematics and Computation
Communications of the ACM
A Hardware Random Number Generator
CHES '02 Revised Papers from the 4th International Workshop on Cryptographic Hardware and Embedded Systems
Compact FPGA-based True and Pseudo Random Number Generators
FCCM '03 Proceedings of the 11th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
A Guide to Monte Carlo Simulations in Statistical Physics
A Guide to Monte Carlo Simulations in Statistical Physics
Fast multipole methods on graphics processors
Journal of Computational Physics
GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model
Journal of Computational Physics
Recent trends in the marketplace of high performance computing
Parallel Computing
Accelerating Matrix Operations with Improved Deeply Pipelined Vector Reduction
IEEE Transactions on Parallel and Distributed Systems
Performance potential for simulating spin models on GPU
Journal of Computational Physics
Implementation of the Longstaff and Schwartz American Option Pricing Model on FPGA
Journal of Signal Processing Systems
Hi-index | 31.45 |
Two-dimensional Ising lattices are simulated on a field programmable gate array (FPGA) based system. Multiple spins are updated at each FPGA clock, leading to a linear increase of simulation time with the lattice size. A hybrid random number generator is designed and shown to have a better statistical quality than the tested pseudorandom generators. For a 1024x1024 Ising lattice, speedups of 1518x over single CPU, 11.8x over single GPU, and 1.5x over previously reported FPGA based simulation systems are achieved. Simulations of 1024x1024 Ising models with sampling periods up to 4.2 million Monte Carlo sweeps (MCS) and total spin updates of 17.2 billion MCS are carried out to study autocorrelation effects at the transition temperature. The mean magnetization is shown to converge to a stable value when the sampling period is reaching 10^5 MCS, and the standard deviation of the mean is shown to be described better with an equation from Kikuchi and Ito.