A new random-number generator for multispin Monte Carlo algorithms

1987 ◽  
Vol 48 (1-2) ◽  
pp. 135-149 ◽  
Author(s):  
L. Pierre ◽  
T. Giamarchi ◽  
H. J. Schulz
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Monte Carlo Algorithms

A Modification to the Monte Carlo Method—The Exodus Method

1968 ◽  
Vol 90 (3) ◽  
pp. 328-332 ◽  
Author(s):  
A. F. Emery ◽  
W. W. Carson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Calculation Time ◽  
The Monte Carlo Method

A modification to the Monte Carlo method is described which reduces calculation time and improves the accuracy. This method—termed “Exodus”—is not dependent upon a random number generator and may be applied to any problem which admits of a nodal network.

scholarly journals FULL QCD ON APE100 MACHINES

1995 ◽  
Vol 06 (01) ◽  
pp. 25-45
Author(s):  
STEFANO ANTONELLI ◽  
MARCO BELLACCI ◽  
ANDREA DONINI ◽  
RENATA SARNO
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Memory Capacity ◽  
Monte Carlo Algorithm ◽  
Number Generator ◽  
Inversion Algorithm ◽  
Hybrid Monte Carlo ◽  
Pure Gauge ◽  
Wilson Fermions

We present the first tests and results from a study of QCD with two flavours of dynamical Wilson fermions using the Hybrid Monte Carlo Algorithm (HMCA) on APE100 machines. The simulations have been performed on 64 lattice for the pure gauge HMCA and on 84, 123×32 lattices for full QCD configurations. We discuss the inversion algorithm for the fermionic operator, the methods used to overcome the problems arising using a 32 bit machine and the implementation of a new random number generator for APE100 machines. We propose different scenarios for the simulation of physical observables, with respect to the memory capacity and speed of different APE100 configurations.

Monte Carlo, Probability, Algebra, and Pi

1981 ◽  
Vol 74 (5) ◽  
pp. 335-339
Author(s):  
Duane C. Hinders
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Random Numbers ◽  
Number Generator ◽  
Monte Carlo Techniques

Monte Carlo techniques are those that use a random number generator to solve a problem. Often, but certainly not always, these are problems that might be difficult or even impossible to solve analytically. A random number generator can be a coin, a die or dice, a computer, a table of random numbers, or some other device.

scholarly journals ERRORS IN MONTE CARLO SIMULATIONS USING SHIFT REGISTER RANDOM NUMBER GENERATORS

1996 ◽  
Vol 06 (06) ◽  
pp. 781-787 ◽  
Author(s):  
F. SCHMID ◽  
N. B. WILDING
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Random Number ◽  
Random Number Generator ◽  
Shift Register ◽  
Magnetization Distribution ◽  
Number Generator ◽  
Random Number Generators ◽  
Single Spin ◽  
System Volume

We report large systematic errors in Monte Carlo simulations of the tricritical Blume–Capel model using single spin Metropolis updating. The error, manifest as a 20% asymmetry in the magnetization distribution, is traced to the interplay between strong triplet correlations in the shift register random number generator and the large tricritical clusters. The effect of these correlations is visible only when the system volume is a multiple of the random number generator lag parameter. No such effects are observed in related models.

scholarly journals PERSPECTIVES FOR MONTE CARLO SIMULATIONS ON THE CNN UNIVERSAL MACHINE

2006 ◽  
Vol 17 (06) ◽  
pp. 909-922 ◽  
Author(s):  
M. ERCSEY-RAVASZ ◽  
T. ROSKA ◽  
Z. NÉDA
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Lattice Models ◽  
Parallel Architecture ◽  
Cellular Neural Network ◽  
Stochastic Simulations ◽  
Number Generator ◽  
Percolation Problem ◽  
Near Future

Possibilities for performing stochastic simulations on the analog and fully parallelized Cellular Neural Network UniversalMachine (CNN-UM) are investigated. By using a chaotic cellular automaton perturbed with the natural noise of the CNN-UM chip, a realistic binary random number generator is built. As a specific example for Monte Carlo type simulations, we use this random number generator and a CNN template to study the classical site-percolation problem on the ACE16K chip. The study reveals that the analog and parallel architecture of the CNN-UM is very appropriate for stochastic simulations on lattice models. The natural trend for increasing the number of cells and local memories on the CNN-UM chip will definitely favor in the near future the CNN-UM architecture for such problems.

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