scholarly journals CLUSTER VS. SINGLE-SPIN ALGORITHMS—WHICH ARE MORE EFFICIENT?

1994 ◽  
Vol 05 (01) ◽  
pp. 1-14 ◽  
Author(s):  
N. ITO ◽  
G.A. KOHRING
Keyword(s):  
Ising Model ◽  
Critical Exponents ◽  
Cluster Algorithm ◽  
Computer Time ◽  
System Size ◽  
Statistical Accuracy ◽  
Single Spin ◽  
Cluster Algorithms ◽  
Vector Computers ◽  
Single Cluster

A comparison between single-cluster and single-spin algorithms is made for the Ising model in 2 and 3 dimensions. We compare the amount of computer time needed to achieve a given level of statistical accuracy, rather than the speed in terms of site updates per second or the dynamical critical exponents. Our main result is that the cluster algorithms become more efficient when the system size, Ld, exceeds, L~70–300 for d=2 and l~80–200 for d=3. The exact value of the crossover is dependent upon the computer being used. The lower end of the crossover range is typical of workstations while the higher end is typical of vector computers. Hence, even for workstations, the system sizes needed for efficient use of the cluster algorithm is relatively large.

scholarly journals CRITICAL DYNAMICS OF TWO-REPLICA CLUSTER ALGORITHMS

2001 ◽  
Vol 12 (02) ◽  
pp. 257-271
Author(s):  
X.-N. LI ◽  
J. MACHTA
Keyword(s):  
Ising Model ◽  
Critical Behavior ◽  
Cluster Algorithm ◽  
The Other ◽  
Square Lattice ◽  
Critical Dynamics ◽  
Two Dimensional ◽  
Cluster Algorithms ◽  
Dynamic Exponent

The dynamic critical behavior of the two-replica cluster algorithm is studied. Several versions of the algorithm are applied to the two-dimensional, square lattice Ising model with a staggered field. The dynamic exponent for the full algorithm is found to be less than 0.4. It is found that odd translations of one replica with respect to the other together with global flips are essential for obtaining a small value of the dynamic exponent.

Critical Dynamics Behavior of the Wolff Algorithm in the Site-Bond-Correlated Ising Model

1998 ◽  
Vol 09 (06) ◽  
pp. 861-865
Author(s):  
P. R. A. Campos ◽  
R. N. Onody
Keyword(s):  
Ising Model ◽  
Cluster Algorithm ◽  
Dynamical Behavior ◽  
Critical Dynamics ◽  
Wolff Algorithm ◽  
Autocorrelation Time ◽  
Dynamical Exponents ◽  
Single Cluster

Here we apply the Wolff single-cluster algorithm to the site-bond-correlated Ising model and study its critical dynamical behavior. We have verified that the autocorrelation time diminishes in the presence of dilution and correlation, showing that the Wolff algorithm performs even better in such situations. The critical dynamical exponents are also estimated.

scholarly journals Transition matrix cluster algorithms

2019 ◽  
Vol 30 (01) ◽  
pp. 1950004 ◽  
Author(s):  
David Yevick ◽  
Yong Hwan Lee
Keyword(s):  
Ising Model ◽  
Density Of States ◽  
Matrix Method ◽  
Transition Matrix ◽  
Detailed Balance ◽  
Accurate Method ◽  
Single Spin ◽  
Cluster Algorithms ◽  
Global Cluster ◽  
Model Transition

We demonstrate that a series of procedures for increasing the efficiency of transition matrix calculations can be realized by integrating the standard single-spin flip transition matrix method with global cluster flipping techniques. Our calculations employ a simple and accurate method based on detailed balance for computing the density of states from the Ising model transition matrix.

scholarly journals Single-cluster algorithm for the site-bond-correlated Ising model

1997 ◽  
Vol 56 (22) ◽  
pp. 14529-14532 ◽  
Author(s):  
P. R. A. Campos ◽  
R. N. Onody
Keyword(s):  
Ising Model ◽  
Cluster Algorithm ◽  
Single Cluster

PARALLEL WOLFF CLUSTER ALGORITHMS

1995 ◽  
Vol 06 (02) ◽  
pp. 197-210 ◽  
Author(s):  
S. BAE ◽  
S.H. KO ◽  
P.D. CODDINGTON
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Efficient Method ◽  
Cluster Algorithm ◽  
Parallel Implementation ◽  
Parallel Computer ◽  
Spin Models ◽  
Cluster Algorithms ◽  
Vector Machines ◽  
Single Cluster

The Wolff single-cluster algorithm is the most efficient method known for Monte Carlo simulation of many spin models. Due to the irregular size, shape and position of the Wolff clusters, this method does not easily lend itself to efficient parallel implementation, so that simulations using this method have thus far been confined to workstations and vector machines. Here we present two parallel implementations of this algorithm, and show that one gives fairly good performance on a MIMD parallel computer.

scholarly journals Parallelization of the Wolff single-cluster algorithm

2010 ◽  
Vol 81 (2) ◽  
Author(s):  
J. Kaupužs ◽  
J. Rimšāns ◽  
R. V. N. Melnik
Keyword(s):  
Cluster Algorithm ◽  
Single Cluster

Separating Phages From Other Virus Families and Classifying the Different Phage Families By GI-Clusters

2021 ◽  
Author(s):  
Xingang Jia ◽  
Qiuhong Han ◽  
Zuhong Lu
Keyword(s):  
Test Data ◽  
Cluster Algorithm ◽  
Nearest Neighbors ◽  
Euclidean Algorithm ◽  
Training Data ◽  
Data Sets ◽  
Maximum Element ◽  
Clustering Techniques ◽  
Cluster Algorithms ◽  
Biological Entities

Abstract Background: Phages are the most abundant biological entities, but the commonly used clustering techniques are difficult to separate them from other virus families and classify the different phage families together.Results: This work uses GI-clusters to separate phages from other virus families and classify the different phage families, where GI-clusters are constructed by GI-features, GI-features are constructed by the togetherness with F-features, training data, MG-Euclidean and Icc-cluster algorithms, F-features are the frequencies of multiple-nucleotides that are generated from genomes of viruses, MG-Euclidean algorithm is able to put the nearest neighbors in the same mini-groups, and Icc-cluster algorithm put the distant samples to the different mini-clusters. For these viruses that the maximum element of their GI-features are in the same locations, they are put to the same GI-clusters, where the families of viruses in test data are identified by GI-clusters, and the families of GI-clusters are defined by viruses of training data.Conclusions: From analysis of 4 data sets that are constructed by the different family viruses, we demonstrate that GI-clusters are able to separate phages from other virus families, correctly classify the different phage families, and correctly predict the families of these unknown phages also.

Sign in / Sign up

Export Citation Format

Share Document