Improved CUDA programs for GPU computing of Swendsen–Wang multi-cluster spin flip algorithm: 2D and 3D Ising, Potts, and XY models
Main Author: | CPC, Mendeley |
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Other Authors: | Komura, Yukihiro, Okabe, Yutaka |
Format: | Dataset |
Terbitan: |
Mendeley
, 2016
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Subjects: | |
Online Access: |
https:/data.mendeley.com/datasets/7fnfbtsgrn |
ctrlnum |
0.17632-7fnfbtsgrn.1 |
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fullrecord |
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<dc><creator>CPC, Mendeley</creator><title>Improved CUDA programs for GPU computing of Swendsen–Wang multi-cluster spin flip algorithm: 2D and 3D Ising, Potts, and XY models </title><publisher>Mendeley</publisher><description>Abstract
We present new versions of sample CUDA programs for the GPU computing of the Swendsen–Wang multi-cluster spin flip algorithm. In this update, we add the method of GPU-based cluster-labeling algorithm without the use of conventional iteration (Komura, 2015) to those programs. For high-precision calculations, we also add a random-number generator in the cuRAND library. Moreover, we fix several bugs and remove the extra usage of shared memory in the kernel functions.
Title of program: SWspin_v2_0
Catalogue Id: AERM_v2_0
Nature of problem
Monte Carlo simulation of classical spin systems. Ising, q-state Potts model, and the classical XY model are treated for both two-dimensional and three-dimensional lattices.
Versions of this program held in the CPC repository in Mendeley Data
AERM_v1_0; SWspin; 10.1016/j.cpc.2013.10.029
AERM_v2_0; SWspin_v2_0; 10.1016/j.cpc.2015.10.003
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)</description><subject>Statistical Physics</subject><subject>Computational Physics</subject><subject>Thermodynamics</subject><contributor>Komura, Yukihiro</contributor><contributor>Okabe, Yutaka</contributor><type>Other:Dataset</type><identifier>10.17632/7fnfbtsgrn.1</identifier><rights>Computer Physics Communications Journal Licence</rights><rights>https://www.elsevier.com/about/policies/open-access-licenses/elsevier-user-license/cpc-license/</rights><relation>https:/data.mendeley.com/datasets/7fnfbtsgrn</relation><date>2016-03-01T12:00:00Z</date><recordID>0.17632-7fnfbtsgrn.1</recordID></dc>
|
format |
Other:Dataset Other |
author |
CPC, Mendeley |
author2 |
Komura, Yukihiro Okabe, Yutaka |
title |
Improved CUDA programs for GPU computing of Swendsen–Wang multi-cluster spin flip algorithm: 2D and 3D Ising, Potts, and XY models |
publisher |
Mendeley |
publishDate |
2016 |
topic |
Statistical Physics Computational Physics Thermodynamics |
url |
https:/data.mendeley.com/datasets/7fnfbtsgrn |
contents |
Abstract
We present new versions of sample CUDA programs for the GPU computing of the Swendsen–Wang multi-cluster spin flip algorithm. In this update, we add the method of GPU-based cluster-labeling algorithm without the use of conventional iteration (Komura, 2015) to those programs. For high-precision calculations, we also add a random-number generator in the cuRAND library. Moreover, we fix several bugs and remove the extra usage of shared memory in the kernel functions.
Title of program: SWspin_v2_0
Catalogue Id: AERM_v2_0
Nature of problem
Monte Carlo simulation of classical spin systems. Ising, q-state Potts model, and the classical XY model are treated for both two-dimensional and three-dimensional lattices.
Versions of this program held in the CPC repository in Mendeley Data
AERM_v1_0; SWspin; 10.1016/j.cpc.2013.10.029
AERM_v2_0; SWspin_v2_0; 10.1016/j.cpc.2015.10.003
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019) |
id |
IOS7969.0.17632-7fnfbtsgrn.1 |
institution |
Universitas Islam Indragiri |
affiliation |
onesearch.perpusnas.go.id |
institution_id |
804 |
institution_type |
library:university library |
library |
Teknologi Pangan UNISI |
library_id |
2816 |
collection |
Artikel mulono |
repository_id |
7969 |
city |
INDRAGIRI HILIR |
province |
RIAU |
shared_to_ipusnas_str |
1 |
repoId |
IOS7969 |
first_indexed |
2020-04-08T08:33:18Z |
last_indexed |
2020-04-08T08:33:18Z |
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dc |
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1686587771689369600 |
score |
17.538404 |