.png){kind=icon}
返回
Multi-GPU-based Swendsen-Wang multi-cluster algorithm with reduced data traffic
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cpc.2015.04.025.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The computational performance of multi-GPU applications can be degraded by the data communication between each GPU. To realize high-speed computation with multiple GPUs, we should minimize the cost of this data communication. In this paper, I propose a multiple GPU computing method for the Swendsen-Wang (SW) multi-cluster algorithm that reduces the data traffic between each GPU. I realize this reduction in data traffic by adjusting the connection information between each GPU in advance. The code is implemented on the large-scale open science TSUBAME 2.5 supercomputer, and its performance is evaluated using a simulation of the three-dimensional Ising model at the critical temperature. The results show that the data communication between each GPU is reduced by 90%, and the number of communications between each GPU decreases by about half. Using 512 GPUs, the computation time is 0.005 ns per spin update at the critical temperature for a total system size of N = 4096(3). (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Monte Carlo simulation
Cluster algorithm
Ising model
Parallel computing
Multi-GPU
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.2W
被引数:
3.7W
机构
引用论文
.png){kind=icon}
Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model二维伊辛模型的多GPU加速多自旋蒙特卡罗模拟
OAAI
OAAI分享收藏
查看更多内容