MAC网格上不可压缩Navier-Stokes方程的优化GPU仿真

L. Itu, F. Moldoveanu, A. Postelnicu, C. Suciu
{"title":"MAC网格上不可压缩Navier-Stokes方程的优化GPU仿真","authors":"L. Itu, F. Moldoveanu, A. Postelnicu, C. Suciu","doi":"10.1109/ROEDUNET.2011.5993692","DOIUrl":null,"url":null,"abstract":"The paper introduces an optimized GPU based implementation of the incompressible Navier-Stokes equations which are solved using the artificial compressibility method. The numerical scheme is based on a finite difference method. The domain on which the simulations have been performed is a backward facing step problem and the discretizations have been carried out on a MAC grid. The most time consuming parts, i.e. the computations of the velocities and of the pressure values, have been moved onto the GPU. Two separate kernels have been defined because there is no communication between the blocks of the GPU grid. Several optimization strategies have incrementally increased the performance of the two kernels. The most important ones are: coalesced global memory, reduced read and copy operations and optimum usage of shared memory. The results of the comparison between the CPU and GPU performance indicate a speed-up which varies from just under one order of magnitude for the coarsest grid up to two orders of magnitude for the finest grid.","PeriodicalId":277269,"journal":{"name":"2011 RoEduNet International Conference 10th Edition: Networking in Education and Research","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Optimized GPU based simulation of the incompressible Navier-Stokes equations on a MAC grid\",\"authors\":\"L. Itu, F. Moldoveanu, A. Postelnicu, C. Suciu\",\"doi\":\"10.1109/ROEDUNET.2011.5993692\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper introduces an optimized GPU based implementation of the incompressible Navier-Stokes equations which are solved using the artificial compressibility method. The numerical scheme is based on a finite difference method. The domain on which the simulations have been performed is a backward facing step problem and the discretizations have been carried out on a MAC grid. The most time consuming parts, i.e. the computations of the velocities and of the pressure values, have been moved onto the GPU. Two separate kernels have been defined because there is no communication between the blocks of the GPU grid. Several optimization strategies have incrementally increased the performance of the two kernels. The most important ones are: coalesced global memory, reduced read and copy operations and optimum usage of shared memory. The results of the comparison between the CPU and GPU performance indicate a speed-up which varies from just under one order of magnitude for the coarsest grid up to two orders of magnitude for the finest grid.\",\"PeriodicalId\":277269,\"journal\":{\"name\":\"2011 RoEduNet International Conference 10th Edition: Networking in Education and Research\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 RoEduNet International Conference 10th Edition: Networking in Education and Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROEDUNET.2011.5993692\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 RoEduNet International Conference 10th Edition: Networking in Education and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROEDUNET.2011.5993692","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

本文介绍了一种基于GPU的不可压缩Navier-Stokes方程的优化实现,该方程采用人工可压缩性方法求解。数值格式基于有限差分法。所模拟的域是一个面向后向的阶跃问题,离散化是在MAC网格上进行的。最耗时的部分,即速度和压力值的计算,已经转移到GPU上。由于GPU网格块之间没有通信,所以定义了两个独立的内核。一些优化策略逐渐提高了这两个内核的性能。最重要的是:合并的全局内存,减少读取和复制操作以及共享内存的最佳使用。CPU和GPU性能之间的比较结果表明,从最粗糙的网格的不到一个数量级到最精细的网格的两个数量级的加速变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized GPU based simulation of the incompressible Navier-Stokes equations on a MAC grid
The paper introduces an optimized GPU based implementation of the incompressible Navier-Stokes equations which are solved using the artificial compressibility method. The numerical scheme is based on a finite difference method. The domain on which the simulations have been performed is a backward facing step problem and the discretizations have been carried out on a MAC grid. The most time consuming parts, i.e. the computations of the velocities and of the pressure values, have been moved onto the GPU. Two separate kernels have been defined because there is no communication between the blocks of the GPU grid. Several optimization strategies have incrementally increased the performance of the two kernels. The most important ones are: coalesced global memory, reduced read and copy operations and optimum usage of shared memory. The results of the comparison between the CPU and GPU performance indicate a speed-up which varies from just under one order of magnitude for the coarsest grid up to two orders of magnitude for the finest grid.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信