有限元电磁学的硬件加速:现场可编程门阵列的高效稀疏矩阵浮点计算

Y. El Kurdi, W. Gross, D. Giannacopoulos
{"title":"有限元电磁学的硬件加速:现场可编程门阵列的高效稀疏矩阵浮点计算","authors":"Y. El Kurdi, W. Gross, D. Giannacopoulos","doi":"10.1109/CEFC-06.2006.1633187","DOIUrl":null,"url":null,"abstract":"Custom hardware acceleration of electromagnetics computations leverages favorable industry trends, which indicate reconfigurable hardware devices such as field programmable gate arrays (FPGAs) may soon outperform general purpose CPUs. We present a new striping method for efficient sparse matrix-vector multiplication implemented in a deeply pipelined FPGA design. The effectiveness of the new method is illustrated for a representative set of finite element matrices","PeriodicalId":262549,"journal":{"name":"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Hardware Acceleration for Finite Element Electromagnetics: Efficient Sparse Matrix Floating-Point Computations with Field Programmable Gate Arrays\",\"authors\":\"Y. El Kurdi, W. Gross, D. Giannacopoulos\",\"doi\":\"10.1109/CEFC-06.2006.1633187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Custom hardware acceleration of electromagnetics computations leverages favorable industry trends, which indicate reconfigurable hardware devices such as field programmable gate arrays (FPGAs) may soon outperform general purpose CPUs. We present a new striping method for efficient sparse matrix-vector multiplication implemented in a deeply pipelined FPGA design. The effectiveness of the new method is illustrated for a representative set of finite element matrices\",\"PeriodicalId\":262549,\"journal\":{\"name\":\"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEFC-06.2006.1633187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEFC-06.2006.1633187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

电磁计算的定制硬件加速利用了有利的行业趋势,这表明可重构硬件设备(如现场可编程门阵列(fpga))可能很快超过通用cpu。我们提出了一种新的条带化方法,在深度流水线FPGA设计中实现了高效的稀疏矩阵向量乘法。通过一个具有代表性的有限元矩阵集,说明了新方法的有效性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hardware Acceleration for Finite Element Electromagnetics: Efficient Sparse Matrix Floating-Point Computations with Field Programmable Gate Arrays
Custom hardware acceleration of electromagnetics computations leverages favorable industry trends, which indicate reconfigurable hardware devices such as field programmable gate arrays (FPGAs) may soon outperform general purpose CPUs. We present a new striping method for efficient sparse matrix-vector multiplication implemented in a deeply pipelined FPGA design. The effectiveness of the new method is illustrated for a representative set of finite element matrices
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:604180095
Book学术官方微信