CISE Postdoctoral Research Associates in Experimental Computer Science: Parallel Hierarchical Methods for Computational Electromagnetics (CCR-NSG; ACIR)
CISE 实验计算机科学博士后研究员:计算电磁学的并行分层方法(CCR-NSG;ACIR)
基本信息
- 批准号:0103748
- 负责人:
- 金额:$ 6.6万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2001
- 资助国家:美国
- 起止时间:2001-08-15 至 2004-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
0103748Aluru, Srinivas.Iowa State UniversityCISE Postdoctoral Associates in Experimental Computer Science: Parallel Hierarchical Methods for Computational ElectromagneticsThe goal of this research is to develop parallel algorithms and build parallel software for the solution of a wide variety of problems involving the computational analysis of electromagnetic scattering. Specific problems of interest are: 1) electromagnetic scattering from quasi-planar surfaces, 2) dielectric random rough surfaces, 3) multiregion surfaces, 4) three-dimensional problems where the fields are characterized by frequency and 5) three-dimensional problems where the field behavior is dependent on time history. The unifying theme in addressing these problems will be the use of the hierarchical Fast Multipole Method and its variants. A major goal of the research is to develop the capability to solve highly non-uniform problems efficiently. Emphasis will be placed on the development of distribution-independent algorithms, i.e., provably efficient algorithms for which the run-time is independent of the distribution without making any assumptions on either the range of distributions or the limited precision of computer arithmetic. The postdoctoral research associate will develop and validate software employing these algorithms in close cooperation with experts in electromagnetics at Iowa State University. Validation of the results will be carried out via comparisons against experimental data as well as numerical results obtained from slower, established solvers. The associate will perform experimental evaluation of the performance of the software using conventional parallel computers and high-performance clusters.0103748Aluru, Srinivas.Iowa State UniversityCISE Postdoctoral Associates in Experimental Computer Science: Parallel Hierarchical Methods for Computational ElectromagneticsThe goal of this research is to develop parallel algorithms and build parallel software for the solution of a wide variety of problems involving the computational analysis of electromagnetic scattering. Specific problems of interest are: 1) electromagnetic scattering from quasi-planar surfaces, 2) dielectric random rough surfaces, 3) multiregion surfaces, 4) three-dimensional problems where the fields are characterized by frequency and 5) three-dimensional problems where the field behavior is dependent on time history. The unifying theme in addressing these problems will be the use of the hierarchical Fast Multipole Method and its variants. A major goal of the research is to develop the capability to solve highly non-uniform problems efficiently. Emphasis will be placed on the development of distribution-independent algorithms, i.e., provably efficient algorithms for which the run-time is independent of the distribution without making any assumptions on either the range of distributions or the limited precision of computer arithmetic. The postdoctoral research associate will develop and validate software employing these algorithms in close cooperation with experts in electromagnetics at Iowa State University. Validation of the results will be carried out via comparisons against experimental data as well as numerical results obtained from slower, established solvers. The associate will perform experimental evaluation of the performance of the software using conventional parallel computers and high-performance clusters.
0103748 Aluru,Srinivas。爱荷华州州立大学CISE实验计算机科学博士后研究员:计算电磁学的并行分层方法本研究的目标是开发并行算法,并建立并行软件,用于解决各种各样的问题,包括电磁散射的计算分析。 感兴趣的具体问题是:1)准平面表面的电磁散射,2)电介质随机粗糙表面,3)多区域表面,4)三维问题,其中场的特征在于频率和5)三维问题,其中场的行为是依赖于时间的历史。 解决这些问题的统一主题将是使用分层快速多极子方法及其变体。 该研究的一个主要目标是开发有效解决高度非均匀问题的能力。 重点将放在独立于分布的算法,即,可证明有效的算法,其运行时间与分布无关,而不对分布范围或计算机运算的有限精度做任何假设。 博士后研究助理将开发和验证软件采用这些算法在密切合作的电磁学专家在爱荷华州州立大学。 验证的结果将通过比较实验数据,以及从较慢的,建立求解器获得的数值结果。 助理将使用传统并行计算机和高性能集群对软件性能进行实验评估。0103748 Aluru,Srinivas.爱荷华州州立大学CISE实验计算机科学博士后助理:计算电磁学的并行分层方法本研究的目标是开发并行算法和构建并行软件,用于解决涉及电磁学的各种问题。电磁散射的计算分析 感兴趣的具体问题是:1)准平面表面的电磁散射,2)电介质随机粗糙表面,3)多区域表面,4)三维问题,其中场的特征在于频率和5)三维问题,其中场的行为是依赖于时间的历史。 解决这些问题的统一主题将是使用分层快速多极子方法及其变体。 该研究的一个主要目标是开发有效解决高度非均匀问题的能力。 重点将放在独立于分布的算法,即,可证明有效的算法,其运行时间与分布无关,而不对分布范围或计算机运算的有限精度做任何假设。 博士后研究助理将开发和验证软件采用这些算法在密切合作的电磁学专家在爱荷华州州立大学。 验证的结果将通过比较实验数据,以及从较慢的,建立求解器获得的数值结果。 该助理将使用传统并行计算机和高性能集群对软件性能进行实验评估。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Srinivas Aluru其他文献
Reply to: “Re-evaluating the evidence for a universal genetic boundary among microbial species”
回复:“重新评估微生物物种间通用遗传边界的证据”
- DOI:
10.1038/s41467-021-24129-1 - 发表时间:
2021-07-07 - 期刊:
- 影响因子:15.700
- 作者:
Luis M. Rodriguez-R;Chirag Jain;Roth E. Conrad;Srinivas Aluru;Konstantinos T. Konstantinidis - 通讯作者:
Konstantinos T. Konstantinidis
Distribution-Independent Hierarchical Algorithms for the N-body Problem
- DOI:
10.1023/a:1008047806690 - 发表时间:
1998-01-01 - 期刊:
- 影响因子:2.700
- 作者:
Srinivas Aluru;John Gustafson;G.M. Prabhu;Fatih E. Sevilgen - 通讯作者:
Fatih E. Sevilgen
A Parallel Monte Carlo Algorithm for Protein Accessible Surface Area Computation
蛋白质可及表面积计算的并行蒙特卡罗算法
- DOI:
10.1007/978-3-540-46642-0_49 - 发表时间:
1999 - 期刊:
- 影响因子:0
- 作者:
Srinivas Aluru;D. Ranjan;N. Futamura - 通讯作者:
N. Futamura
Srinivas Aluru的其他文献
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{{ truncateString('Srinivas Aluru', 18)}}的其他基金
A scalable integrated multi-modal single cell analysis framework for gene regulatory and cell-cell interaction networks
用于基因调控和细胞间相互作用网络的可扩展集成多模式单细胞分析框架
- 批准号:
2233887 - 财政年份:2023
- 资助金额:
$ 6.6万 - 项目类别:
Continuing Grant
BD Hubs: Collaborative Proposal: SOUTH:The South Big Data Innovation Hub
BD Hubs:合作提案:SOUTH:南方大数据创新中心
- 批准号:
1916589 - 财政年份:2019
- 资助金额:
$ 6.6万 - 项目类别:
Cooperative Agreement
AF: Small: Algorithmic Techniques for High-throughput Analysis of Long Reads
AF:小:长读长高通量分析的算法技术
- 批准号:
1816027 - 财政年份:2018
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
EAGER: A Framework for Learning Graph Algorithms with Applications to Social and Gene Networks
EAGER:学习图算法及其在社交和基因网络中的应用的框架
- 批准号:
1841351 - 财政年份:2018
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
MRI: Acquisition of an HPC System for Data-Driven Discovery in Computational Astrophysics, Biology, Chemistry, and Materials Science
MRI:获取 HPC 系统,用于计算天体物理学、生物学、化学和材料科学中的数据驱动发现
- 批准号:
1828187 - 财政年份:2018
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
Big Data Regional Innovation Hubs and Spokes Workshop
大数据区域创新中心和辐射研讨会
- 批准号:
1736154 - 财政年份:2017
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
SHF:Small: Reproducibility and Comprehensive Assessment of Next Generation Sequencing Bioinformatics Software
SHF:Small:下一代测序生物信息学软件的重现性和综合评估
- 批准号:
1718479 - 财政年份:2017
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
AF: Medium: Collaborative Research: Sequential and Parallel Algorithms for Approximate Sequence Matching with Applications to Computational Biology
AF:媒介:协作研究:近似序列匹配的顺序和并行算法及其在计算生物学中的应用
- 批准号:
1704552 - 财政年份:2017
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
BD Hubs: Collaborative Proposal: SOUTH: A Big Data Innovation Hub for the South Region
BD 中心:合作提案:SOUTH:南部地区的大数据创新中心
- 批准号:
1550305 - 财政年份:2015
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
EAGER: Exploratory Research on the Micron Automata Processor
EAGER:微米自动机处理器的探索性研究
- 批准号:
1448333 - 财政年份:2014
- 资助金额:
$ 6.6万 - 项目类别:
Standard Grant
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