2013 - SIAM Fellow For contributions to parallel computing and computational science.
2009 - ACM Gordon Bell Prize The Cat is Out of the Bag: Cortical Simulations with 109 Neurons, 1013 Synapses
His scientific interests lie mostly in Parallel computing, Algorithm, Combinatorics, Cluster analysis and Lanczos algorithm. His Parallel computing study integrates concerns from other disciplines, such as Kernel and Computational science. Horst D. Simon works mostly in the field of Algorithm, limiting it down to topics relating to Eigenvalues and eigenvectors and, in certain cases, Stability and Maxima and minima.
His Combinatorics research incorporates elements of Discrete mathematics, QR decomposition and Gramian matrix. The various areas that Horst D. Simon examines in his Cluster analysis study include Adjacency matrix, Matrix and Graph partition. His Supercomputer research is multidisciplinary, incorporating elements of Unified Parallel C and Massively parallel.
Horst D. Simon spends much of his time researching Supercomputer, Parallel computing, Computational science, Algorithm and Eigenvalues and eigenvectors. His TOP500 study in the realm of Supercomputer interacts with subjects such as National laboratory. Horst D. Simon specializes in Parallel computing, namely Massively parallel.
His Computational science research incorporates themes from Parallel algorithm, Parallel processing, Visualization, Software and MIMD. Horst D. Simon interconnects Dimension, Sparse matrix, Laplacian matrix and Cluster analysis in the investigation of issues within Algorithm. As a part of the same scientific study, Horst D. Simon usually deals with the Eigenvalues and eigenvectors, concentrating on Applied mathematics and frequently concerns with Mathematical optimization.
His primary areas of investigation include Supercomputer, Parallel computing, Data science, Futures contract and Massively parallel. His Supercomputer study combines topics from a wide range of disciplines, such as Emerging technologies and Computer engineering. His work deals with themes such as Solver, Data structure and Fortran, which intersect with Parallel computing.
His Data science study combines topics in areas such as Field, Computing Methodologies and TOP500. His work carried out in the field of Massively parallel brings together such families of science as Load balancing, Theoretical computer science, Distributed memory and Partition. His research in Algorithm tackles topics such as Hermitian matrix which are related to areas like Mathematical optimization.
Horst D. Simon mostly deals with Supercomputer, Parallel computing, Mathematical optimization, Data science and Massively parallel. His research integrates issues of Computing Methodologies, Work in process and FLOPS in his study of Supercomputer. His study brings together the fields of Computation and Parallel computing.
His Mathematical optimization research focuses on subjects like Algorithm, which are linked to Hermitian matrix, Eigenvalues and eigenvectors, Lanczos resampling and Semi-supervised learning. He has researched Data science in several fields, including Data-driven, Computer architecture, TOP500 and Metric. His studies in Massively parallel integrate themes in fields like Scalability and Theoretical computer science.
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The NAS parallel benchmarks—summary and preliminary results
D. H. Bailey;E. Barszcz;J. T. Barton;D. S. Browning.
conference on high performance computing (supercomputing) (1991)
The Nas Parallel Benchmarks
D.H. Bailey;E. Barszcz;J.T. Barton;D.S. Browning.
ieee international conference on high performance computing data and analytics (1991)
Partitioning sparse matrices with eigenvectors of graphs
Alex Pothen;Horst D. Simon;Kan-Pu Liou.
SIAM Journal on Matrix Analysis and Applications (1990)
Partitioning of unstructured problems for parallel processing
Computing Systems in Engineering (1991)
A min-max cut algorithm for graph partitioning and data clustering
C.H.Q. Ding;Xiaofeng He;Xiaofeng He;Hongyuan Zha;Ming Gu.
international conference on data mining (2001)
Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems
Stephen T. Barnard;Horst D. Simon.
Concurrency and Computation: Practice and Experience (1994)
Spectral Relaxation for K-means Clustering
Hongyuan Zha;Xiaofeng He;Chris Ding;Ming Gu.
neural information processing systems (2001)
A Shifted Block Lanczos Algorithm for Solving Sparse Symmetric Generalized Eigenproblems
Roger G. Grimes;John G. Lewis;Horst D. Simon.
SIAM Journal on Matrix Analysis and Applications (1994)
PageRank: HITS and a Unified Framework for Link Analysis.
Chris H. Q. Ding;Xiaofeng He;Parry Husbands;Hongyuan Zha.
siam international conference on data mining (2003)
Bipartite graph partitioning and data clustering
Hongyuan Zha;Xiaofeng He;Chris Ding;Horst Simon.
conference on information and knowledge management (2001)
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