Hendrickson, B. (Bruce)
Overview
Works:  19 works in 21 publications in 1 language and 67 library holdings 

Genres:  Handbooks and manuals Academic theses Conference papers and proceedings 
Roles:  Editor, Author 
Classifications:  QA75.5, 511.6 
Publication Timeline
.
Most widely held works by
B Hendrickson
Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms by SIAM Conference on Applied and Computational Discrete Algorithms(
)
2 editions published in 2021 in English and held by 37 WorldCat member libraries worldwide
The SIAM Conference on Applied and Computational Discrete Algorithms is a new conference that brings together researchers who design and study combinatorial and graph algorithms motivated by applications. ACDA is organized by SIAM under the auspices of the SIAM Activity Group on Applied and Computational Discrete Algorithms. ACDA subsumes the longrunning series of SIAM Workshops on Combinatorial Scientific Computing, and expands its scope to applications of discrete models and algorithms across all areas in the physical and life sciences and engineering, the social and information sciences, and anywhere discrete mathematical techniques are used to formulate and solve problems in the world. ACDA invites papers on the formulation of combinatorial problems from applications; theoretical analyses; design of algorithms; computational evaluation of the algorithms; and deployment of the resulting software to enable applications
2 editions published in 2021 in English and held by 37 WorldCat member libraries worldwide
The SIAM Conference on Applied and Computational Discrete Algorithms is a new conference that brings together researchers who design and study combinatorial and graph algorithms motivated by applications. ACDA is organized by SIAM under the auspices of the SIAM Activity Group on Applied and Computational Discrete Algorithms. ACDA subsumes the longrunning series of SIAM Workshops on Combinatorial Scientific Computing, and expands its scope to applications of discrete models and algorithms across all areas in the physical and life sciences and engineering, the social and information sciences, and anywhere discrete mathematical techniques are used to formulate and solve problems in the world. ACDA invites papers on the formulation of combinatorial problems from applications; theoretical analyses; design of algorithms; computational evaluation of the algorithms; and deployment of the resulting software to enable applications
A divideandconquer algorithm for identifying strongly connectedcomponents(
)
1 edition published in 2003 in English and held by 5 WorldCat member libraries worldwide
Strongly connected components of a directed graph can be found in an optimal linear time, by algorithms based on depth first search. Unfortunately, depth first search is difficult to parallelize. We describe two divideandconquer algorithms for this problem that have significantly greater potential for parallelization. We show the expected serial runtime of our simpler algorithm to be O(m log n), for a graph with n vertices and m edges. We then show that the second algorithm has O(mlog n) worstcase complexity
1 edition published in 2003 in English and held by 5 WorldCat member libraries worldwide
Strongly connected components of a directed graph can be found in an optimal linear time, by algorithms based on depth first search. Unfortunately, depth first search is difficult to parallelize. We describe two divideandconquer algorithms for this problem that have significantly greater potential for parallelization. We show the expected serial runtime of our simpler algorithm to be O(m log n), for a graph with n vertices and m edges. We then show that the second algorithm has O(mlog n) worstcase complexity
Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21) by SIAM Conference on Applied and Computational Discrete Algorithms(
)
1 edition published in 2021 in English and held by 4 WorldCat member libraries worldwide
The SIAM Conference on Applied and Computational Discrete Algorithms is a new conference that brings together researchers who design and study combinatorial and graph algorithms motivated by applications. ACDA is organized by SIAM under the auspices of the SIAM Activity Group on Applied and Computational Discrete Algorithms. ACDA subsumes the longrunning series of SIAM Workshops on Combinatorial Scientific Computing, and expands its scope to applications of discrete models and algorithms across all areas in the physical and life sciences and engineering, the social and information sciences, and anywhere discrete mathematical techniques are used to formulate and solve problems in the world. ACDA invites papers on the formulation of combinatorial problems from applications; theoretical analyses; design of algorithms; computational evaluation of the algorithms; and deployment of the resulting software to enable applications
1 edition published in 2021 in English and held by 4 WorldCat member libraries worldwide
The SIAM Conference on Applied and Computational Discrete Algorithms is a new conference that brings together researchers who design and study combinatorial and graph algorithms motivated by applications. ACDA is organized by SIAM under the auspices of the SIAM Activity Group on Applied and Computational Discrete Algorithms. ACDA subsumes the longrunning series of SIAM Workshops on Combinatorial Scientific Computing, and expands its scope to applications of discrete models and algorithms across all areas in the physical and life sciences and engineering, the social and information sciences, and anywhere discrete mathematical techniques are used to formulate and solve problems in the world. ACDA invites papers on the formulation of combinatorial problems from applications; theoretical analyses; design of algorithms; computational evaluation of the algorithms; and deployment of the resulting software to enable applications
Conditions for unique graph embeddings by
B Hendrickson(
Book
)
2 editions published in 1988 in English and held by 4 WorldCat member libraries worldwide
If we assume the edge lengths are unrelated then the uniqueness question can be approached from a purely graph theoretic framework that ignores edge lengths. This paper identifies three necessary graph theoretic conditions for a graph to have a unique embedding in any dimension. Efficient sequential and NC algorithms are presented for each condition, although these algorithms have very different flavors in different dimensions."
2 editions published in 1988 in English and held by 4 WorldCat member libraries worldwide
If we assume the edge lengths are unrelated then the uniqueness question can be approached from a purely graph theoretic framework that ignores edge lengths. This paper identifies three necessary graph theoretic conditions for a graph to have a unique embedding in any dimension. Efficient sequential and NC algorithms are presented for each condition, although these algorithms have very different flavors in different dimensions."
Multidimensional spectral load balancing by
B Hendrickson(
Book
)
1 edition published in 1993 in English and held by 2 WorldCat member libraries worldwide
We describe an algorithm for the static load balancing of scientific computations that generalizes and improves upon spectral bisection. Through a novel use of multiple eigenvectors, our new spectral algorithm can divide a computation into 4 or 8 pieces at once. These multidimensional spectral partitioning algorithms generate balanced partitions that have lower communication overhead and are less expensive to compute than those produced by spectral bisection. In addition, they automatically work to minimize message contention on a hypercube or mesh architecture. These spectral partitions are further improved by a multidimensional generalization of the KernighanLin graph partitioning algorithm. Results on several computational grids are given and compared with other popular methods
1 edition published in 1993 in English and held by 2 WorldCat member libraries worldwide
We describe an algorithm for the static load balancing of scientific computations that generalizes and improves upon spectral bisection. Through a novel use of multiple eigenvectors, our new spectral algorithm can divide a computation into 4 or 8 pieces at once. These multidimensional spectral partitioning algorithms generate balanced partitions that have lower communication overhead and are less expensive to compute than those produced by spectral bisection. In addition, they automatically work to minimize message contention on a hypercube or mesh architecture. These spectral partitions are further improved by a multidimensional generalization of the KernighanLin graph partitioning algorithm. Results on several computational grids are given and compared with other popular methods
Graph partitioning and parallel computing(
Book
)
1 edition published in 2000 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 2000 in English and held by 2 WorldCat member libraries worldwide
Combinatorial parallel and scientific computing by
B Hendrickson(
)
1 edition published in 2005 in Undetermined and held by 1 WorldCat member library worldwide
Combinatorial algorithms have long played apivotal enabling role in many applications of parallel computing. Graph algorithms in particular arise in load balancing, scheduling, mapping and many other aspects of the parallelization of irregular applications. These are still active research areas, mostly due to evolving computational techniques and rapidly changing computational platforms. But the relationship between parallel computing and discrete algorithms is much richer than the mere use of graphalgorithms to support the parallelization of traditional scientific computations. Important, emerging areas of science are fundamentally discrete, and they are increasingly reliant on the power of parallel computing. Examples include computational biology, scientific datamining, and network analysis. These applications are changing the relationship between discrete algorithms and parallel computing. Inaddition to their traditional role as enablers of high performance, combinatorial algorithms are now customers for parallel computing. New parallelization techniques for combinatorial algorithms need to be developed to support these nontraditional scientific approaches
1 edition published in 2005 in Undetermined and held by 1 WorldCat member library worldwide
Combinatorial algorithms have long played apivotal enabling role in many applications of parallel computing. Graph algorithms in particular arise in load balancing, scheduling, mapping and many other aspects of the parallelization of irregular applications. These are still active research areas, mostly due to evolving computational techniques and rapidly changing computational platforms. But the relationship between parallel computing and discrete algorithms is much richer than the mere use of graphalgorithms to support the parallelization of traditional scientific computations. Important, emerging areas of science are fundamentally discrete, and they are increasingly reliant on the power of parallel computing. Examples include computational biology, scientific datamining, and network analysis. These applications are changing the relationship between discrete algorithms and parallel computing. Inaddition to their traditional role as enablers of high performance, combinatorial algorithms are now customers for parallel computing. New parallelization techniques for combinatorial algorithms need to be developed to support these nontraditional scientific approaches
An improved spectral load balancing method(
Book
)
1 edition published in 1993 in English and held by 1 WorldCat member library worldwide
We describe an algorithm for the static load balancing of scientific computations that generalizes and improves upon spectral bisection. Though a novel use of multiple eigenvectors, our new spectral algorithm can divide a computation into 4 or 8 pieces at once. This leads to balanced partitions that have lower communication overhead and are less expensive to compute than those of spectral bisection. In addition, our approach automatically works to minimize message contention in a hypercube or mesh architecture
1 edition published in 1993 in English and held by 1 WorldCat member library worldwide
We describe an algorithm for the static load balancing of scientific computations that generalizes and improves upon spectral bisection. Though a novel use of multiple eigenvectors, our new spectral algorithm can divide a computation into 4 or 8 pieces at once. This leads to balanced partitions that have lower communication overhead and are less expensive to compute than those of spectral bisection. In addition, our approach automatically works to minimize message contention in a hypercube or mesh architecture
A multilevel algorithm for reducing the envelope of sparse matrices by
Stanford University(
Book
)
1 edition published in 1996 in English and held by 1 WorldCat member library worldwide
1 edition published in 1996 in English and held by 1 WorldCat member library worldwide
An improved spectral graph partitioning algorithm for mapping parallel computations by
B Hendrickson(
Book
)
1 edition published in 1992 in English and held by 1 WorldCat member library worldwide
1 edition published in 1992 in English and held by 1 WorldCat member library worldwide
Parallel processing for scientific computing : ... SIAM conference, 9th, March 2224, 1999, San Antonio, TX(
Book
)
1 edition published in 1998 in English and held by 1 WorldCat member library worldwide
1 edition published in 1998 in English and held by 1 WorldCat member library worldwide
Combinatorial Parallel and Scientific Computing by
B Hendrickson(
)
1 edition published in 2006 in Undetermined and held by 1 WorldCat member library worldwide
Combinatorial algorithms have long played apivotal enabling role in many applications of parallel computing. \index Graph algorithms in particular arise in load balancing, scheduling, mapping and many other aspects of the parallelization of \index irregular applications. These are still active research areas, mostly due toevolving computational techniques and rapidly changing computational platforms. But the relationship between parallel computing and discrete algorithms is much richer than the mere use of \index graph algorithms to support the parallelization of traditional scientific computations. Important, emerging areas of science are fundamentally discrete, and they are increasingly reliant on the power of parallel computing. Examples include \index computational biology, \index scientific datamining, and \index network analysis. These applications are changing the relationship between \index discrete algorithms and parallel computing. In addition to their traditional role as enablers of high performance, \index combinatorial algorithms are now customers for parallel computing. New parallelization techniques for combinatorial algorithms need to be developed to support these nontraditional scientific approaches. This chapter will describe some of the many areas of intersection between discrete algorithms and parallel scientific computing. Due to space limitations, this chapter is not a comprehensive survey, but rather an introduction to a diverse set of techniques and applications with a particular emphasis on work presented at the Eleventh SIAM Conference on Parallel Processing for Scientific Computing. Some topics highly relevant to this chapter (e.g., \load balancing) are addressed elsewhere in this book, and so we will not discuss them here
1 edition published in 2006 in Undetermined and held by 1 WorldCat member library worldwide
Combinatorial algorithms have long played apivotal enabling role in many applications of parallel computing. \index Graph algorithms in particular arise in load balancing, scheduling, mapping and many other aspects of the parallelization of \index irregular applications. These are still active research areas, mostly due toevolving computational techniques and rapidly changing computational platforms. But the relationship between parallel computing and discrete algorithms is much richer than the mere use of \index graph algorithms to support the parallelization of traditional scientific computations. Important, emerging areas of science are fundamentally discrete, and they are increasingly reliant on the power of parallel computing. Examples include \index computational biology, \index scientific datamining, and \index network analysis. These applications are changing the relationship between \index discrete algorithms and parallel computing. In addition to their traditional role as enablers of high performance, \index combinatorial algorithms are now customers for parallel computing. New parallelization techniques for combinatorial algorithms need to be developed to support these nontraditional scientific approaches. This chapter will describe some of the many areas of intersection between discrete algorithms and parallel scientific computing. Due to space limitations, this chapter is not a comprehensive survey, but rather an introduction to a diverse set of techniques and applications with a particular emphasis on work presented at the Eleventh SIAM Conference on Parallel Processing for Scientific Computing. Some topics highly relevant to this chapter (e.g., \load balancing) are addressed elsewhere in this book, and so we will not discuss them here
The Chaco user's guide. Version 1.0(
Book
)
1 edition published in 1993 in English and held by 1 WorldCat member library worldwide
Graph partitioning is a fundamental problem in many scientific settings. This document describes the capabilities and operation of Chaco, a software package designed to partition graphs. Chaco allows for recursive application of any of several different methods for finding small edge separators in weighted graphs. These methods include inertial, spectral, KernighanLin and multilevel methods in addition to several simpler strategies. Each of these methods can be used to partition the graph into two, four or eight pieces at each level of recursion. In addition, the KernighanLin method can be used to improve partitions generated by any of the other methods. Brief descriptions of these methods are provided, along with references to relevant literature. The user interface, input/output formats and appropriate settings for a variety of code parameters are discussed in detail, and some suggestions on algorithm selection are offered
1 edition published in 1993 in English and held by 1 WorldCat member library worldwide
Graph partitioning is a fundamental problem in many scientific settings. This document describes the capabilities and operation of Chaco, a software package designed to partition graphs. Chaco allows for recursive application of any of several different methods for finding small edge separators in weighted graphs. These methods include inertial, spectral, KernighanLin and multilevel methods in addition to several simpler strategies. Each of these methods can be used to partition the graph into two, four or eight pieces at each level of recursion. In addition, the KernighanLin method can be used to improve partitions generated by any of the other methods. Brief descriptions of these methods are provided, along with references to relevant literature. The user interface, input/output formats and appropriate settings for a variety of code parameters are discussed in detail, and some suggestions on algorithm selection are offered
Savings habits of North Dakotans by
B Hendrickson(
)
1 edition published in 1982 in English and held by 1 WorldCat member library worldwide
1 edition published in 1982 in English and held by 1 WorldCat member library worldwide
ASCR@40: Highlights and Impacts of ASCR’s Programs by
B Hendrickson(
)
1 edition published in 2020 in Undetermined and held by 1 WorldCat member library worldwide
1 edition published in 2020 in Undetermined and held by 1 WorldCat member library worldwide
Proceedings of the ninth SIAM conference on parallel processing for scientific computing : San Antonio (TX), 2224.03.1999
: sponsored by the SIAM Activity Group on Supercomputing by
B Hendrickson(
Book
)
1 edition published in 1999 in English and held by 1 WorldCat member library worldwide
1 edition published in 1999 in English and held by 1 WorldCat member library worldwide
An efficient parallel algorithm for matrixvector multiplication by
B Hendrickson(
Book
)
1 edition published in 1993 in English and held by 1 WorldCat member library worldwide
The multiplication of a vector by a matrix is the kernel computation of many algorithms in scientific computations. A fast parallel algorithm for this calculation is therefore necessary if we are to make full use of the new generation of parallel supercomputers. This paper presents a high performance, parallel matrixvector multiplication algorithm that is particularly wellsuited to hypercube multiprocessors.
1 edition published in 1993 in English and held by 1 WorldCat member library worldwide
The multiplication of a vector by a matrix is the kernel computation of many algorithms in scientific computations. A fast parallel algorithm for this calculation is therefore necessary if we are to make full use of the new generation of parallel supercomputers. This paper presents a high performance, parallel matrixvector multiplication algorithm that is particularly wellsuited to hypercube multiprocessors.
Conditions for unique graph embeddings by
B Hendrickson(
Book
)
1 edition published in 1988 in English and held by 1 WorldCat member library worldwide
1 edition published in 1988 in English and held by 1 WorldCat member library worldwide
Proceedings of the Ninth SIAM Conference on Parallel Processing for Scientific Computing 1999(
Book
)
1 edition published in 1999 in English and held by 1 WorldCat member library worldwide
1 edition published in 1999 in English and held by 1 WorldCat member library worldwide
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Related Identities
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