H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 5,763 98 World Ranking 8254 National Ranking 65

Overview

What is he best known for?

The fields of study he is best known for:

  • Algebra
  • Algorithm
  • Programming language

The scientist’s investigation covers issues in Linear algebra, Algorithm, Parallel computing, Sparse approximation and Matrix-free methods. The various areas that Fred G. Gustavson examines in his Linear algebra study include Fortran, Recursion, System of linear equations, Computation and Cholesky decomposition. The concepts of his Algorithm study are interwoven with issues in Multiplication and Memory hierarchy.

His study on Parallel computing is mostly dedicated to connecting different topics, such as Central processing unit. His Sparse approximation research integrates issues from Sparse matrix and Coefficient matrix. To a larger extent, he studies Matrix with the aim of understanding Sparse matrix.

His most cited work include:

  • Fast solution of toeplitz systems of equations and computation of Padé approximants (349 citations)
  • The Sparse Tableau Approach to Network Analysis and Design (273 citations)
  • Two Fast Algorithms for Sparse Matrices: Multiplication and Permuted Transposition (271 citations)

What are the main themes of his work throughout his whole career to date?

Parallel computing, Algorithm, Matrix, Cholesky decomposition and Linear algebra are his primary areas of study. The study incorporates disciplines such as Computation, Basic Linear Algebra Subprograms and Subroutine in addition to Parallel computing. His Algorithm research is multidisciplinary, incorporating elements of Multiplication, Numerical linear algebra, Data structure and Matrix calculus.

His Matrix research is multidisciplinary, relying on both Structure and Square. His Cholesky decomposition research is multidisciplinary, incorporating perspectives in Factorization, Symmetric matrix and Fortran. The Linear algebra study combines topics in areas such as Theoretical computer science, Multi-core processor and Implementation.

He most often published in these fields:

  • Parallel computing (47.10%)
  • Algorithm (39.13%)
  • Matrix (38.41%)

What were the highlights of his more recent work (between 2006-2018)?

  • Parallel computing (47.10%)
  • Algorithm (39.13%)
  • Cholesky decomposition (26.81%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Parallel computing, Algorithm, Cholesky decomposition, Matrix and Factorization. His research integrates issues of ScaLAPACK and Linear algebra in his study of Parallel computing. Algorithm connects with themes related to Data structure in his study.

His Cholesky decomposition study combines topics in areas such as Symmetric matrix, Basic Linear Algebra Subprograms and Fortran. His Fortran research incorporates themes from Software, Computation and Hermitian matrix. His Matrix research focuses on Parallelism and how it connects with Space, Square and Set.

Between 2006 and 2018, his most popular works were:

  • An experimental comparison of cache-oblivious and cache-conscious programs (76 citations)
  • Parallel and Cache-Efficient In-Place Matrix Storage Format Conversion (55 citations)
  • Method and structure of using simd vector architectures to implement matrix multiplication (19 citations)

In his most recent research, the most cited papers focused on:

  • Algebra
  • Algorithm
  • Programming language

Fred G. Gustavson mostly deals with Parallel computing, Algorithm, Data structure, Software and Parallel algorithm. His work in the fields of Cache overlaps with other areas such as Load balancing. His Cholesky decomposition research extends to Algorithm, which is thematically connected.

His biological study spans a wide range of topics, including Transformation, Structure, Representation and Matrix. Fred G. Gustavson combines subjects such as Structure, Matrix multiplication and SIMD with his study of Software. His Parallel algorithm study combines topics from a wide range of disciplines, such as Synchronization, Overhead, Distributed memory and Information and Computer Science.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Fast solution of toeplitz systems of equations and computation of Padé approximants

Richard P Brent;Fred G Gustavson;David Y.Y Yun.
Journal of Algorithms (1980)

533 Citations

Two Fast Algorithms for Sparse Matrices: Multiplication and Permuted Transposition

Fred G. Gustavson.
ACM Transactions on Mathematical Software (1978)

409 Citations

The Sparse Tableau Approach to Network Analysis and Design

G. Hachtel;R. Brayton;F. Gustavson.
IEEE Transactions on Circuit Theory (1971)

404 Citations

FLAME: Formal Linear Algebra Methods Environment

John A. Gunnels;Fred G. Gustavson;Greg M. Henry;Robert A. van de Geijn.
ACM Transactions on Mathematical Software (2001)

354 Citations

Implementing Linear Algebra Algorithms for Dense Matrices on a Vector Pipeline Machine

J. J. Dongarra;F. G. Gustavson;A. Karp.
Siam Review (1984)

330 Citations

Recursion leads to automatic variable blocking for dense linear-algebra algorithms

F. G. Gustavson.
Ibm Journal of Research and Development (1997)

328 Citations

Recursive Blocked Algorithms and Hybrid Data Structures for Dense Matrix Library Software

Erik Elmroth;Fred G. Gustavson;Isak Jonsson;Bo Kågström.
Siam Review (2004)

255 Citations

Method and system for dynamically reconfiguring a register file in a vector processor

Ramesh C. Agarwal;Randall D. Groves;Fred G. Gustavson;Mark A. Johnson.
(1994)

223 Citations

A three-dimensional approach to parallel matrix multiplication

R. C. Agarwal;S. M. Balle;F. G. Gustavson;M. Joshi.
Ibm Journal of Research and Development (1995)

179 Citations

Method and system in a data processing system for loading and storing vectors in a plurality of modes

Ramesh Chandra Agarwal;Randall Dean Groves;Fred G. Gustavson;Mark A. Johnson.
(1995)

167 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Fred G. Gustavson

Jack Dongarra

Jack Dongarra

University of Tennessee at Knoxville

Publications: 99

Robert A. van de Geijn

Robert A. van de Geijn

The University of Texas at Austin

Publications: 56

James Demmel

James Demmel

University of California, Berkeley

Publications: 54

Victor Y. Pan

Victor Y. Pan

City University of New York

Publications: 50

Enrique S. Quintana-Ortí

Enrique S. Quintana-Ortí

Universitat Politècnica de València

Publications: 46

Piotr Luszczek

Piotr Luszczek

University of Tennessee at Knoxville

Publications: 31

Aydin Buluc

Aydin Buluc

Lawrence Berkeley National Laboratory

Publications: 31

Erich Kaltofen

Erich Kaltofen

North Carolina State University

Publications: 24

John A. Gunnels

John A. Gunnels

Amazon (United States)

Publications: 23

Michael Karl Gschwind

Michael Karl Gschwind

Facebook (United States)

Publications: 20

Sivan Toledo

Sivan Toledo

Tel Aviv University

Publications: 18

José E. Moreira

José E. Moreira

IBM (United States)

Publications: 18

Katherine Yelick

Katherine Yelick

Lawrence Berkeley National Laboratory

Publications: 17

Mahmut Kandemir

Mahmut Kandemir

Pennsylvania State University

Publications: 16

Iain S. Duff

Iain S. Duff

Rutherford Appleton Laboratory

Publications: 16

Manish Gupta

Manish Gupta

Google (United States)

Publications: 15

Something went wrong. Please try again later.