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- Robert A. van de Geijn

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
41
Citations
7,454
122
World Ranking
4289
National Ranking
2149

- Operating system
- Parallel computing
- Programming language

Robert A. van de Geijn mainly focuses on Parallel computing, Linear algebra, Matrix multiplication, Algorithm and Implementation. His research on Parallel computing often connects related areas such as Scalability. His Linear algebra research is multidisciplinary, incorporating perspectives in Programming language, Correctness, Computation and Basic Linear Algebra Subprograms.

His research is interdisciplinary, bridging the disciplines of Matrix and Algorithm. His Implementation research incorporates themes from Collective communication, Matrix calculus and Multiplication. His research in Matrix calculus intersects with topics in Memory architecture, Computer engineering and Selection.

- Anatomy of high-performance matrix multiplication (520 citations)
- SUMMA: Scalable Universal Matrix Multiplication Algorithm (400 citations)
- High-performance implementation of the level-3 BLAS (251 citations)

Parallel computing, Linear algebra, Algorithm, Matrix and Matrix multiplication are his primary areas of study. His study in Parallel computing is interdisciplinary in nature, drawing from both Scalability, QR decomposition and Sparse matrix. His Linear algebra research includes elements of Linear system, Numerical linear algebra, Computation and Basic Linear Algebra Subprograms.

His Algorithm research focuses on Blocking and how it connects with Function. In his study, which falls under the umbrella issue of Matrix, Implementation is strongly linked to Parallel algorithm. Robert A. van de Geijn interconnects Multiplication and Overhead in the investigation of issues within Matrix multiplication.

- Parallel computing (49.67%)
- Linear algebra (47.06%)
- Algorithm (32.68%)

- Matrix multiplication (20.92%)
- Algorithm (32.68%)
- Parallel computing (49.67%)

Robert A. van de Geijn mainly investigates Matrix multiplication, Algorithm, Parallel computing, Linear algebra and Multiplication. Robert A. van de Geijn has researched Matrix multiplication in several fields, including Upper and lower bounds, Overhead and Combinatorics. He combines subjects such as Distributed memory, QR decomposition, Givens rotation, Strassen algorithm and Speedup with his study of Algorithm.

His Parallel computing study integrates concerns from other disciplines, such as Scalability, Eigenvalues and eigenvectors, Eigendecomposition of a matrix and Singular value decomposition. The Linear algebra study combines topics in areas such as Domain, Basic Linear Algebra Subprograms, Software framework, LU decomposition and Software. His Multiplication study combines topics in areas such as Matrix, Computation, Polygon mesh and Parallel processing.

- BLIS: A Framework for Rapidly Instantiating BLAS Functionality (152 citations)
- Anatomy of High-Performance Many-Threaded Matrix Multiplication (72 citations)
- The BLIS Framework: Experiments in Portability (52 citations)

- Operating system
- Programming language
- Parallel computing

Parallel computing, Linear algebra, Algorithm, Matrix and Matrix multiplication are his primary areas of study. His Linear algebra research is multidisciplinary, relying on both Programming language, Software framework, Basic Linear Algebra Subprograms and Speedup. His biological study spans a wide range of topics, including Distributed memory, Xeon and Strassen algorithm.

His work in Algorithm covers topics such as QR decomposition which are related to areas like Function, Randomized algorithm and Blocking. His Matrix research incorporates elements of Task parallelism, Multiplication, Computation and Overhead. His studies examine the connections between Matrix multiplication and genetics, as well as such issues in Scalability, with regards to Multi-core processor, PowerPC, Porting and Xeon Phi.

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.

Anatomy of high-performance matrix multiplication

Kazushige Goto;Robert A. van de Geijn.

ACM Transactions on Mathematical Software **(2008)**

785 Citations

SUMMA: Scalable Universal Matrix Multiplication Algorithm

Robert A. van de Geijn;Jerrell Watts.

Concurrency and Computation: Practice and Experience **(1995)**

626 Citations

High-performance implementation of the level-3 BLAS

Kazushige Goto;Robert Van De Geijn.

ACM Transactions on Mathematical Software **(2008)**

403 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

Using PLAPACK: parallel linear algebra package

Robert A. van de Geijn.

**(1997)**

323 Citations

Elemental: A New Framework for Distributed Memory Dense Matrix Computations

Jack Poulson;Bryan Marker;Robert A. van de Geijn;Jeff R. Hammond.

ACM Transactions on Mathematical Software **(2013)**

287 Citations

Collective communication: theory, practice, and experience

Ernie Chan;Marcel Heimlich;Avi Purkayastha;Robert A. van de Geijn.

Concurrency and Computation: Practice and Experience **(2007)**

262 Citations

BLIS: A Framework for Rapidly Instantiating BLAS Functionality

Field G. Van Zee;Robert A. van de Geijn.

ACM Transactions on Mathematical Software **(2015)**

227 Citations

A fast solution method for three‐dimensional many‐particle problems of linear elasticity

Yuhong Fu;Kenneth J. Klimkowski;Gregory J. Rodin;Emery Berger.

International Journal for Numerical Methods in Engineering **(1998)**

217 Citations

The science of deriving dense linear algebra algorithms

Paolo Bientinesi;John A. Gunnels;Margaret E. Myers;Enrique S. Quintana-Ortí.

ACM Transactions on Mathematical Software **(2005)**

212 Citations

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Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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