What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Parallel computing (49.67%)
- Linear algebra (47.06%)
- Algorithm (32.68%)
What were the highlights of his more recent work (between 2013-2021)?
- Matrix multiplication (20.92%)
- Algorithm (32.68%)
- Parallel computing (49.67%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
