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.
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.
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.
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.
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Anatomy of high-performance matrix multiplication
Kazushige Goto;Robert A. van de Geijn.
ACM Transactions on Mathematical Software (2008)
SUMMA: Scalable Universal Matrix Multiplication Algorithm
Robert A. van de Geijn;Jerrell Watts.
Concurrency and Computation: Practice and Experience (1995)
High-performance implementation of the level-3 BLAS
Kazushige Goto;Robert Van De Geijn.
ACM Transactions on Mathematical Software (2008)
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)
Using PLAPACK: parallel linear algebra package
Robert A. van de Geijn.
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)
Collective communication: theory, practice, and experience
Ernie Chan;Marcel Heimlich;Avi Purkayastha;Robert A. van de Geijn.
Concurrency and Computation: Practice and Experience (2007)
BLIS: A Framework for Rapidly Instantiating BLAS Functionality
Field G. Van Zee;Robert A. van de Geijn.
ACM Transactions on Mathematical Software (2015)
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)
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)
Profile was last updated on December 6th, 2021.
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