What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Parallel computing
His primary scientific interests are in Parallel computing, Linear algebra, Distributed computing, Software and Supercomputer.
Jack Dongarra works on Parallel computing which deals in particular with Distributed memory.
His research integrates issues of Linear system, Numerical linear algebra, Basic Linear Algebra Subprograms and Algorithm, Iterative refinement in his study of Linear algebra.
His Distributed computing research focuses on Grid computing and how it connects with Suspension.
His Software research focuses on Computational science and how it relates to Graphics.
His Supercomputer research is multidisciplinary, incorporating elements of Computer architecture and Concurrent computing.
His most cited work include:
- MPI: The Complete Reference (2132 citations)
- PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing (1641 citations)
- A set of level 3 basic linear algebra subprograms (1540 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Parallel computing, Linear algebra, Distributed computing, Supercomputer and Software.
Jack Dongarra is studying Multi-core processor, which is a component of Parallel computing.
His Multi-core processor study combines topics from a wide range of disciplines, such as QR decomposition, CUDA and Shared memory.
Jack Dongarra combines subjects such as Linear system, Numerical linear algebra, Distributed memory and Computational science with his study of Linear algebra.
His research in Distributed computing intersects with topics in Grid, Grid computing and Scheduling.
His studies in Matrix integrate themes in fields like Algorithm and Eigenvalues and eigenvectors.
He most often published in these fields:
- Parallel computing (54.61%)
- Linear algebra (25.67%)
- Distributed computing (17.35%)
What were the highlights of his more recent work (between 2015-2021)?
- Parallel computing (54.61%)
- Matrix (20.51%)
- Linear algebra (25.67%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Parallel computing, Matrix, Linear algebra, Computational science and Linear system.
His primary area of study in Parallel computing is in the field of Multi-core processor.
He has researched Matrix in several fields, including Factorization, Algorithm, Multiplication and Preconditioner.
His Linear algebra study integrates concerns from other disciplines, such as Xeon Phi, Software, Porting, Vectorization and Numerical linear algebra.
His research in Software focuses on subjects like Supercomputer, which are connected to Distributed computing.
His research investigates the link between Computational science and topics such as Energy consumption that cross with problems in Coprocessor.
Between 2015 and 2021, his most popular works were:
- High-performance conjugate-gradient benchmark (68 citations)
- Performance, Design, and Autotuning of Batched GEMM for GPUs (61 citations)
- Performance, Design, and Autotuning of Batched GEMM for GPUs (61 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Algorithm
Jack Dongarra spends much of his time researching Parallel computing, Matrix, Linear system, Linear algebra and Computational science.
He conducts interdisciplinary study in the fields of Parallel computing and Kernel through his works.
His Linear system research is multidisciplinary, relying on both Positive-definite matrix and Algorithm.
Jack Dongarra interconnects Scheduling, Matrix multiplication and Kernel in the investigation of issues within Linear algebra.
His Computational science study also includes
- Iterative refinement, which have a strong connection to Coprocessor,
- Xeon Phi and LOBPCG most often made with reference to Efficient energy use,
- Distributed computing most often made with reference to CUDA.
His study looks at the relationship between Cholesky decomposition and fields such as Software, as well as how they intersect with chemical problems.
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.
