What is he best known for?
The fields of study he is best known for:
- Operating system
- Algorithm
- Quantum mechanics
His primary areas of study are Parallel computing, CUDA, Linear algebra, Multi-core processor and Computational science.
His work carried out in the field of Parallel computing brings together such families of science as Multiplication, Hybrid system and Cholesky decomposition.
His CUDA research includes elements of Supercomputer, Matrix multiplication, Coprocessor and Double-precision floating-point format.
His Linear algebra research incorporates themes from Magma, Matrix, Numerical linear algebra, Iterative refinement and Computation.
His Multi-core processor research is multidisciplinary, incorporating elements of Software and Tridiagonal matrix.
His research in Computational science tackles topics such as Graphics which are related to areas like Software portability.
His most cited work include:
- Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects (353 citations)
- Towards dense linear algebra for hybrid GPU accelerated manycore systems (305 citations)
- From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming (250 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, Multi-core processor, Computational science and CUDA.
His studies deal with areas such as Matrix, Sparse matrix, Cholesky decomposition and Solver as well as Parallel computing.
His Linear algebra research focuses on Matrix multiplication and how it connects with Multiplication.
As a part of the same scientific family, Stanimire Tomov mostly works in the field of Multi-core processor, focusing on Scalability and, on occasion, Massively parallel.
His Computational science research is multidisciplinary, incorporating perspectives in Mixed precision, Computation, Linear system, Tensor and Iterative refinement.
His studies in CUDA integrate themes in fields like Block, Supercomputer and LU decomposition.
He most often published in these fields:
- Parallel computing (58.74%)
- Linear algebra (26.21%)
- Multi-core processor (24.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Parallel computing (58.74%)
- Computational science (21.36%)
- Supercomputer (7.77%)
In recent papers he was focusing on the following fields of study:
Stanimire Tomov spends much of his time researching Parallel computing, Computational science, Supercomputer, Fast Fourier transform and Linear algebra.
His Parallel computing study integrates concerns from other disciplines, such as Sparse matrix and Singular value decomposition.
His Computational science research includes themes of Linear system, Generalized minimal residual method, Tensor, Iterative refinement and Multiplication.
He has included themes like Power, Electrical efficiency, Xeon Phi, Software and Efficient energy use in his Supercomputer study.
His Fast Fourier transform research incorporates elements of Exascale computing, Scalability and Phase.
Stanimire Tomov combines subjects such as Matrix, Numerical linear algebra, Matrix multiplication, Arithmetic and General-purpose computing on graphics processing units with his study of Linear algebra.
Between 2018 and 2021, his most popular works were:
- Fast Batched Matrix Multiplication for Small Sizes Using Half-Precision Arithmetic on GPUs (16 citations)
- Investigating power capping toward energy‐efficient scientific applications (16 citations)
- A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic (10 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Algorithm
- Programming language
His primary areas of study are Parallel computing, Matrix, Data access, Supercomputer and General-purpose computing on graphics processing units.
Many of his studies involve connections with topics such as Fast Fourier transform and Parallel computing.
His Matrix research integrates issues from Load balancing, Kernel, SIMD and Linear algebra.
His Data access study combines topics from a wide range of disciplines, such as Divide and conquer algorithms, Singular value decomposition, Function, Out-of-core algorithm and Mature technology.
His work carried out in the field of Supercomputer brings together such families of science as Power, Electrical efficiency, Xeon Phi and Efficient energy use, Electrical engineering.
As part of the same scientific family, Stanimire Tomov usually focuses on General-purpose computing on graphics processing units, concentrating on LU decomposition and intersecting with Generalized minimal residual method, Iterative refinement and Computational science.
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