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 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.
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.
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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Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects
Emmanuel Agullo;Jim Demmel;Jack Dongarra;Bilel Hadri.
Journal of Physics: Conference Series (2009)
Towards dense linear algebra for hybrid GPU accelerated manycore systems
Stanimire Tomov;Jack Dongarra;Marc Baboulin.
parallel computing (2010)
From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming
Peng Du;Rick Weber;Piotr Luszczek;Stanimire Tomov.
parallel computing (2012)
Dense linear algebra solvers for multicore with GPU accelerators
Stanimire Tomov;Rajib Nath;Hatem Ltaief;Jack Dongarra.
ieee international symposium on parallel distributed processing workshops and phd forum (2010)
An Improved Magma Gemm For Fermi Graphics Processing Units
Rajib Nath;Stanimire Tomov;Jack Dongarra.
ieee international conference on high performance computing data and analytics (2010)
Accelerating Scientific Computations with Mixed Precision Algorithms
Marc Baboulin;Alfredo Buttari;Jack J. Dongarra;Jack J. Dongarra;Jack J. Dongarra;Jakub Kurzak.
Computer Physics Communications (2009)
A Note on Auto-tuning GEMM for GPUs
Yinan Li;Jack Dongarra;Stanimire Tomov.
international conference on computational science (2009)
The impact of multicore on math software
Alfredo Buttari;Jack Dongarra;Jakub Kurzak;Julien Langou.
parallel computing (2006)
Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixed-precision iterative refinement solvers
Azzam Haidar;Stanimire Tomov;Jack Dongarra;Nicholas J. Higham.
ieee international conference on high performance computing data and analytics (2018)
Autotuning GEMM Kernels for the Fermi GPU
J. Kurzak;S. Tomov;J. Dongarra.
IEEE Transactions on Parallel and Distributed Systems (2012)
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