What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Parallel computing
The scientist’s investigation covers issues in Parallel computing, Linear algebra, Iterative refinement, HPC Challenge Benchmark and Supercomputer.
His studies in Parallel computing integrate themes in fields like Scalability and Matrix multiplication.
His Linear algebra research includes themes of Hybrid system, Algorithm, Numerical linear algebra and Cholesky decomposition.
His study in Iterative refinement is interdisciplinary in nature, drawing from both Floating point, Double-precision floating-point format and Single-precision floating-point format.
His HPC Challenge Benchmark study integrates concerns from other disciplines, such as Petascale computing, TOP500 and Locality of reference.
His Supercomputer research is multidisciplinary, incorporating elements of Programming language, Task and Distributed memory systems.
His most cited work include:
- The LINPACK Benchmark: past, present and future (556 citations)
- Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects (353 citations)
- The HPC Challenge (HPCC) benchmark suite (252 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Parallel computing, Linear algebra, Multi-core processor, Software and Numerical linear algebra.
His study of CUDA is a part of Parallel computing.
His Linear algebra study also includes
- Computation most often made with reference to Computational science,
- Supercomputer together with Benchmark.
His work in Multi-core processor addresses subjects such as Factorization, which are connected to disciplines such as System of linear equations.
His Software study combines topics from a wide range of disciplines, such as Numerical analysis and Software engineering.
As part of one scientific family, Piotr Luszczek deals mainly with the area of LU decomposition, narrowing it down to issues related to the Pivot element, and often Gaussian elimination.
He most often published in these fields:
- Parallel computing (55.44%)
- Linear algebra (29.53%)
- Multi-core processor (23.32%)
What were the highlights of his more recent work (between 2015-2021)?
- Parallel computing (55.44%)
- Linear algebra (29.53%)
- Multi-core processor (23.32%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Parallel computing, Linear algebra, Multi-core processor, Software and Supercomputer.
His Parallel computing study combines topics in areas such as Scalability, Programming paradigm and Computational science.
His Linear algebra research integrates issues from Xeon Phi, Scheduling and Matrix, Cholesky decomposition.
His Multi-core processor research includes elements of Coprocessor, Computer engineering and Generalized minimal residual method.
His studies deal with areas such as Data type, Singular value decomposition, Solver and Profiling as well as Software.
His Supercomputer research is multidisciplinary, relying on both Machine learning, Ranking, Software engineering and Arithmetic.
Between 2015 and 2021, his most popular works were:
- High-performance conjugate-gradient benchmark (68 citations)
- The Singular Value Decomposition: Anatomy of Optimizing an Algorithm for Extreme Scale (34 citations)
- Porting the PLASMA Numerical Library to the OpenMP Standard (20 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Central processing unit
Piotr Luszczek mostly deals with Parallel computing, Linear algebra, Multi-core processor, Computation and Computational science.
His research integrates issues of Artificial neural network and Software portability in his study of Parallel computing.
The concepts of his Linear algebra study are interwoven with issues in Xeon Phi, Matrix, Cholesky decomposition, Porting and Scheduling.
His Multi-core processor research incorporates themes from Multithreading, Coprocessor, Theory of computation and Programming paradigm.
His Computation study combines topics in areas such as Divide and conquer algorithms, Pipeline, Singular value decomposition, Instruction set and Generalized minimal residual method.
His studies deal with areas such as Floating point, IEEE floating point, Computer engineering and Benchmark as well as Computational science.
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