- Home
- Best Scientists - Computer Science
- Piotr Luszczek

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
30
Citations
6,291
144
World Ranking
8545
National Ranking
3937

- 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.

- 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)

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.

- Parallel computing (55.44%)
- Linear algebra (29.53%)
- Multi-core processor (23.32%)

- Parallel computing (55.44%)
- Linear algebra (29.53%)
- Multi-core processor (23.32%)

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.

- 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)

- 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.

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.

The LINPACK Benchmark: past, present and future

Jack J. Dongarra;Piotr Luszczek;Antoine Petitet.

Concurrency and Computation: Practice and Experience **(2003)**

777 Citations

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)**

482 Citations

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)**

437 Citations

The HPC Challenge (HPCC) benchmark suite

Piotr R Luszczek;David H Bailey;Jack J Dongarra;Jeremy Kepner.

conference on high performance computing (supercomputing) **(2006)**

293 Citations

Introduction to the HPC Challenge Benchmark Suite

Piotr Luszczek;Jack J. Dongarra;David Koester;Rolf Rabenseifner.

SC2005, Seattle, WA, Nov 12-18,2005 **(2005)**

237 Citations

Measuring Energy and Power with PAPI

Vincent M. Weaver;Matt Johnson;Kiran Kasichayanula;James Ralph.

international conference on parallel processing **(2012)**

232 Citations

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)**

208 Citations

The impact of multicore on math software

Alfredo Buttari;Jack Dongarra;Jakub Kurzak;Julien Langou.

parallel computing **(2006)**

179 Citations

Exploiting the performance of 32 bit floating point arithmetic in obtaining 64 bit accuracy (revisiting iterative refinement for linear systems)

Julie Langou;Julien Langou;Piotr Luszczek;Jakub Kurzak.

conference on high performance computing (supercomputing) **(2006)**

177 Citations

Mixed Precision Iterative Refinement Techniques for the Solution of Dense Linear Systems

Alfredo Buttari;Jack Dongarra;Julie Langou;Julien Langou.

ieee international conference on high performance computing data and analytics **(2007)**

157 Citations

University of Tennessee at Knoxville

University of Tennessee at Knoxville

University of Tennessee at Knoxville

Oak Ridge National Laboratory

University of California, Berkeley

École Normale Supérieure de Lyon

University of Manchester

Georgia Institute of Technology

Lawrence Berkeley National Laboratory

MIT

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Something went wrong. Please try again later.