Publications
Export 193 results:
Filters: Author is Piotr Luszczek [Clear All Filters]
Accelerating Restarted GMRES with Mixed Precision Arithmetic,”
IEEE Transactions on Parallel and Distributed Systems, June 2021.
DOI: 10.1109/TPDS.2021.3090757
(572.4 KB)
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Accelerating Scientific Computations with Mixed Precision Algorithms,”
Computer Physics Communications, vol. 180, issue 12, pp. 2526-2533, December 2009.
DOI: 10.1016/j.cpc.2008.11.005
(402.69 KB)
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Achieving numerical accuracy and high performance using recursive tile LU factorization with partial pivoting,”
Concurrency and Computation: Practice and Experience, vol. 26, issue 7, pp. 1408-1431, May 2014.
DOI: 10.1002/cpe.3110
(1.96 MB)
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Achieving Numerical Accuracy and High Performance using Recursive Tile LU Factorization,”
University of Tennessee Computer Science Technical Report (also as a LAWN), no. ICL-UT-11-08, September 2011.
(618.53 KB)
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Analysis of the Communication and Computation Cost of FFT Libraries towards Exascale,”
ICL Technical Report, no. ICL-UT-22-07: Innovative Computing Laboratory, July 2022.
(5.91 MB)
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Autotuning Batch Cholesky Factorization in CUDA with Interleaved Layout of Matrices,”
Parallel and Distributed Processing Symposium Workshops (IPDPSW), Orlando, FL, IEEE, June 2017.
DOI: 10.1109/IPDPSW.2017.18
“Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators,”
Proceedings of the IEEE, vol. 106, issue 11, pp. 2040–2055, November 2018.
DOI: 10.1109/JPROC.2018.2868961
(2.53 MB)
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Autotuning Techniques for Performance-Portable Point Set Registration in 3D,”
Supercomputing Frontiers and Innovations, vol. 5, no. 4, December 2018.
DOI: 10.14529/jsfi180404
(720.15 KB)
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Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
, July 2018.
(483.05 KB)

Bringing High Performance Computing to Big Data Algorithms,”
Handbook of Big Data Technologies: Springer, 2017.
DOI: 10.1007/978-3-319-49340-4
(1.22 MB)
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C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB)
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C++ API for BLAS and LAPACK,”
SLATE Working Notes, no. 02, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.
(1.12 MB)
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The Case for Directive Programming for Accelerator Autotuner Optimization,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-07: University of Tennessee, October 2017.
(341.52 KB)
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CholeskyQR with Randomization and Pivoting for Tall Matrices (CQRRPT)
: arXiv, February 2024.
Clover: Computational Libraries Optimized via Exascale Research
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(872 KB)

A Collection of White Papers from the BDEC2 Workshop in Poznan, Poland,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-10: University of Tennessee, Knoxville, May 2019.
(5.82 MB)
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Communication Avoiding 2D Stencil Implementations over PaRSEC Task-Based Runtime,”
2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), New Orleans, LA, IEEE, May 2020.
DOI: 10.1109/IPDPSW50202.2020.00127
(1.33 MB)
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Dense Linear Algebra on Distributed Heterogeneous Hardware with a Symbolic DAG Approach,”
Scalable Computing and Communications: Theory and Practice: John Wiley & Sons, pp. 699-735, March 2013.
(1.01 MB)
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Design and Implementation of the PULSAR Programming System for Large Scale Computing,”
Supercomputing Frontiers and Innovations, vol. 4, issue 1, 2017.
DOI: 10.14529/jsfi170101
(764.96 KB)
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Designing SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 03, ICL-UT-17-06: Innovative Computing Laboratory, University of Tennessee, October 2017.
(2.8 MB)
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Distributed Dense Numerical Linear Algebra Algorithms on Massively Parallel Architectures: DPLASMA,”
University of Tennessee Computer Science Technical Report, UT-CS-10-660, September 2010.
(366.26 KB)
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Distributed-Memory Task Execution and Dependence Tracking within DAGuE and the DPLASMA Project,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-10-02, 00 2010.
(400.75 KB)
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Docker Container based PaaS Cloud Computing Comprehensive Benchmarks using LAPACK,”
Computer Modeling and Intelligent Systems CMIS-2020, Zaporizhzhoa, March 2020.
(451.33 KB)
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