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Mixed Precision Iterative Refinement Techniques for the Solution of Dense Linear Systems,” International Journal of High Performance Computer Applications (to appear), August 2007.“
Model-Driven One-Sided Factorizations on Multicore, Accelerated Systems,” Supercomputing Frontiers and Innovations, vol. 1, issue 1, 2014. DOI: http://dx.doi.org/10.14529/jsfi1401“
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PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP,” ACM Transactions on Mathematical Software, vol. 45, issue 2, June 2019. DOI: 10.1145/3264491“
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Porting the PLASMA Numerical Library to the OpenMP Standard,” International Journal of Parallel Programming, June 2016. DOI: 10.1007/s10766-016-0441-6“
Power Aware Computing on GPUs,” SAAHPC '12 (Best Paper Award), Argonne, IL, July 2012.“
Preliminary Results of Autotuning GEMM Kernels for the NVIDIA Kepler Architecture,” LAWN 267, 00 2012.“
Prospectus for the Next LAPACK and ScaLAPACK Libraries,” PARA 2006, Umea, Sweden, June 2006.“
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A Set of Batched Basic Linear Algebra Subprograms,” ACM Transactions on Mathematical Software, October 2020.“
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Soft Error Resilient QR Factorization for Hybrid System with GPGPU,” Journal of Computational Science, Seattle, WA, Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems at SC11, November 2011.“
Soft Error Resilient QR Factorization for Hybrid System with GPGPU,” Journal of Computational Science, vol. 4, issue 6, pp. 457–464, November 2013. DOI: http://dx.doi.org/10.1016/j.jocs.2013.01.004“
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MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi , Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
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On Algorithmic Variants of Parallel Gaussian Elimination: Comparison of Implementations in Terms of Performance and Numerical Properties,” University of Tennessee Computer Science Technical Report, no. UT-CS-13-715, July 2013, 2012.“
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C++ API for Batch BLAS,” SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.“
C++ API for BLAS and LAPACK,” SLATE Working Notes, no. 02, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.“
The Case for Directive Programming for Accelerator Autotuner Optimization,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-07: University of Tennessee, October 2017.“
clMAGMA: High Performance Dense Linear Algebra with OpenCL,” University of Tennessee Technical Report (Lawn 275), no. UT-CS-13-706: University of Tennessee, March 2013.“
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.“
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.“
Distributed Dense Numerical Linear Algebra Algorithms on Massively Parallel Architectures: DPLASMA,” University of Tennessee Computer Science Technical Report, UT-CS-10-660, September 2010.“
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.“
GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,” University of Tennessee Computer Science Technical Report UT-CS-11-690 (also Lawn 260), December 2011.“
High Performance Bidiagonal Reduction using Tile Algorithms on Homogeneous Multicore Architectures,” University of Tennessee Computer Science Technical Report, UT-CS-11-673, (also Lawn 247), May 2011.“
HPCG Benchmark: a New Metric for Ranking High Performance Computing Systems,” University of Tennessee Computer Science Technical Report , no. ut-eecs-15-736: University of Tennessee, January 2015.“