Publications
High-Performance GPU Implementation of PageRank with Reduced Precision based on Mantissa Segmentation,”
8th Workshop on Irregular Applications: Architectures and Algorithms, 2018.
“Incomplete Sparse Approximate Inverses for Parallel Preconditioning,”
Parallel Computing, vol. 71, pp. 1–22, January 2018.
DOI: 10.1016/j.parco.2017.10.003
(1.24 MB)
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A Jaccard Weights Kernel Leveraging Independent Thread Scheduling on GPUs,”
SBAC-PAD, Lyon, France, IEEE, 2018.
(237.68 KB)
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Optimization and Performance Evaluation of the IDR Iterative Krylov Solver on GPUs,”
The International Journal of High Performance Computing Applications, vol. 32, no. 2, pp. 220–230, March 2018.
DOI: 10.1177/1094342016646844
(2.08 MB)
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ParILUT - A New Parallel Threshold ILU,”
SIAM Journal on Scientific Computing, vol. 40, issue 4: SIAM, pp. C503–C519, July 2018.
DOI: 10.1137/16M1079506
(19.26 MB)
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Software-Defined Events (SDEs) in MAGMA-Sparse,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-12: University of Tennessee, December 2018.
(481.69 KB)
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Solver Interface & Performance on Cori,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-05: University of Tennessee, June 2018.
(188.05 KB)
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Using Jacobi Iterations and Blocking for Solving Sparse Triangular Systems in Incomplete Factorization Preconditioning,”
Journal of Parallel and Distributed Computing, vol. 119, pp. 219–230, November 2018.
DOI: 10.1016/j.jpdc.2018.04.017
(273.53 KB)
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Variable-Size Batched Condition Number Calculation on GPUs,”
SBAC-PAD, Lyon, France, September 2018.
(509.3 KB)
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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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Flexible Batched Sparse Matrix Vector Product on GPUs
, Denver, Colorado, ScalA'17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, November 2017.
(16.8 MB)

Flexible Batched Sparse Matrix-Vector Product on GPUs,”
8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17), Denver, CO, ACM Press, November 2017.
DOI: http://dx.doi.org/10.1145/3148226.3148230
(583.4 KB)
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Preconditioned Krylov Solvers on GPUs,”
Parallel Computing, June 2017.
DOI: 10.1016/j.parco.2017.05.006
(1.19 MB)
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Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 01, ICL-UT-17-02: Innovative Computing Laboratory, University of Tennessee, June 2017.
(2.8 MB)
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Variable-Size Batched Gauss-Huard for Block-Jacobi Preconditioning,”
International Conference on Computational Science (ICCS 2017), vol. 108, Zurich, Switzerland, Procedia Computer Science, pp. 1783-1792, June 2017.
DOI: 10.1016/j.procs.2017.05.186
(512.57 KB)
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Variable-Size Batched LU for Small Matrices and Its Integration into Block-Jacobi Preconditioning,”
46th International Conference on Parallel Processing (ICPP), Bristol, United Kingdom, IEEE, August 2017.
DOI: 10.1109/ICPP.2017.18
“With Extreme Computing, the Rules Have Changed,”
Computing in Science & Engineering, vol. 19, issue 3, pp. 52-62, May 2017.
DOI: 10.1109/MCSE.2017.48
(485.34 KB)
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Fine-grained Bit-Flip Protection for Relaxation Methods,”
Journal of Computational Science, November 2016.
DOI: 10.1016/j.jocs.2016.11.013
(1.47 MB)
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Heterogeneous Streaming,”
The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.
(2.73 MB)
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Linear Algebra Software for Large-Scale Accelerated Multicore Computing,”
Acta Numerica, vol. 25, pp. 1-160, May 2016.
DOI: 10.1017/S0962492916000015
“On the performance and energy efficiency of sparse linear algebra on GPUs,”
International Journal of High Performance Computing Applications, October 2016.
DOI: 10.1177/1094342016672081
(1.19 MB)
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MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi
, Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
(2.03 MB)
