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Computational Benefit of GPU Optimization for Atmospheric Chemistry Modeling,” Journal of Advances in Modeling Earth Systems, vol. 10, issue 8, pp. 1952–1969, August 2018. DOI: 10.1029/2018MS001276“
Comparing Hybrid CPU-GPU and Native GPU-only Acceleration for Linear Algebra,” 2015 SIAM Conference on Applied Linear Algebra, Atlanta, GA, SIAM, October 2015.“
Clover: Computational Libraries Optimized via Exascale Research , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
clMAGMA: High Performance Dense Linear Algebra with OpenCL ,” International Workshop on OpenCL, Bristol University, England, May 2014.“
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.“
C++ API for BLAS and LAPACK,” SLATE Working Notes, no. 02, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.“
C++ API for Batch BLAS,” SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.“
Bringing High Performance Computing to Big Data Algorithms,” Handbook of Big Data Technologies: Springer, 2017. DOI: 10.1007/978-3-319-49340-4“
Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,” ICCS 2012, Omaha, NE, June 2012.“
Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems , no. UT-CS-11-689, December 2011.
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“
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“
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.“
Accelerating the SVD Two Stage Bidiagonal Reduction and Divide and Conquer Using GPUs,” Parallel Computing, vol. 74, pp. 3–18, May 2018. DOI: 10.1016/j.parco.2017.10.004“
Accelerating Numerical Dense Linear Algebra Calculations with GPUs,” Numerical Computations with GPUs: Springer International Publishing, pp. 3-28, 2014. DOI: 10.1007/978-3-319-06548-9_1“
Accelerating Linear Algebra with MAGMA , Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
Accelerating Eigenvector Computation in the Nonsymmetric Eigenvalue Problem,” VECPAR 2014, Eugene, OR, June 2014.“
Accelerating Collaborative Filtering for Implicit Feedback Datasets using GPUs,” 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, IEEE, November 2015.“