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Accelerating the SVD Bi-Diagonalization of a Batch of Small Matrices using GPUs,” Journal of Computational Science, vol. 26, pp. 237–245, May 2018. DOI: 10.1016/j.jocs.2018.01.007“
Accelerating the Reduction to Upper Hessenberg, Tridiagonal, and Bidiagonal Forms through Hybrid GPU-Based Computing,” Parallel Computing, vol. 36, no. 12, pp. 645-654, 00 2010.“
Accelerating the Reduction to Upper Hessenberg Form through Hybrid GPU-Based Computing,” University of Tennessee Computer Science Technical Report, UT-CS-09-642 (also LAPACK Working Note 219), May 2009.“
Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,” Spring Simulation Multi-Conference 2015 (SpringSim'15), Alexandria, VA, SCS, April 2015.“
Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,” University of Tennessee Computer Science Technical Report, no. UT-EECS-14-731: University of Tennessee, October 2014.“
Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched , Atlanta, GA, SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation, March 2017.
Accelerating Tensor Contractions for High-Order FEM on CPUs, GPUs, and KNLs , Gatlinburg, TN, moky Mountains Computational Sciences and Engineering Conference (SMC16), Poster, September 2016.
Accelerating NWChem Coupled Cluster through dataflow-based Execution,” The International Journal of High Performance Computing Applications, vol. 32, issue 4, pp. 540--551, July 2018. DOI: 10.1177/1094342016672543“
Accelerating NWChem Coupled Cluster through dataflow-based Execution,” 11th International Conference on Parallel Processing and Applied Mathematics (PPAM 2015), Krakow, Poland, Springer International Publishing, September 2015.“
Accelerating NWChem Coupled Cluster through Dataflow-Based Execution,” The International Journal of High Performance Computing Applications, pp. 1–13, January 2017. DOI: 10.1177/1094342016672543“
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 System Solutions Using Randomization Techniques,” INRIA RR-7616 / LAWN #246 (presented at International AMMCS’11), Waterloo, Ontario, Canada, July 2011.“
Accelerating Linear System Solutions Using Randomization Techniques,” ACM Transactions on Mathematical Software (also LAWN 246), vol. 39, issue 2, February 2013. DOI: 10.1145/2427023.2427025“
Accelerating GPU Kernels for Dense Linear Algebra,” Proc. of VECPAR'10, Berkeley, CA, June 2010.“
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.“
“8th International Conference on Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science (LNCS),” PPAM 2009 Proceedings, vol. 6067, Wroclaw, Poland, Springer, September 2010.
The 30th Anniversary of the Supercomputing Conference: Bringing the Future Closer—Supercomputing History and the Immortality of Now,” Computer, vol. 51, issue 10, pp. 74–85, November 2018. DOI: 10.1109/MC.2018.3971352“
2016 Dense Linear Algebra Software Packages Survey,” University of Tennessee Computer Science Technical Report, no. UT-EECS-16-744 / LAWN 290: University of Tennessee, September 2016.“
“,” 8th International Conference on Computational Science (ICCS), Proceedings Parts I, II, and III, Lecture Notes in Computer Science, vol. 5101, Krakow, Poland, Springer Berlin, January 2008.
“,” 7th International parallel Processing and Applied Mathematics Conference, Lecture Notes in Comptuer Science, vol. 4967, Gdansk, Poland, Springer Berlin, January 2008.