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“
Bringing High Performance Computing to Big Data Algorithms,” Handbook of Big Data Technologies: Springer, 2017. DOI: 10.1007/978-3-319-49340-4“
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.“
Accelerating Eigenvector Computation in the Nonsymmetric Eigenvalue Problem,” VECPAR 2014, Eugene, OR, June 2014.“
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“
clMAGMA: High Performance Dense Linear Algebra with OpenCL ,” International Workshop on OpenCL, Bristol University, England, May 2014.“
Comparing Hybrid CPU-GPU and Native GPU-only Acceleration for Linear Algebra,” 2015 SIAM Conference on Applied Linear Algebra, Atlanta, GA, SIAM, October 2015.“
Heterogeneous Streaming,” The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.“
Massively Parallel Automated Software Tuning,” 48th International Conference on Parallel Processing (ICPP 2019), Kyoto, Japan, ACM Press, August 2019. DOI: 10.1145/3337821.3337908“
Performance and Portability with OpenCL for Throughput-Oriented HPC Workloads Across Accelerators, Coprocessors, and Multicore Processors,” 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '14), New Orleans, LA, IEEE, November 2014. DOI: 10.1109/ScalA.2014.8“
Portable HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi,” PPAM 2013, Warsaw, Poland, September 2013.“
Search Space Generation and Pruning System for Autotuners,” 30th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Chicago, IL, IEEE, May 2016.“
SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library,” International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Denver, CO, ACM, November 2019. DOI: 10.1145/3295500.3356223“
Toward a scalable multi-GPU eigensolver via compute-intensive kernels and efficient communication,” Proceedings of the 27th ACM International Conference on Supercomputing (ICS '13), Eugene, Oregon, USA, ACM Press, June 2013. DOI: 10.1145/2464996.2465438“
Least Squares Solvers for Distributed-Memory Machines with GPU Accelerators,” ACM International Conference on Supercomputing (ICS '19), Phoenix, Arizona, ACM, pp. 117–126, June 2019. DOI: 10.1145/3324989.3325719“
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“
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“
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.
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“
HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi,” Scientific Programming, vol. 23, issue 1, January 2015. DOI: 10.3233/SPR-140404“
Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs,” IEEE Transactions on Parallel and Distributed Systems, no. 1045-9219, November 2015.“
Linear Algebra Software for Large-Scale Accelerated Multicore Computing,” Acta Numerica, vol. 25, pp. 1-160, May 2016. DOI: 10.1017/S0962492916000015“
A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks,” International Journal of High Performance Computing Applications, vol. 28, issue 2, pp. 196-209, May 2014. DOI: 10.1177/1094342013502097“
Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems,” Supercomputing Frontiers and Innovations, vol. 2, no. 4, October 2015. DOI: 10.14529/jsfi1504“
PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP,” ACM Transactions on Mathematical Software, vol. 45, issue 2, June 2019. DOI: 10.1145/3264491“
Preconditioned Krylov Solvers on GPUs,” Parallel Computing, June 2017. DOI: 10.1016/j.parco.2017.05.006“
The Singular Value Decomposition: Anatomy of Optimizing an Algorithm for Extreme Scale,” SIAM Review, vol. 60, issue 4, pp. 808–865, November 2018. DOI: 10.1137/17M1117732“
A Survey of Recent Developments in Parallel Implementations of Gaussian Elimination,” Concurrency and Computation: Practice and Experience, vol. 27, issue 5, pp. 1292-1309, April 2015. DOI: 10.1002/cpe.3306“
Accelerating Linear Algebra with MAGMA , Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
MAGMA: A New Generation of Linear Algebra Library for GPU and Multicore Architectures , Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), Presentation, November 2012.
MAGMA MIC: Linear Algebra Library for Intel Xeon Phi Coprocessors , Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), November 2012.
MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi , Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
MAGMA Tutorial , Atlanta, GA, Keeneland Workshop, February 2012.
SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library , Denver, CO, International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), November 2019.
SLATE Tutorial , Houston, TX, 2020 ECP Annual Meeting, February 2020.
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.“
C++ API for Batch BLAS,” SLATE Working Notes, no. 4, ICL-UT-17-12: University of Tennessee, December 2017.“
C++ API for BLAS and LAPACK,” SLATE Working Notes, no. 2, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 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.“
Designing SLATE: Software for Linear Algebra Targeting Exascale,” SLATE Working Notes, no. 3, ICL-UT-17-06: Innovative Computing Laboratory, University of Tennessee, October 2017.“
Implementation of the C++ API for Batch BLAS,” SLATE Working Notes, no. 7, ICL-UT-18-04: Innovative Computing Laboratory, University of Tennessee, June 2018.“
Least Squares Performance Report,” SLATE Working Notes, no. 9, ICL-UT-18-10: Innovative Computing Laboratory, University of Tennessee, December 2018.“
Linear Systems Performance Report,” SLATE Working Notes, no. 8, ICL-UT-18-08: Innovative Computing Laboratory, University of Tennessee, September 2018.“
MAGMA-sparse Interface Design Whitepaper,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.“
Parallel BLAS Performance Report,” SLATE Working Notes, no. 5, ICL-UT-18-01: University of Tennessee, April 2018.“
Parallel Norms Performance Report,” SLATE Working Notes, no. 6, ICL-UT-18-06: Innovative Computing Laboratory, University of Tennessee, June 2018.“