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High-performance Cholesky Factorization for GPU-only Execution,” Proceedings of the General Purpose GPUs (GPGPU-10), Austin, TX, ACM, February 2017. DOI: 10.1145/3038228.3038237“
High-performance Matrix-matrix Multiplications of Very Small Matrices,” 22nd International European Conference on Parallel and Distributed Computing (Euro-Par'16), Grenoble, France, Springer International Publishing, August 2016.“
High-Performance Tensor Contractions for GPUs,” University of Tennessee Computer Science Technical Report, no. UT-EECS-16-738: University of Tennessee, January 2016.“
High-Performance Tensor Contractions for GPUs,” International Conference on Computational Science (ICCS'16), San Diego, CA, June 2016.“
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“
Hybrid Multicore Cholesky Factorization with Multiple GPU Accelerators,” IEEE Transaction on Parallel and Distributed Systems (submitted), March 2010.“
Hybrid Multi-Elimination ILU Preconditioners on GPUs,” International Heterogeneity in Computing Workshop (HCW), IPDPS 2014, Phoenix, AZ, IEEE, May 2014.“
A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs,” in GPU Computing Gems, Jade Edition, vol. 2: Elsevier, pp. 473-484, 00 2011.“
Hydrodynamic Computation with Hybrid Programming on CPU-GPU Clusters,” University of Tennessee Computer Science Technical Report, no. ut-cs-13-714, July 2013.“
The Impact of Multicore on Math Software,” PARA 2006, Umea, Sweden, June 2006.“
Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation,” Workshop on Exascale MPI (ExaMPI) at SC19, Denver, CO, November 2019.“
Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs,” University of Tennessee Computer Science Technical Report, no. UT-EECS-14-727: University of Tennessee, April 2014.“
An Improved MAGMA GEMM for Fermi GPUs,” International Journal of High Performance Computing, vol. 24, no. 4, pp. 511-515, 00 2010.“
An Improved MAGMA GEMM for Fermi GPUs,” University of Tennessee Computer Science Technical Report, no. UT-CS-10-655 (also LAPACK working note 227), July 2010.“
Improving the performance of CA-GMRES on multicores with multiple GPUs,” IPDPS 2014, Phoenix, AZ, IEEE, May 2014.“
Interior State Computation of Nano Structures,” PARA 2008, 9th International Workshop on State-of-the-Art in Scientific and Parallel Computing, Trondheim, Norway, May 2008.“
An Introduction to the MAGMA project - Acceleration of Dense Linear Algebra : NVIDIA Webinar, June 2010.
Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers,” ScalA17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Denver, CO, ACM.“
Investigating Power Capping toward Energy-Efficient Scientific Applications,” Concurrency Computation: Practice and Experience, vol. 2018, issue e4485, pp. 1-14, April 2018. DOI: 10.1002/cpe.4485“
Investigating the Benefit of FP16-Enabled Mixed-Precision Solvers for Symmetric Positive Definite Matrices using GPUs,” International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, Springer, Cham, June 2020. DOI: 10.1007/978-3-030-50417-5_18“
Keeneland: Computational Science Using Heterogeneous GPU Computing,” Contemporary High Performance Computing: From Petascale Toward Exascale, Boca Raton, FL, Taylor and Francis, 2013.“
Leading Edge Hybrid Multi-GPU Algorithms for Generalized Eigenproblems in Electronic Structure Calculations,” International Supercomputing Conference (ISC), Lecture Notes in Computer Science, vol. 7905, Leipzig, Germany, Springer Berlin Heidelberg, pp. 67-80, June 2013. DOI: 10.1007/978-3-642-38750-0_6“
Linear Algebra Software for High-Performance Computing (Part 2: Software for Hardware Accelerators and Coprocessors) , Frankfurt, Germany, ISC High Performance (ISC18), Tutorial Presentation, June 2015.
Linear Algebra Software for Large-Scale Accelerated Multicore Computing,” Acta Numerica, vol. 25, pp. 1-160, May 2016. DOI: 10.1017/S0962492916000015“
LU Factorization for Accelerator-Based Systems,” IEEE/ACS AICCSA 2011, Sharm-El-Sheikh, Egypt, December 2011.“
LU Factorization of Small Matrices: Accelerating Batched DGETRF on the GPU,” 16th IEEE International Conference on High Performance Computing and Communications (HPCC), Paris, France, IEEE, August 2014.“
LU, QR, and Cholesky Factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi,” IEEE High Performance Extreme Computing Conference (HPEC'16), Waltham, MA, IEEE, September 2016.“
MAGMA: A Breakthrough in Solvers for Eigenvalue Problems , San Jose, CA, GPU Technology Conference (GTC12), Presentation, May 2012.
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 Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing,” 2015 IEEE High Performance Extreme Computing Conference (HPEC ’15), (Best Paper Award), Waltham, MA, IEEE, September 2015.“
MAGMA - LAPACK for GPUs , Atlanta, GA, Keeneland GPU Tutorial, April 2011.
MAGMA - LAPACK for HPC on Heterogeneous Architectures , Oak Ridge, TN, Titan Summit at Oak Ridge National Laboratory, Presentation, August 2011.
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 Tensors and Batched Computing for Accelerating Applications on GPUs , San Jose, CA, GPU Technology Conference (GTC17), Presentation in Session S7728, May 2017.
MagmaDNN 0.2 High-Performance Data Analytics for Manycore GPUs and CPUs : University of Tennessee, January 2019. DOI: 10.13140/RG.2.2.14906.64961
MagmaDNN: Accelerated Deep Learning Using MAGMA,” Practice and Experience in Advanced Research Computing (PEARC ’19), Chicago, IL, ACM, July 2019.“
MagmaDNN – High-Performance Data Analytics for Manycore GPUs and CPUs , Knoxville, TN, 2017 Summer Research Experiences for Undergraduate (REU), Presentation, December 2017.
MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing,” ISC High Performance, Frankfurt, Germany, Springer International Publishing, June 2019.“
MAGMA-sparse Interface Design Whitepaper,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.“
MATEDOR: MAtrix, TEnsor, and Deep-learning Optimized Routines , Dallas, TX, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Research Poster, November 2018.
Matrices Over Runtime Systems at Exascale,” Supercomputing '12 (poster), Salt Lake City, Utah, November 2012.“
Matrix Algebra on GPU and Multicore Architectures , Basel, Switzerland, Workshop on GPU-enabled Numerical Libraries, Presentation, May 2011.
MAtrix, TEnsor, and Deep-learning Optimized Routines (MATEDOR) , Washington, DC, NSF PI Meeting, Poster, April 2018. DOI: 10.6084/m9.figshare.6174143.v3
Mixed-precision Block Gram Schmidt Orthogonalization,” 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Austin, TX, ACM, November 2015.“
Mixed-Precision Cholesky QR Factorization and its Case Studies on Multicore CPU with Multiple GPUs,” SIAM Journal on Scientific Computing, vol. 37, no. 3, pp. C203-C330, May 2015. DOI: DOI:10.1137/14M0973773“
Mixed-precision orthogonalization process Performance on multicore CPUs with GPUs,” 2015 SIAM Conference on Applied Linear Algebra, Atlanta, GA, SIAM, October 2015.“
Mixed-precision orthogonalization scheme and adaptive step size for CA-GMRES on GPUs,” VECPAR 2014 (Best Paper), Eugene, OR, June 2014.“