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Tomov, S., J. Dongarra, A. Haidar, I. Yamazaki, T. Dong, T. Schulthess, and R. Solcà, MAGMA: A Breakthrough in Solvers for Eigenvalue Problems , San Jose, CA, GPU Technology Conference (GTC12), Presentation, May 2012.  (9.23 MB)
Dongarra, J., T. Dong, M. Gates, A. Haidar, S. Tomov, and I. Yamazaki, 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.  (4.69 MB)
Dong, T., A. Haidar, P. Luszczek, S. Tomov, A. Abdelfattah, and J. Dongarra, MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs,” Innovative Computing Laboratory Technical Report, no. ICL-UT-16-02: University of Tennessee, August 2016.  (929.79 KB)
Haidar, A., S. Tomov, P. Luszczek, and J. Dongarra, 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.  (678.86 KB)
Tomov, S., MAGMA - LAPACK for GPUs , Atlanta, GA, Keeneland GPU Tutorial, April 2011.  (742.14 KB)
Tomov, S., and J. Dongarra, MAGMA - LAPACK for HPC on Heterogeneous Architectures , Oak Ridge, TN, Titan Summit at Oak Ridge National Laboratory, Presentation, August 2011.  (20.43 MB)
Dongarra, J., M. Gates, Y. Jia, K. Kabir, P. Luszczek, and S. Tomov, 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.  (6.4 MB)
Anzt, H., J. Dongarra, M. Gates, A. Haidar, K. Kabir, P. Luszczek, S. Tomov, and I. Yamazaki, MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi , Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.  (2.03 MB)
Farhan, M. Al, A. Abdelfattah, S. Tomov, M. Gates, D. Sukkari, A. Haidar, R. Rosenberg, and J. Dongarra, MAGMA Templates for Scalable Linear Algebra on Emerging Architectures,” The International Journal of High Performance Computing Applications, vol. 34, issue 6, pp. 645-658, November 2020. DOI: 10.1177/1094342020938421
Tomov, S., and A. Haidar, MAGMA Tensors and Batched Computing for Accelerating Applications on GPUs , San Jose, CA, GPU Technology Conference (GTC17), Presentation in Session S7728, May 2017.  (11.12 MB)
Ng, L., S. Chen, A. Gessinger, D. Nichols, S. Cheng, A. Meenasorna, K. Wong, S. Tomov, A. Haidar, E. D'Azevedo, et al., 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  (7.84 MB)
Nichols, D., K. Wong, S. Tomov, L. Ng, S. Chen, and A. Gessinger, MagmaDNN: Accelerated Deep Learning Using MAGMA,” Practice and Experience in Advanced Research Computing (PEARC ’19), Chicago, IL, ACM, July 2019.  (1.09 MB)
Ng, L., K. Wong, A. Haidar, S. Tomov, and J. Dongarra, MagmaDNN – High-Performance Data Analytics for Manycore GPUs and CPUs , Knoxville, TN, 2017 Summer Research Experiences for Undergraduate (REU), Presentation, December 2017.  (5.06 MB)
Nichols, D., N-S. Tomov, F. Betancourt, S. Tomov, K. Wong, and J. Dongarra, MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing,” ISC High Performance, Frankfurt, Germany, Springer International Publishing, June 2019. DOI: 10.1007/978-3-030-34356-9_37  (1.37 MB) (8.72 MB)
Anzt, H., E. Boman, J. Dongarra, G. Flegar, M. Gates, M. Heroux, M. Hoemmen, J. Kurzak, P. Luszczek, S. Rajamanickam, et al., MAGMA-sparse Interface Design Whitepaper,” Innovative Computing Laboratory Technical Report, no. ICL-UT-17-05, September 2017.  (1.28 MB)
Abdelfattah, A., J. Dongarra, A. Haidar, S. Tomov, and I. Yamazaki, 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.  (2.55 MB)
Tomov, S., MATEDOR: MAtrix, TEnsor, and Deep-learning Optimized Routines , Seattle, WA, 2020 NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Principal Investigator Meeting, February 2020.  (2.28 MB)
Agullo, E., G. Bosilca, C. Castagnède, J. Dongarra, H. Ltaeif, and S. Tomov, Matrices Over Runtime Systems at Exascale,” Supercomputing '12 (poster), Salt Lake City, Utah, November 2012.
Tomov, S., Matrix Algebra on GPU and Multicore Architectures , Basel, Switzerland, Workshop on GPU-enabled Numerical Libraries, Presentation, May 2011.  (49.27 MB)
Abdelfattah, A., S. Tomov, and J. Dongarra, Matrix Multiplication on Batches of Small Matrices in Half and Half-Complex Precisions,” Journal of Parallel and Distributed Computing, vol. 145, pp. 188-201, November 2020. DOI: 10.1016/j.jpdc.2020.07.001  (1.3 MB)
Haidar, A., S. Tomov, A. Abdelfattah, I. Yamazaki, and J. Dongarra, MAtrix, TEnsor, and Deep-learning Optimized Routines (MATEDOR) , Washington, DC, NSF PI Meeting, Poster, April 2018. DOI: 10.6084/m9.figshare.6174143.v3  (2.4 MB)
Cayrols, S., J. Li, G. Bosilca, S. Tomov, A. Ayala, and J. Dongarra, Mixed precision and approximate 3D FFTs: Speed for accuracy trade-off with GPU-aware MPI and run-time data compression,” ICL Technical Report, no. ICL-UT-22-04, May 2022.  (706.14 KB)
Yamazaki, I., S. Tomov, J. Kurzak, J. Dongarra, and J. Barlow, Mixed-precision Block Gram Schmidt Orthogonalization,” 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Austin, TX, ACM, November 2015.  (235.69 KB)
Yamazaki, I., S. Tomov, and J. Dongarra, 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  (374.8 KB)
Haidar, A., H. Bayraktar, S. Tomov, J. Dongarra, and N. J. Higham, Mixed-Precision Iterative Refinement using Tensor Cores on GPUs to Accelerate Solution of Linear Systems,” Proceedings of the Royal Society A, vol. 476, issue 2243, November 2020. DOI: 10.1098/rspa.2020.0110  (2.24 MB)
Yamazaki, I., J. Barlow, S. Tomov, J. Kurzak, and J. Dongarra, Mixed-precision orthogonalization process Performance on multicore CPUs with GPUs,” 2015 SIAM Conference on Applied Linear Algebra, Atlanta, GA, SIAM, October 2015.  (301.01 KB)
Yamazaki, I., S. Tomov, T. Dong, and J. Dongarra, Mixed-precision orthogonalization scheme and adaptive step size for CA-GMRES on GPUs,” VECPAR 2014 (Best Paper), Eugene, OR, June 2014.  (438.54 KB)
Haidar, A., H. Bayraktar, S. Tomov, J. Dongarra, and N. J. Higham, Mixed-Precision Solution of Linear Systems Using Accelerator-Based Computing,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-05: University of Tennessee, May 2020.  (1.03 MB)
Du, P., P. Luszczek, S. Tomov, and J. Dongarra, Mixed-Tool Performance Analysis on Hybrid Multicore Architectures,” First International Workshop on Parallel Software Tools and Tool Infrastructures (PSTI 2010), San Diego, CA, September 2010.  (1.24 MB)
Dongarra, J., A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, and A. YarKhan, Model-Driven One-Sided Factorizations on Multicore, Accelerated Systems,” Supercomputing Frontiers and Innovations, vol. 1, issue 1, 2014. DOI: http://dx.doi.org/10.14529/jsfi1401  (1.86 MB)
Sharp, D., M. Stoyanov, S. Tomov, and J. Dongarra, A More Portable HeFFTe: Implementing a Fallback Algorithm for Scalable Fourier Transforms,” ICL Technical Report, no. ICL-UT-21-04: University of Tennessee, August 2021.  (493.17 KB)
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Yamazaki, I., S. Tomov, and J. Dongarra, Non-GPU-resident Dense Symmetric Indefinite Factorization,” Concurrency and Computation: Practice and Experience, November 2016. DOI: 10.1002/cpe.4012
Li, Y., J. Dongarra, and S. Tomov, A Note on Auto-tuning GEMM for GPUs,” 9th International Conference on Computational Science (ICCS 2009), no. 5544-5545, Baton Rouge, LA, pp. 884-892, May 2009. DOI: 10.1007/978-3-642-01970-8_89  (236.02 KB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, Novel HPC Techniques to Batch Execution of Many Variable Size BLAS Computations on GPUs,” International Conference on Supercomputing (ICS '17), Chicago, Illinois, ACM, June 2017. DOI: 10.1145/3079079.3079103  (1.04 MB)
Solcà, R., A. Haidar, S. Tomov, J. Dongarra, and T. C. Schulthess, A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks,” Supercomputing '12 (poster), Salt Lake City, Utah, November 2012.
Haidar, A., R. Solcà, M. Gates, S. Tomov, T. C. Schulthess, and J. Dongarra, 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  (1.74 MB)
Agullo, E., J. Demmel, J. Dongarra, B. Hadri, J. Kurzak, J. Langou, H. Ltaeif, P. Luszczek, and S. Tomov, Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects,” Journal of Physics: Conference Series, vol. 180, 00 2009.  (119.37 KB)
Agullo, E., J. Demmel, J. Dongarra, B. Hadri, J. Kurzak, J. Langou, H. Ltaeif, P. Luszczek, R. Nath, S. Tomov, et al., Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects , Portland, OR, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC09), November 2009.  (3.53 MB)
Nath, R., J. Dongarra, S. Tomov, H. Ltaeif, and P. Du, Numerical Linear Algebra on Hybrid Architectures: Recent Developments in the MAGMA Project , Portland, Oregon, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC09), November 2009.  (1.41 MB)
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Yamazaki, I., S. Tomov, and J. Dongarra, One-Sided Dense Matrix Factorizations on a Multicore with Multiple GPU Accelerators,” The International Conference on Computational Science (ICCS), June 2012.
Betancourt, F., K. Wong, E. Asemota, Q. Marshall, D. Nichols, and S. Tomov, OpenDIEL: A Parallel Workflow Engine and DataAnalytics Framework,” Practice and Experience in Advanced Research Computing (PEARC ’19), Chicago, IL, ACM, July 2019.  (1.48 MB)
Haidar, A., T. Dong, P. Luszczek, S. Tomov, and J. Dongarra, Optimization for Performance and Energy for Batched Matrix Computations on GPUs,” 8th Workshop on General Purpose Processing Using GPUs (GPGPU 8), San Francisco, CA, ACM, February 2015. DOI: 10.1145/2716282.2716288  (699.5 KB)
Abdelfattah, A., S. Tomov, and J. Dongarra, Optimizing Batch HGEMM on Small Sizes Using Tensor Cores , San Jose, CA, GPU Technology Conference (GTC), March 2019.  (2.47 MB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization,” IEEE High Performance Extreme Computing Conference (HPEC’18), Waltham, MA, IEEE, September 2018.  (729.87 KB)
Tomov, S., P. Luszczek, I. Yamazaki, J. Dongarra, H. Anzt, and W. Sawyer, Optimizing Krylov Subspace Solvers on Graphics Processing Units,” Fourth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2014, Phoenix, AZ, IEEE, May 2014.  (536.32 KB)
Nath, R., S. Tomov, T. Dong, and J. Dongarra, Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs,” ACM/IEEE Conference on Supercomputing (SC’11), Seattle, WA, November 2011.  (630.63 KB)
Dong, T., A. Haidar, S. Tomov, and J. Dongarra, Optimizing the SVD Bidiagonalization Process for a Batch of Small Matrices,” International Conference on Computational Science (ICCS 2017), Zurich, Switzerland, Procedia Computer Science, June 2017. DOI: 10.1016/j.procs.2017.05.237  (364.95 KB)
Haidar, A., K. Kabir, D. Fayad, S. Tomov, and J. Dongarra, Out of Memory SVD Solver for Big Data,” 2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Waltham, MA, IEEE, September 2017.  (1.33 MB)

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