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Alvaro, W., J. Kurzak, and J. Dongarra, “Fast and Small Short Vector SIMD Matrix Multiplication Kernels for the CELL Processor,” University of Tennessee Computer Science Technical Report, no. UT-CS-08-609, (also LAPACK Working Note 189), January 2008.

(500.99 KB)

Abdelfattah, A., S. Tomov, and J. Dongarra, “Fast Batched Matrix Multiplication for Small Sizes using Half Precision Arithmetic on GPUs,” 33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil, IEEE, May 2019.

(675.5 KB)

Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, “Fast Cholesky Factorization on GPUs for Batch and Native Modes in MAGMA,” Journal of Computational Science, vol. 20, pp. 85–93, May 2017. DOI: 10.1016/j.jocs.2016.12.009

(3.6 MB)

Agullo, E., C. Augonnet, J. Dongarra, H. Ltaeif, R. Namyst, S. Thibault, and S. Tomov, “Faster, Cheaper, Better - A Hybridization Methodology to Develop Linear Algebra Software for GPUs,” LAPACK Working Note, no. 230, 00 2010.

(334.48 KB)

Ayala, A., S. Tomov, P. Luszczek, S. Cayrols, G. Ragghianti, and J. Dongarra, “FFT Benchmark Performance Experiments on Systems Targeting Exascale,” ICL Technical Report, no. ICL-UT-22-02, March 2022.

(5.87 MB)

Tomov, S., A. Ayala, A. Haidar, and J. Dongarra, “FFT-ECP API and High-Performance Library Prototype for 2-D and 3-D FFTs on Large-Scale Heterogeneous Systems with GPUs,” ECP Milestone Report, no. FFT-ECP STML13-27: Innovative Computing Laboratory, University of Tennessee, January 2020.

(9.71 MB)

Tomov, S., A. Haidar, A. Ayala, D. Schultz, and J. Dongarra, FFT-ECP Fast Fourier Transform , Houston, TX, 2019 ECP Annual Meeting (Research Poster), January 2019.

(1.51 MB)

Tomov, S., A. Haidar, A. Ayala, H. Shaiek, and J. Dongarra, “FFT-ECP Implementation Optimizations and Features Phase,” Innovative Computing Laboratory Technical Report, no. ICL-UT-19-12: University of Tennessee, October 2019.

(4.14 MB)

Anzt, H., J. Dongarra, and E. S. Quintana-Orti, “Fine-grained Bit-Flip Protection for Relaxation Methods,” Journal of Computational Science, November 2016. DOI: 10.1016/j.jocs.2016.11.013

(1.47 MB)

Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Orti, Flexible Batched Sparse Matrix Vector Product on GPUs , Denver, Colorado, ScalA'17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, November 2017.

(16.8 MB)

Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “Flexible Batched Sparse Matrix-Vector Product on GPUs,” 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17), Denver, CO, ACM Press, November 2017. DOI: http://dx.doi.org/10.1145/3148226.3148230

(583.4 KB)

Cao, Q., G. Bosilca, W. Wu, D. Zhong, A. Bouteiller, and J. Dongarra, “Flexible Data Redistribution in a Task-Based Runtime System,” IEEE International Conference on Cluster Computing (Cluster 2020), Kobe, Japan, IEEE, September 2020. DOI: 10.1109/CLUSTER49012.2020.00032

(354.8 KB)

Bosilca, G., A. Bouteiller, A. Danalis, M. Faverge, A. Haidar, T. Herault, J. Kurzak, J. Langou, P. Lemariner, H. Ltaeif, et al., “Flexible Development of Dense Linear Algebra Algorithms on Massively Parallel Architectures with DPLASMA,” Proceedings of the Workshops of the 25th IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2011 Workshops), Anchorage, Alaska, USA, IEEE, pp. 1432-1441, May 2011.

(1.26 MB)

Jagode, H., A. Danalis, and J. Dongarra, “Formulation of Requirements for New PAPI++ Software Package: Part I: Survey Results,” PAPI++ Working Notes, no. 1, ICL-UT-20-02: Innovative Computing Laboratory, University of Tennessee Knoxville, January 2020.

(1.49 MB)

Kabir, K., A. Haidar, S. Tomov, A. Bouteiller, and J. Dongarra, “A Framework for Out of Memory SVD Algorithms,” ISC High Performance 2017, pp. 158–178, June 2017. DOI: 10.1007/978-3-319-58667-0_9

(393.22 KB)

Cao, Q., R. Alomairy, Y. Pei, G. Bosilca, H. Ltaief, D. Keyes, and J. Dongarra, “A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization,” IEEE International Parallel and Distributed Processing Symposium (IPDPS), July 2022. DOI: 10.1109/IPDPS53621.2022.00047

(1.03 MB)

Du, P., R. Weber, P. Luszczek, S. Tomov, G. D. Peterson, and J. Dongarra, “From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming,” Parallel Computing, vol. 38, no. 8, pp. 391-407, August 2012.

(1.64 MB)

Bosilca, G., A. Bouteiller, A. Danalis, T. Herault, and J. Dongarra, “From Serial Loops to Parallel Execution on Distributed Systems,” International European Conference on Parallel and Distributed Computing (Euro-Par '12), Rhodes, Greece, August 2012.

(203.08 KB)

Tomov, S., and J. Dongarra, The Future of Computing: Software Libraries , Savannah, GA, DOD CREATE Developers' Review, Keynote Presentation, February 2012.

(6.76 MB)

Lindquist, N., P. Luszczek, and J. Dongarra, Generalizing Random Butterfly Transforms to Arbitrary Matrix Sizes : arXiv, December 2023.

Herault, T., Y. Robert, G. Bosilca, and J. Dongarra, “Generic Matrix Multiplication for Multi-GPU Accelerated Distributed-Memory Platforms over PaRSEC,” ScalA'19: 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Denver, CO, IEEE, November 2019.

(260.69 KB)

Anzt, H., T. Cojean, Y-C. Chen, F. Goebel, T. Gruetzmacher, P. Nayak, T. Ribizel, Y-H. Tsai, and J. Dongarra, Ginkgo: A Node-Level Sparse Linear Algebra Library for HPC (Poster) , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.

(699 KB)

Anzt, H., P. Luszczek, J. Dongarra, and V. Heuveline, “GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement,” EuroPar 2012 (also LAWN 260), Rhodes Island, Greece, August 2012.

(662.98 KB)

Shaiek, H., S. Tomov, A. Ayala, A. Haidar, and J. Dongarra, “GPUDirect MPI Communications and Optimizations to Accelerate FFTs on Exascale Systems,” EuroMPI'19 Posters, Zurich, Switzerland, no. icl-ut-19-06: ICL, September 2019.

(2.25 MB)

Haidar, A., A. Abdelfattah, M. Zounon, S. Tomov, and J. Dongarra, “A Guide for Achieving High Performance with Very Small Matrices on GPUs: A Case Study of Batched LU and Cholesky Factorizations,” IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 5, pp. 973–984, May 2018. DOI: 10.1109/TPDS.2017.2783929

(832.92 KB)