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2022

Abdelfattah, A., P. Ghysels, W. Boukaram, S. Tomov, X. Sherry Li, and J. Dongarra, “Addressing Irregular Patterns of Matrix Computations on GPUs and Their Impact on Applications Powered by Sparse Direct Solvers,” 2022 International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), Dallas, TX, IEEE Computer Society, pp. 354-367, November 2022.

(1.57 MB)

Ayala, A., S. Tomov, P. Luszczek, S. Cayrols, G. Ragghianti, and J. Dongarra, “Analysis of the Communication and Computation Cost of FFT Libraries towards Exascale,” ICL Technical Report, no. ICL-UT-22-07: Innovative Computing Laboratory, July 2022.

(5.91 MB)

Abdelfattah, A., S. Tomov, and J. Dongarra, “Batch QR Factorization on GPUs: Design, Optimization, and Tuning,” Lecture Notes in Computer Science, vol. 13350, Cham, Springer International Publishing, June 2022. DOI: 10.1007/978-3-031-08751-6_5

Fortenberry, A., and S. Tomov, “Extending MAGMA Portability with OneAPI,” The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), Ninth Workshop on Accelerator Programming Using Directives (WACCPD 2022), Dallas, TX, November 2022.

(999.19 KB)

Fortenberry, A., S. Tomov, and K. Wong, Extending MAGMA Portability with OneAPI , Dallas, TX, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), ACM Student Research Competition, November 2022.

(1.33 MB)

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)

Cayrols, S., J. Li, G. Bosilca, S. Tomov, A. Ayala, and J. Dongarra, “Lossy all-to-all exchange for accelerating parallel 3-D FFTs on hybrid architectures with GPUs,” 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 152-160, September 2022. DOI: 10.1109/CLUSTER51413.2022.00029

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)

Sid-Lakhdar, W. M., S. Cayrols, D. Bielich, A. Abdelfattah, P. Luszczek, M. Gates, S. Tomov, H. Johansen, D. Williams-Young, T. A. Davis, et al., “PAQR: Pivoting Avoiding QR factorization,” ICL Technical Report, no. ICL-UT-22-06, June 2022.

(364.85 KB)

Ayala, A., S. Tomov, M. Stoyanov, A. Haidar, and J. Dongarra, “Performance Analysis of Parallel FFT on Large Multi-GPU Systems,” 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lyon, France, IEEE, August 2022. DOI: 10.1109/IPDPSW55747.2022.00072

Nance, D., S. Tomov, and K. Wong, “A Python Library for Matrix Algebra on GPU and Multicore Architectures,” 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, CO, IEEE, December 2022. DOI: 10.1109/MASS56207.2022.00121

(414.36 KB)

2021

Ayala, A., S. Tomov, A. Haidar, M. Stoyanov, S. Cayrols, J. Li, G. Bosilca, and J. Dongarra, Accelerating FFT towards Exascale Computing : NVIDIA GPU Technology Conference (GTC2021), 2021.

(27.23 MB)

Kolev, T., P. Fischer, M. Min, J. Dongarra, J. Brown, V. Dobrev, T. Warburton, S. Tomov, M. S. Shephard, A. Abdelfattah, et al., “Efficient exascale discretizations: High-order finite element methods,” The International Journal of High Performance Computing Applications, pp. 10943420211020803, 2021. DOI: 10.1177/10943420211020803

Iqbal, Z., S. Nooshabadi, I. Yamazaki, S. Tomov, and J. Dongarra, “Exploiting Block Structures of KKT Matrices for Efficient Solution of Convex Optimization Problems,” IEEE Access, 2021. DOI: 10.1109/ACCESS.2021.3106054

(1.35 MB)

Ayala, A., S. Tomov, P. Luszczek, S. Cayrols, G. Ragghianti, and J. Dongarra, “Interim Report on Benchmarking FFT Libraries on High Performance Systems,” Innovative Computing Laboratory Technical Report, no. ICL-UT-21-03: University of Tennessee, July 2021.

(2.68 MB)

Brown, J., A. Abdelfattah, V. Barra, N. Beams, J-S. Camier, V. Dobrev, Y. Dudouit, L. Ghaffari, T. Kolev, D. Medina, et al., “libCEED: Fast algebra for high-order element-based discretizations,” Journal of Open Source Software, vol. 6, no. 63, pp. 2945, 2021. DOI: 10.21105/joss.02945

Tomov, S., K. Wong, R. Febbo, and J. Halloy, Linear Algebra Prepara.on for Emergent Neural Network Architectures: MAGMA, BLAS, and Batched GPU Computing , Virtual, LAPENNA Workshop, November 2021.

(17.8 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)

Ayala, A., S. Tomov, M. Stoyanov, and J. Dongarra, “Scalability Issues in FFT Computation,” International Conference on Parallel Computing Technologies: Springer, pp. 279–287, 2021. DOI: 10.1007/978-3-030-86359-3_21

Abdelfattah, A., T. Costa, J. Dongarra, M. Gates, A. Haidar, S. Hammarling, N. J. Higham, J. Kurzak, P. Luszczek, S. Tomov, et al., “A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines,” ACM Transactions on Mathematical Software (TOMS), vol. 47, no. 3, pp. 1–23, 2021. DOI: 10.1145/3431921

Dongarra, J., M. Gates, P. Luszczek, and S. Tomov, “Translational process: Mathematical software perspective,” Journal of Computational Science, vol. 52, pp. 101216, 2021. DOI: 10.1016/j.jocs.2020.101216

2020

Lopez, F., E. Chow, S. Tomov, and J. Dongarra, “Asynchronous SGD for DNN Training on Shared-Memory Parallel Architectures,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-04: University of Tennessee, Knoxville, March 2020.

(188.51 KB)

Lopez, F., E. Chow, S. Tomov, and J. Dongarra, “Asynchronous SGD for DNN Training on Shared-Memory Parallel Architectures,” Workshop on Scalable Deep Learning over Parallel And Distributed Infrastructures (ScaDL 2020), May 2020.

(188.51 KB)

Kolev, T., P. Fischer, A. Abdelfattah, S. Ananthan, V. Barra, N. Beams, R. Bleile, J. Brown, R. Carson, J-S. Camier, et al., “CEED ECP Milestone Report: Improve Performance and Capabilities of CEED-Enabled ECP Applications on Summit/Sierra,” ECP Milestone Reports: Zenodo, May 2020. DOI: 10.5281/zenodo.3860804

(28.12 MB)

Gates, M., S. Tomov, H. Anzt, P. Luszczek, and J. Dongarra, Clover: Computational Libraries Optimized via Exascale Research , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.

(872 KB)

Brown, C., A. Abdelfattah, S. Tomov, and J. Dongarra, “Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-12: University of Tennessee, August 2020.

(476.36 KB)

Brown, C., A. Abdelfattah, S. Tomov, and J. Dongarra, “Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs,” 2020 IEEE High Performance Extreme Computing Virtual Conference: IEEE, September 2020.

(476.36 KB)

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)

Ayala, A., S. Tomov, A. Haidar, and J. Dongarra, “heFFTe: Highly Efficient FFT for Exascale,” International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, June 2020. DOI: 10.1007/978-3-030-50371-0_19

(2.62 MB)

Ayala, A., S. Tomov, A. Haidar, and J. Dongarra, heFFTe: Highly Efficient FFT for Exascale (Poster) : NVIDIA GPU Technology Conference (GTC2020), October 2020.

(866.88 KB)

Ayala, A., S. Tomov, A. Haidar, and J. Dongarra, heFFTe: Highly Efficient FFT for Exascale (Poster) , Seattle, WA, SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP20), February 2020.

(1.54 MB)

Ayala, A., S. Tomov, J. Dongarra, and A. Haidar, heFFTe: Highly Efficient FFT for Exascale (Poster) , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.

(6.2 MB)

Beams, N., A. Abdelfattah, S. Tomov, J. Dongarra, T. Kolev, and Y. Dudouit, “High-Order Finite Element Method using Standard and Device-Level Batch GEMM on GPUs,” 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA): IEEE, November 2020.

(1.3 MB)

Brown, C., A. Abdelfattah, S. Tomov, and J. Dongarra, hipMAGMA v1.0 : Zenodo, March 2020. DOI: 10.5281/zenodo.3908549

Brown, C., A. Abdelfattah, S. Tomov, and J. Dongarra, hipMAGMA v2.0 : Zenodo, July 2020. DOI: 10.5281/zenodo.3928667

Wong, K., S. Tomov, D. Nichols, R. Febbo, F. Lopez, J. Halloy, and X. Ma, How to Build Your Own Deep Neural Network : PEARC20, July 2020.

(18.8 MB)

Tomov, S., K. Wong, J. Dongarra, R. Archibald, E. Chow, E. D'Azevedo, M. Eisenbach, R. Febbo, F. Lopez, D. Nichols, et al., Integrating Deep Learning in Domain Science at Exascale (MagmaDNN) , virtual, DOD HPCMP seminar, December 2020.

(11.12 MB)

Archibald, R., E. Chow, E. D'Azevedo, J. Dongarra, M. Eisenbach, R. Febbo, F. Lopez, D. Nichols, S. Tomov, K. Wong, et al., “Integrating Deep Learning in Domain Sciences at Exascale,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-10: University of Tennessee, August 2020.

(1.09 MB)

Archibald, R., E. Chow, E. D'Azevedo, J. Dongarra, M. Eisenbach, R. Febbo, F. Lopez, D. Nichols, S. Tomov, K. Wong, et al., “Integrating Deep Learning in Domain Sciences at Exascale,” 2020 Smoky Mountains Computational Sciences and Engineering Conference (SMC 2020), August 2020.

Abdelfattah, A., S. Tomov, and J. Dongarra, “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

(702.38 KB)

Anzt, H., T. Cojean, C. Yen-Chen, J. Dongarra, G. Flegar, P. Nayak, S. Tomov, Y. M. Tsai, and W. Wang, “Load-Balancing Sparse Matrix Vector Product Kernels on GPUs,” ACM Transactions on Parallel Computing, vol. 7, issue 1, March 2020. DOI: 10.1145/3380930

(5.67 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., 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)

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., 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)

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)

Wong, K., S. Tomov, and J. Dongarra, “Project-Based Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning,” The Journal of Computational Science Education, vol. 11, issue 1, pp. 36-44, January 2020. DOI: 10.22369/issn.2153-4136/11/1/7

(4.4 MB)

Lu, Y., I. Yamazaki, F. Ino, Y. Matsushita, S. Tomov, and J. Dongarra, “Reducing the Amount of out-of-core Data Access for GPU-Accelerated Randomized SVD,” Concurrency and Computation: Practice and Experience, April 2020. DOI: 10.1002/cpe.5754

(1.43 MB)