Publications

Barry, D., A. Danalis, and H. Jagode, “Effortless Monitoring of Arithmetic Intensity with PAPI's Counter Analysis Toolkit,” 13th International Workshop on Parallel Tools for High Performance Computing, Dresden, Germany, Springer International Publishing, September 2020.

(738.47 KB)

Han, L., Y. Gao, J. Liu, Y. Robert, and F. Vivien, “Energy-Aware Strategies for Reliability-Oriented Real-Time Task Allocation on Heterogeneous Platforms,” 49th International Conference on Parallel Processing (ICPP 2020), Edmonton, AB, Canada, ACM Press, 2020.

(804.96 KB)

Nayak, P., T. Cojean, and H. Anzt, “Evaluating Asynchronous Schwarz Solvers on GPUs,” International Journal of High Performance Computing Applications, August 2020. DOI: 10.1177/1094342020946814

Anzt, H., Y. M. Tsai, A. Abdelfattah, T. Cojean, and J. Dongarra, “Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse and Batched Computations,” 2020 IEEE/ACM Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS): IEEE, November 2020.

(1.9 MB)

Jagode, H., A. Danalis, and J. Dongarra, Exa-PAPI: The Exascale Performance API with Modern C++ , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.

(556.78 KB)

Cao, Q., Y. Pei, K. Akbudak, A. Mikhalev, G. Bosilca, H. Ltaief, D. Keyes, and J. Dongarra, “Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications,” Platform for Advanced Scientific Computing Conference (PASC20), Geneva, Switzerland, ACM, June 2020. DOI: 10.1145/3394277.3401846

(2.71 MB)

Losada, N., P. González, M. J. Martín, G. Bosilca, A. Bouteiller, and K. Teranishi, “Fault Tolerance of MPI Applications in Exascale Systems: The ULFM Solution,” Future Generation Computer Systems, vol. 106, pp. 467-481, May 2020. DOI: 10.1016/j.future.2020.01.026

(2.06 MB)

Wang, L., W. Wu, J. Zhang, H. Liu, G. Bosilca, M. Herlihy, and R. Fonseca, “FFT-Based Gradient Sparsification for the Distributed Training of Deep Neural Networks,” 9th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 20), Stockholm, Sweden, ACM, June 2020. DOI: 10.1145/3369583.3392681

(4.72 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)

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)

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)

Anzt, H., T. Cojean, Y-C. Chen, F. Goebel, T. Gruetzmacher, P. Nayak, T. Ribizel, and Y-H. Tsai, “Ginkgo: A High Performance Numerical Linear Algebra Library,” Journal of Open Source Software, vol. 5, issue 52, August 2020. DOI: 10.21105/joss.02260

(721.84 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)

Luo, X., W. Wu, G. Bosilca, Y. Pei, Q. Cao, T. Patinyasakdikul, D. Zhong, and J. Dongarra, “HAN: A Hierarchical AutotuNed Collective Communication Framework,” IEEE Cluster Conference, Kobe, Japan, Best Paper Award, IEEE Computer Society Press, September 2020.

(764.05 KB)

Beckman, P., J. Dongarra, N. Ferrier, G. Fox, T. Moore, D. Reed, and M. Beck, “Harnessing the Computing Continuum for Programming Our World,” Fog Computing: Theory and Practice: John Wiley & Sons, Inc., 2020. DOI: 10.1002/9781119551713.ch7

(1.4 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)

Han, L., L-C. Canon, J. Liu, Y. Robert, and F. Vivien, “Improved Energy-Aware Strategies for Periodic Real-Time Tasks under Reliability Constraints,” 40th IEEE Real-Time Systems Symposium (RTSS 2019), York, UK, IEEE Press, February 2020.

Lindquist, N., P. Luszczek, and J. Dongarra, “Improving the Performance of the GMRES Method using Mixed-Precision Techniques,” Smoky Mountains Computational Sciences & Engineering Conference (SMC2020), August 2020.

(600.33 KB)

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.

Beck, M., T. Moore, P. Luszczek, and A. Danalis, “Interoperable Convergence of Storage, Networking, and Computation,” Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC), no. 2: Springer International Publishing, pp. 667-690, 2020.

(1.8 MB)

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)

Lopez, F., and T. Mary, “Mixed Precision LU Factorization on GPU Tensor Cores: Reducing Data Movement and Memory Footprint,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-13: University of Tennessee, September 2020.

(409 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)

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)

Goebel, F., H. Anzt, T. Cojean, G. Flegar, and E. S. Quintana-Orti, “Multiprecision Block-Jacobi for Iterative Triangular Solves,” European Conference on Parallel Processing (Euro-Par 2020): Springer, August 2020. DOI: 10.1007/978-3-030-57675-2_34

Dongarra, J., L. Grigori, and N. J. Higham, “Numerical Algorithms for High-Performance Computational Science,” Philosophical Transactions of the Royal Society A, vol. 378, issue 2166, 2020. DOI: 10.1098/rsta.2019.0066

(724.37 KB)

Hori, A., K. Yoshinaga, T. Herault, A. Bouteiller, G. Bosilca, and Y. Ishikawa, “Overhead of Using Spare Nodes,” The International Journal of High Performance Computing Applications, February 2020. DOI: 10.1177%2F1094342020901885

(2.15 MB)

Wyrzykowski, R., E. Deelman, J. Dongarra, and K. Karczewski, “Parallel Processing and Applied Mathematics: 13th International Conference, PPAM 2019, Bialystok, Poland, September 8–11, 2019, Revised Selected Papers, Part II,” Lecture Notes in Computer Science, no. 12044: Springer International Publishing, pp. 503, March 2020. DOI: 10.1007/978-3-030-43222-5

Wyrzykowski, R., E. Deelman, J. Dongarra, and K. Karczewski, “Parallel Processing and Applied Mathematics: 13th International Conference, PPAM 2019, Bialystok, Poland, September 8–11, 2019, Revised Selected Papers, Part I,” Lecture Notes in Computer Science, 1, no. 12043: Springer International Publishing, pp. 581, March 2020. DOI: 10.1007/978-3-030-43229-4

Dongarra, J., H. Jagode, A. Danalis, D. Barry, and V. Weaver, Performance Application Programming Interface for Extreme-Scale Environments (PAPI-EX) (Poster) , Seattle, WA, 2020 NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Principal Investigator Meeting, 20 2020.

(2.53 MB)

Gates, M., A. Charara, A. YarKhan, D. Sukkari, M. Al Farhan, and J. Dongarra, “Performance Tuning SLATE,” SLATE Working Notes, no. 14, ICL-UT-20-01: Innovative Computing Laboratory, University of Tennessee, January 2020.

(1.29 MB)

Luszczek, P., and J. Dongarra, The PLASMA Library on CORAL Systems and Beyond (Poster) , Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.

(550.86 KB)

Hunold, S., A. Bhatele, G. Bosilca, and P. Knees, “Predicting MPI Collective Communication Performance Using Machine Learning,” 2020 IEEE International Conference on Cluster Computing (CLUSTER), Kobe, Japan, IEEE, September 2020. DOI: 10.1109/CLUSTER49012.2020.00036

(619.68 KB)

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)

Demmel, J., J. Dongarra, J. Langou, J. Langou, P. Luszczek, and M. Mahoney, “Prospectus for the Next LAPACK and ScaLAPACK Libraries: Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC),” LAPACK Working Notes, no. 297, ICL-UT-20-07: University of Tennessee.

(1.41 MB)

Jagode, H., and A. Danalis, PULSE: PAPI Unifying Layer for Software-Defined Events (Poster) , Seattle, WA, 2020 NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Principal Investigator Meeting, February 2020.

(1.86 MB)

Winkler, F., “Redesigning PAPI's High-Level API,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-03: University of Tennessee, February 2020.

(356.41 KB)

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