Publications

Export 1241 results:
2023
Thiyagalingam, J., G. von Laszewski, J. Yin, M. Emani, J. Papay, G. Barrett, P. Luszczek, A. Tsaris, C. Kirkpatrick, F. Wang, et al., AI Benchmarking for Science: Efforts from the MLCommons Science Working Group,” Lecture Notes in Computer Science, vol. 13387: Springer International Publishing, pp. 47 - 64, January 2023. DOI: 10.1007/978-3-031-23220-610.1007/978-3-031-23220-6_4
Reed, D., D. Gannon, and J. Dongarra, HPC Forecast,” Communications of the ACM, vol. 664648, issue 2, pp. 82 - 90, January 2023. DOI: 10.1145/3552309
2022
Abdulah, S., Q. Cao, Y. Pei, G. Bosilca, J. Dongarra, M. G. Genton, D. E. Keyes, H. Ltaief, and Y. Sun, Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, issue 4, pp. 964 - 976, April 2022. DOI: 10.1109/TPDS.2021.3084071
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)
Anzt, H., M. Casas, C. I. Malossi, E. S. Quintana-Ortí, F. Scheidegger, and S. Zhuang, Approximate Computing for Scientific Applications,” Approximate Computing Techniques, 322: Springer International Publishing, pp. 415 - 465, January 2022. DOI: 10.1007/978-3-030-94705-7_14
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
Kashi, A., P. Nayak, D. Kulkarni, A. Scheinberg, P. Lin, and H. Anzt, Batched sparse iterative solvers on GPU for the collision operator for fusion plasma simulations,” 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Lyon, France, IEEE, July 2022. DOI: 10.1109/IPDPS53621.2022.00024  (1.26 MB)
Benoit, A., Y. Du, T. Herault, L. Marchal, G. Pallez, L. Perotin, Y. Robert, H. Sun, and F. Vivien, Checkpointing à la Young/Daly: An Overview,” IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing, Noida, India, ACM Press, pp. 701-710, August 2022. DOI: 10.1145/3549206  (639.77 KB)
Alomairy, R., M. Gates, S. Cayrols, D. Sukkari, K. Akbudak, A. YarKhan, P. Bagwell, and J. Dongarra, Communication Avoiding LU with Tournament Pivoting in SLATE,” SLATE Working Notes, no. 18, ICL-UT-22-01, January 2022.  (3.74 MB)
Bosilca, G., A. Bouteiller, T. Herault, V. Le Fèvre, Y. Robert, and J. Dongarra, Comparing Distributed Termination Detection Algorithms for Modern HPC Platforms,” International Journal of Networking and Computing, vol. 12, issue 1, pp. 26 - 46, January 2022. DOI: 10.15803/ijnc.12.1_26
Aliaga, J. I., H. Anzt, T. Grützmacher, E. S. Quintana-Ortí, and A. E. Tomás, Compressed basis GMRES on high-performance graphics processing units,” The International Journal of High Performance Computing Applications, May 2022. DOI: 10.1177/10943420221115140  (13.52 MB)
Aliaga, J. I., H. Anzt, T. Grützmacher, E. S. Quintana-Orti, and A. E. Tomás, Compression and load balancing for efficient sparse matrix‐vector product on multicore processors and graphics processing units,” Concurrency and Computation: Practice and Experience, vol. 34, issue 14, June 2022. DOI: 10.1002/cpe.6515  (749.82 KB)
Kovalchuk, S. V., V. V. Krzhizhanovskaya, M. Paszyński, D. Kranzlmüller, J. Dongarra, and P. M. A. Sloot, Computational science for a better future,” Journal of Computational Science, vol. 62, pp. 101745, July 2022. DOI: 10.1016/j.jocs.2022.101745
Sid-Lakhdar, W. M., M. Aznaveh, P. Luszczek, and J. Dongarra, Deep Gaussian process with multitask and transfer learning for performance optimization,” 2022 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1-7, September 2022. DOI: 10.1109/HPEC55821.2022.9926396
Cao, Q., G. Bosilca, N. Losada, W. Wu, D. Zhong, and J. Dongarra, Evaluating Data Redistribution in PaRSEC,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 8, pp. 1856-1872, August 2022. DOI: 10.1109/TPDS.2021.3131657  (3.19 MB)
Penchoff, D. A., C. C. Peterson, E. M. Wrancher, G. Bosilca, R. J. Harrison, E. F. Valeev, and P. D. Benny, Evaluations of molecular modeling and machine learning for predictive capabilities in binding of lanthanum and actinium with carboxylic acids,” Journal of Radioanalytical and Nuclear Chemistry, December 2022. DOI: 10.1007/s10967-022-08620-7
Dongarra, J., The evolution of mathematical software,” Communications of the ACM, vol. 65227, issue 12, pp. 66 - 72, December 2022. DOI: 10.1145/3554977
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)
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)
Schuchart, J., P. Nookala, M. Mahdi Javanmard, T. Herault, E. F. Valeev, G. Bosilca, and R. J. Harrison, Generalized Flow-Graph Programming Using Template Task-Graphs: Initial Implementation and Assessment,” 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Lyon, France, IEEE, July 2022. DOI: 10.1109/IPDPS53621.2022.00086
Anzt, H., T. Cojean, G. Flegar, F. Göbel, T. Grützmacher, P. Nayak, T. Ribizel, Y. Mike Tsai, and E. S. Quintana-Ortí, Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing,” ACM Transactions on Mathematical Software, vol. 48, issue 12, pp. 1 - 33, March 2022. DOI: 10.1145/3480935  (4.2 MB)
Cojean, T., Y-H. Mike Tsai, and H. Anzt, Ginkgo—A math library designed for platform portability,” Parallel Computing, vol. 111, pp. 102902, February 2022. DOI: 10.1016/j.parco.2022.102902
Whitlock, M., N. Morales, G. Bosilca, A. Bouteiller, B. Nicolae, K. Teranishi, E. Giem, and V. Sarkar, Integrating process, control-flow, and data resiliency layers using a hybrid Fenix/Kokkos approach,” 2022 IEEE International Conference on Cluster Computing (CLUSTER 2022), Heidelberg, Germany, September 2022.
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)
Bak, S., C. Bertoni, S. Boehm, R. Budiardja, B. M. Chapman, J. Doerfert, M. Eisenbach, H. Finkel, O. Hernandez, J. Huber, et al., OpenMP application experiences: Porting to accelerated nodes,” Parallel Computing, vol. 109, March 2022. DOI: 10.1016/j.parco.2021.102856
Du, Y., G. Pallez, L. Marchal, and Y. Robert, Optimal Checkpointing Strategies for Iterative Applications,” IEEE Transactions on Parallel Distributed Systems, vol. 33, issue 3, pp. 507-522, March 2022. DOI: 10.1109/TPDS.2021.3099440  (1.47 MB)
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
Tsai, Y. M., T. Cojean, and H. Anzt, Porting Sparse Linear Algebra to Intel GPUs,” Euro-Par 2021: Parallel Processing Workshops, vol. 13098, Lisbon, Portugal, Springer International Publishing, pp. 57 - 68, June 2022. DOI: 10.1007/978-3-031-06156-1_5
Funk, Y., M. Götz, and H. Anzt, Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning,” 2022 SIAM Conference on Parallel Processing for Scientific Computing (PP), Philadelphia, PA, Society for Industrial and Applied Mathematics, pp. 14 - 24. DOI: 10.1137/1.978161197714110.1137/1.9781611977141.2
Tsai, Y-H. M., T. Cojean, and H. Anzt, Providing performance portable numerics for Intel GPUs,” Concurrency and Computation: Practice and Experience, vol. 17, October 2022. DOI: 10.1002/cpe.7400  (3.16 MB)
Schuchart, J., P. Nookala, T. Herault, E. F. Valeev, and G. Bosilca, Pushing the Boundaries of Small Tasks: Scalable Low-Overhead Data-Flow Programming in TTG,” 2022 IEEE International Conference on Cluster Computing (CLUSTER), Heidelberg, Germany, IEEE, September 2022. DOI: 10.1109/CLUSTER51413.2022.00026
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)
Murray, R., J. Demmel, M. W. Mahoney, B. N. Erichson, M. Melnichenko, O. Asif Malik, L. Grigori, P. Luszczek, M. Dereziński, M. E. Lopes, et al., Randomized Numerical Linear Algebra: A Perspective on the Field with an Eye to Software,” University of California, Berkeley EECS Technical Report, no. UCB/EECS-2022-258: University of California, Berkeley, November 2022.  (1.05 MB)
Reed, D., D. Gannon, and J. Dongarra, Reinventing High Performance Computing: Challenges and Opportunities,” ICL Technical Report, no. ICL-UT-22-03, March 2022.  (1.36 MB)
Dongarra, J., and A. Geist, Report on the Oak Ridge National Laboratory's Frontier System,” ICL Technical Report, no. ICL-UT-22-05, May 2022.  (16.87 MB)
Cao, Q., S. Abdulah, R. Alomairy, Y. Pei, P. Nag, G. Bosilca, J. Dongarra, M. G. Genton, D. Keyes, H. Ltaief, et al., Reshaping Geostatistical Modeling and Prediction for Extreme-Scale Environmental Applications,” 2022 International Conference for High Performance Computing, Networking, Storage and Analysis (SC22), Dallas, TX, IEEE Press, November 2022.
Agullo, E., M. Altenbernd, H. Anzt, L. Bautista-Gomez, T. Benacchio, L. Bonaventura, H-J. Bungartz, S. Chatterjee, F. M. Ciorba, N. DeBardeleben, et al., Resiliency in numerical algorithm design for extreme scale simulations,” The International Journal of High Performance Computing Applications, vol. 36371337212766180823, issue 2, pp. 251 - 285, March 2022. DOI: 10.1177/10943420211055188
Luszczek, P., and C. Brown, Surrogate ML/AI Model Benchmarking for FAIR Principles' Conformance,” 2022 IEEE High Performance Extreme Computing Conference (HPEC): IEEE, September 2022. DOI: 10.1109/HPEC55821.2022.9926401
Lindquist, N., M. Gates, P. Luszczek, and J. Dongarra, Threshold Pivoting for Dense LU Factorization,” ScalAH22: 13th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems , Dallas, Texas, IEEE, 2022.  (721.77 KB)
Zhong, D., Q. Cao, G. Bosilca, and J. Dongarra, Using long vector extensions for MPI reductions,” Parallel Computing, vol. 109, pp. 102871, March 2022. DOI: 10.1016/j.parco.2021.102871

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