Publications

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2022
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
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
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
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
Luszczek, P., W. M. Sid-Lakhdar, and J. Dongarra, “Combining multitask and transfer learning with deep Gaussian processes for autotuning-based performance engineering,” The International Journal of High Performance Computing Applications, March 2023. DOI: 10.1177/10943420231166365
Abdelfattah, A., S. Tomov, P. Luszczek, H. Anzt, and J. Dongarra, “GPU-based LU Factorization and Solve on Batches of Matrices with Band Structure,” SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023. DOI: 10.1145/3624062.3624247
Sid-Lakhdar, W., S. Cayrols, D. Bielich, A. Abdelfattah, P. Luszczek, M. Gates, S. Tomov, H. Johansen, D. Williams-Young, T. Davis, et al., “PAQR: Pivoting Avoiding QR factorization,” 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), St. Petersburg, FL, USA, IEEE, 2023. DOI: 10.1109/IPDPS54959.2023.00040
2024
Luszczek, P., A. Abdelfattah, H. Anzt, A. Suzuki, and S. Tomov, “Batched sparse and mixed-precision linear algebra interface for efficient use of GPU hardware accelerators in scientific applications,” Future Generation Computer Systems, vol. 160, pp. 359 - 374, November 2024. DOI: 10.1016/j.future.2024.06.004
Luszczek, P., A. Castaldo, Y. M. Tsai, D. Mishler, and J. Dongarra, “Numerical eigen-spectrum slicing, accurate orthogonal eigen-basis, and mixed-precision eigenvalue refinement using OpenMP data-dependent tasks and accelerator offload,” The International Journal of High Performance Computing Applications, vol. 303, issue 136, September 2024. DOI: 10.1177/10943420241281050
Tahmid, T., M. Gates, P. Luszczek, and C. Schuman, “Towards Scalable and Efficient Spiking Reinforcement Learning for Continuous Control Tasks,” 2024 International Conference on Neuromorphic Systems (ICONS), Arlington, VA, USA, IEEE, 2024. DOI: 10.1109/ICONS62911.2024.00057