Publications

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Losada, N., A. Bouteiller, and G. Bosilca, “Asynchronous Receiver-Driven Replay for Local Rollback of MPI Applications,” Fault Tolerance for HPC at eXtreme Scale (FTXS) Workshop at The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'19), November 2019.  (440.7 KB)
Losada, N., G. Bosilca, A. Bouteiller, P. González, and M. J. Martín, “Local Rollback for Resilient MPI Applications with Application-Level Checkpointing and Message Logging,” Future Generation Computer Systems, vol. 91, pp. 450-464, February 2019. DOI: 10.1016/j.future.2018.09.041  (1.16 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)
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, M. G., W. Joubert, V. Larrea, O. Hernandez, A. Haidar, S. Tomov, and J. Dongarra, “Evaluation of Directive-Based Performance Portable Programming Models,” International Journal of High Performance Computing and Networking, vol. 14, issue 2, pp. 165-182. DOI: http://dx.doi.org/10.1504/IJHPCN.2017.10009064  (1.12 MB)
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)
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)
London, K., S. Moore, P. Mucci, K. Seymour, and R. Luczak, “The PAPI Cross-Platform Interface to Hardware Performance Counters,” Department of Defense Users' Group Conference Proceedings, Biloxi, Mississippi, June 2001.  (328.56 KB)
London, K., J. Dongarra, S. Moore, P. Mucci, K. Seymour, and T. Spencer, “End-user Tools for Application Performance Analysis, Using Hardware Counters,” International Conference on Parallel and Distributed Computing Systems, Dallas, TX, August 2001.  (306.54 KB)
Lindquist, N., P. Luszczek, and J. Dongarra, “Replacing Pivoting in Distributed Gaussian Elimination with Randomized Techniques,” 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA), Atlanta, GA, IEEE, November 2020.  (184.6 KB)
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, November 2022. DOI: 10.1109/ScalAH56622.2022.00010  (721.77 KB)
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)
Lindquist, N., P. Luszczek, and J. Dongarra, Generalizing Random Butterfly Transforms to Arbitrary Matrix Sizes : arXiv, December 2023.
Lindquist, N., P. Luszczek, and J. Dongarra, “Accelerating Restarted GMRES with Mixed Precision Arithmetic,” IEEE Transactions on Parallel and Distributed Systems, June 2021. DOI: 10.1109/TPDS.2021.3090757  (572.4 KB)
Lindquist, N., P. Luszczek, and J. Dongarra, “Using Additive Modifications in LU Factorization Instead of Pivoting,” 37th ACM International Conference on Supercomputing (ICS'23), Orlando, FL, ACM, June 2023. DOI: 10.1145/3577193.3593731  (624.18 KB)
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
Li, J., G. Bosilca, A. Bouteiller, and B. Nicolae, “Elastic deep learning through resilient collective operations,” SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, ACM, November 2023. DOI: 10.1145/3624062.3626080
Li, J., B. Nicolae, J. M. Wozniak, and G. Bosilca, “Understanding Scalability and Fine-Grain Parallelism of Synchronous Data Parallel Training,” 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), Denver, CO, IEEE, November 2019. DOI: 10.1109/MLHPC49564.2019.00006  (696.89 KB)
Li, Y., J. Dongarra, and S. Tomov, “A Note on Auto-tuning GEMM for GPUs,” 9th International Conference on Computational Science (ICCS 2009), no. 5544-5545, Baton Rouge, LA, pp. 884-892, May 2009. DOI: 10.1007/978-3-642-01970-8_89  (236.02 KB)
Le Fèvre, V., T. Herault, Y. Robert, A. Bouteiller, A. Hori, G. Bosilca, and J. Dongarra, “Comparing the Performance of Rigid, Moldable, and Grid-Shaped Applications on Failure-Prone HPC Platforms,” Parallel Computing, vol. 85, pp. 1–12, July 2019. DOI: 10.1016/j.parco.2019.02.002  (865.18 KB)
Le Fèvre, V., G. Bosilca, A. Bouteiller, T. Herault, A. Hori, Y. Robert, and J. Dongarra, “Do moldable applications perform better on failure-prone HPC platforms?,” 11th Workshop on Resiliency in High Performance Computing in Clusters, Clouds, and Grids, Turin, Italy, Springer Verlag, August 2018.  (360.72 KB)
Lacoste, X., M. Faverge, P. Ramet, S. Thibault, and G. Bosilca, “Taking Advantage of Hybrid Systems for Sparse Direct Solvers via Task-Based Runtimes,” 23rd International Heterogeneity in Computing Workshop, IPDPS 2014, Phoenix, AZ, IEEE, May 2014.  (807.33 KB)
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Kurzak, J., P. Luszczek, A. YarKhan, M. Faverge, J. Langou, H. Bouwmeester, and J. Dongarra, “Multithreading in the PLASMA Library,” Multi and Many-Core Processing: Architecture, Programming, Algorithms, & Applications: Taylor & Francis, 00 2013.  (536.28 KB)
Kurzak, J., S. Tomov, and J. Dongarra, “Autotuning GEMM Kernels for the Fermi GPU,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 11, November 2012. DOI: 10.1109/TPDS.2011.311  (742.5 KB)
Kurzak, J., P. Luszczek, I. Yamazaki, Y. Robert, and J. Dongarra, “Design and Implementation of the PULSAR Programming System for Large Scale Computing,” Supercomputing Frontiers and Innovations, vol. 4, issue 1, 2017. DOI: 10.14529/jsfi170101  (764.96 KB)
Kurzak, J., M. Gates, A. Charara, A. YarKhan, and J. Dongarra, “SLATE Working Note 12: Implementing Matrix Inversions,” SLATE Working Notes, no. 12, ICL-UT-19-04: Innovative Computing Laboratory, University of Tennessee, June 2019.  (1.95 MB)
Kurzak, J., M. Gates, A. YarKhan, I. Yamazaki, P. Luszczek, J. Finney, and J. Dongarra, “Parallel Norms Performance Report,” SLATE Working Notes, no. 06, ICL-UT-18-06: Innovative Computing Laboratory, University of Tennessee, June 2018.  (1.13 MB)
Kurzak, J., P. Luszczek, and J. Dongarra, “LU Factorization with Partial Pivoting for a Multicore System with Accelerators,” IEEE Transactions on Parallel and Distributed Computing, vol. 24, issue 8, pp. 1613-1621, August 2013. DOI: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.242  (1.08 MB)

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