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Danalis, A., H. Jagode, and J. Dongarra, Is your scheduling good? How would you know? , Bordeaux, France, 14th Scheduling for Large Scale Systems Workshop, June 2019.  (2.5 MB)
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Hoefler, T., M. Copik, P. Beckman, A. Jones, I. Foster, M. Parashar, D. Reed, M. Troyer, T. Schulthess, D. Ernst, et al., XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing : arXiv, January 2024.
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Dongarra, J., S. Tomov, P. Luszczek, J. Kurzak, M. Gates, I. Yamazaki, H. Anzt, A. Haidar, and A. Abdelfattah, “With Extreme Computing, the Rules Have Changed,” Computing in Science & Engineering, vol. 19, issue 3, pp. 52-62, May 2017. DOI: 10.1109/MCSE.2017.48  (485.34 KB)
Jagode, H., A. Danalis, and J. Dongarra, What it Takes to keep PAPI Instrumental for the HPC Community , Collegeville, MN, The 2019 Collegeville Workshop on Sustainable Scientific Software (CW3S19), July 2019.  (3.29 MB)
Jagode, H., A. Danalis, and J. Dongarra, “What it Takes to keep PAPI Instrumental for the HPC Community,” 1st Workshop on Sustainable Scientific Software (CW3S19), Collegeville, Minnesota, July 2019.  (50.57 KB)
Haidar, A., Y. Jia, P. Luszczek, S. Tomov, A. YarKhan, and J. Dongarra, “Weighted Dynamic Scheduling with Many Parallelism Grains for Offloading of Numerical Workloads to Multiple Varied Accelerators,” Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA'15), vol. No. 5, Austin, TX, ACM, November 2015.  (347.6 KB)
Anzt, H., J. Dongarra, and V. Heuveline, “Weighted Block-Asynchronous Relaxation for GPU-Accelerated Systems,” SIAM Journal on Computing (submitted), March 2012.  (811.01 KB)
Anzt, H., S. Tomov, J. Dongarra, and V. Heuveline, “Weighted Block-Asynchronous Iteration on GPU-Accelerated Systems,” Tenth International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (Best Paper), Rhodes Island, Greece, August 2012.  (764.02 KB)
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Haugen, B., S. Richmond, J. Kurzak, C. A. Steed, and J. Dongarra, “Visualizing Execution Traces with Task Dependencies,” 2nd Workshop on Visual Performance Analysis (VPA '15), Austin, TX, ACM, November 2015.  (927.5 KB)
Lee, DW., and J. Dongarra, “VisPerf: Monitoring Tool for Grid Computing,” Lecture Notes in Computer Science, vol. 2659: Springer Verlag, Heidelberg, pp. 233-243, 00 2003.  (835.09 KB)
Kurzak, J., P. Luszczek, M. Gates, I. Yamazaki, and J. Dongarra, “Virtual Systolic Array for QR Decomposition,” 15th Workshop on Advances in Parallel and Distributed Computational Models, IEEE International Parallel & Distributed Processing Symposium (IPDPS 2013), Boston, MA, IEEE, May 2013. DOI: 10.1109/IPDPS.2013.119  (749.84 KB)
Casanova, H., T. Bartol, F. Berman, A. Birnbaum, J. Dongarra, M. Ellisman, M. Faerman, E. Gockay, M. Miller, G. Obertelli, et al., “The Virtual Instrument: Support for Grid-enabled Scientific Simulations,” Journal of Parallel and Distributed Computing (submitted), October 2002.  (282.16 KB)
Casanova, H., T. Bartol, F. Berman, A. Birnbaum, J. Dongarra, M. Ellisman, M. Faerman, E. Gockay, M. Miller, G. Obertelli, et al., “The Virtual Instrument: Support for Grid-enabled Scientific Simulations,” International Journal of High Performance Computing Applications, vol. 18, no. 1, pp. 3-17, January 2004.  (282.16 KB)
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “Variable-Size Batched LU for Small Matrices and Its Integration into Block-Jacobi Preconditioning,” 46th International Conference on Parallel Processing (ICPP), Bristol, United Kingdom, IEEE, August 2017. DOI: 10.1109/ICPP.2017.18
Anzt, H., J. Dongarra, G. Flegar, and E. S. Quintana-Orti, “Variable-Size Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioning on Graphics Processors,” Parallel Computing, vol. 81, pp. 131-146, January 2019. DOI: 10.1016/j.parco.2017.12.006  (1.9 MB)
Anzt, H., J. Dongarra, G. Flegar, E. S. Quintana-Orti, and A. E. Thomas, “Variable-Size Batched Gauss-Huard for Block-Jacobi Preconditioning,” International Conference on Computational Science (ICCS 2017), vol. 108, Zurich, Switzerland, Procedia Computer Science, pp. 1783-1792, June 2017. DOI: 10.1016/j.procs.2017.05.186  (512.57 KB)
Anzt, H., J. Dongarra, G. Flegar, and T. Gruetzmacher, “Variable-Size Batched Condition Number Calculation on GPUs,” SBAC-PAD, Lyon, France, September 2018.  (509.3 KB)
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McCraw, H., A. Danalis, G. Bosilca, J. Dongarra, K. Kowalski, and T. Windus, “Utilizing Dataflow-based Execution for Coupled Cluster Methods,” 2014 IEEE International Conference on Cluster Computing, no. ICL-UT-14-02, Madrid, Spain, IEEE, September 2014.  (260.23 KB)
Tsai, Y., P. Luszczek, and J. Dongarra, Using Quantized Integer in LU Factorization with Partial Pivoting (Poster) , Seattle, WA, SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP20), February 2020.  (6.65 MB)
Dongarra, J., K. London, S. Moore, P. Mucci, and D. Terpstra, “Using PAPI for Hardware Performance Monitoring on Linux Systems,” Conference on Linux Clusters: The HPC Revolution, Urbana, Illinois, Linux Clusters Institute, June 2001.  (422.35 KB)
Buttari, A., J. Dongarra, J. Kurzak, P. Luszczek, and S. Tomov, “Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy,” ACM Transactions on Mathematical Software, vol. 34, no. 4, pp. 17-22, 00 2008.  (364.48 KB)
Tomov, S., M. Faverge, P. Luszczek, and J. Dongarra, “Using MAGMA with PGI Fortran,” PGI Insider, November 2010.  (176.67 KB)
Chow, E., H. Anzt, J. Scott, and J. Dongarra, “Using Jacobi Iterations and Blocking for Solving Sparse Triangular Systems in Incomplete Factorization Preconditioning,” Journal of Parallel and Distributed Computing, vol. 119, pp. 219–230, November 2018. DOI: 10.1016/j.jpdc.2018.04.017  (273.53 KB)
Fürlinger, K., J. Dongarra, and M. Gerndt, “On Using Incremental Profiling for the Performance Analysis of Shared Memory Parallel Applications,” Proceedings of the 13th International Euro-Par Conference on Parallel Processing (Euro-Par '07), Rennes, France, Springer LNCS, January 2007.
Haidar, A., S. Tomov, A. Abdelfattah, M. Zounon, and J. Dongarra, Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption , Frankfurt, Germany, ISC High Performance (ISC18), Best Poster Award, June 2018.  (3.01 MB)
Haidar, A., S. Tomov, A. Abdelfattah, M. Zounon, and J. Dongarra, “Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption,” ISC High Performance (ISC'18), Best Poster, Frankfurt, Germany, June 2018.  (3.01 MB)
Zhong, D., P. Shamis, Q. Cao, G. Bosilca, and J. Dongarra, “Using Arm Scalable Vector Extension to Optimize Open MPI,” 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID 2020), Melbourne, Australia, IEEE/ACM, May 2020. DOI: 10.1109/CCGrid49817.2020.00-71  (359.95 KB)
Zhong, D., G. Bosilca, Q. Cao, and J. Dongarra, Using Advanced Vector Extensions AVX-512 for MPI Reduction (Poster) , Austin, TX, EuroMPI/USA '20: 27th European MPI Users' Group Meeting, September 2020.  (708.68 KB)
Zhong, D., Q. Cao, G. Bosilca, and J. Dongarra, “Using Advanced Vector Extensions AVX-512 for MPI Reduction,” EuroMPI/USA '20: 27th European MPI Users' Group Meeting, Austin, TX, September 2020. DOI: 10.1145/3416315.3416316  (634.45 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)
Agrawal, S., D. Arnold, S. Blackford, J. Dongarra, M. Miller, K. Sagi, Z. Shi, K. Seymour, and S. Vadhiyar, “Users' Guide to NetSolve v1.4.1,” ICL Technical Report, no. ICL-UT-02-05, June 2002.  (328.01 KB)
Voemel, C., S. Tomov, L-W. Wang, O. Marques, and J. Dongarra, “The Use of Bulk States to Accelerate the Band Edge State Calculation of a Semiconductor Quantum Dot,” Journal of Computational Physics, vol. 223, pp. 774-782, 00 2007.  (452.6 KB)
Voemel, C., S. Tomov, L-W. Wang, O. Marques, and J. Dongarra, “The use of bulk states to accelerate the band edge state calculation of a semiconductor quantum dot,” Journal of Computational Physics (submitted), January 2006.  (337.08 KB)
Anzt, H., E. Chow, J. Saak, and J. Dongarra, “Updating Incomplete Factorization Preconditioners for Model Order Reduction,” Numerical Algorithms, vol. 73, issue 3, no. 3, pp. 611–630, February 2016. DOI: 10.1007/s11075-016-0110-2  (565.34 KB)
Blackford, S., J. Demmel, J. Dongarra, I. Duff, S. Hammarling, G. Henry, M. Heroux, L. Kaufman, A. Lumsdaine, A. Petitet, et al., “An Updated Set of Basic Linear Algebra Subprograms (BLAS),” ACM Transactions on Mathematical Software, vol. 28, no. 2, pp. 135-151, December 2002. DOI: 10.1145/567806.567807  (228.33 KB)
Bosilca, G., A. Bouteiller, E. Brunet, F. Cappello, J. Dongarra, A. Guermouche, T. Herault, Y. Robert, F. Vivien, and D. Zaidouni, “Unified Model for Assessing Checkpointing Protocols at Extreme-Scale,” Concurrency and Computation: Practice and Experience, November 2013. DOI: 10.1002/cpe.3173  (894.61 KB)
Bosilca, G., A. Bouteiller, E. Brunet, F. Cappello, J. Dongarra, A. Guermouche, T. Herault, Y. Robert, F. Vivien, and D. Zaidouni, “Unified Model for Assessing Checkpointing Protocols at Extreme-Scale,” University of Tennessee Computer Science Technical Report (also LAWN 269), no. UT-CS-12-697, June 2012.  (2.76 MB)
Bosilca, G., A. Bouteiller, T. Herault, P. Lemariner, N. Ohm Saengpatsa, S. Tomov, and J. Dongarra, “A Unified HPC Environment for Hybrid Manycore/GPU Distributed Systems,” IEEE International Parallel and Distributed Processing Symposium (submitted), Anchorage, AK, May 2011.
Haidar, A., C. Cao, J. Dongarra, P. Luszczek, and S. Tomov, “Unified Development for Mixed Multi-GPU and Multi-Coprocessor Environments using a Lightweight Runtime Environment,” IPDPS 2014, Phoenix, AZ, IEEE, May 2014.  (1.51 MB)
Danalis, A., H. Jagode, D. Barry, and J. Dongarra, Understanding Native Event Semantics , Knoxville, TN, 9th JLESC Workshop, April 2019.  (2.33 MB)
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Luszczek, P., H. Ltaeif, and J. Dongarra, “Two-stage Tridiagonal Reduction for Dense Symmetric Matrices using Tile Algorithms on Multicore Architectures,” IEEE International Parallel and Distributed Processing Symposium (submitted), Anchorage, AK, May 2011.
Dongarra, J., G. H. Golub, E. Grosse, C. Moler, and K. Moore, “Twenty-Plus Years of Netlib and NA-Net,” University of Tennessee Computer Science Department Technical Report, UT-CS-04-526, 00 2006.  (62.79 KB)
Krzhizhanovskaya, V., G. Závodszky, M. Lees, J. Dongarra, P. Sloot, S. Brissos, and J. Teixeira, “Twenty Years of Computational Science,” International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, June 2020.  (149.66 KB)
Anzt, H., J. Dongarra, and E. S. Quintana-Orti, “Tuning Stationary Iterative Solvers for Fault Resilience,” 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA15), Austin, TX, ACM, November 2015.  (1.28 MB)
Du, P., M. Parsons, E. Fuentes, S-L. Shaw, and J. Dongarra, “Tuning Principal Component Analysis for GRASS GIS on Multi-core and GPU Architectures,” FOSS4G 2010, Barcelona, Spain, September 2010.  (1.57 MB)
Hiroyasu, T., M. Miki, H. Shimosaka, M. Sano, Y. Tanimura, Y. Mimura, S. Yoshimura, and J. Dongarra, “Truss Structural Optimization Using NetSolve System,” Meeting of the Japan Society of Mechanical Engineers, Kyoto University, Kyoto, Japan, October 2002.  (450.65 KB)
Yamazaki, I., T. Dong, S. Tomov, and J. Dongarra, “Tridiagonalization of a Symmetric Dense Matrix on a GPU Cluster,” The Third International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), May 2013.
Yamazaki, I., T. Dong, R. Solcà, S. Tomov, J. Dongarra, and T. C. Schulthess, “Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems,” Concurrency and Computation: Practice and Experience, October 2013.  (1.71 MB)
Dongarra, J., “A Tribute to Gene Golub,” Computing in Science and Engineering: IEEE, pp. 5, January 2008.
Dongarra, J., “Trends in High Performance Computing,” The Computer Journal, vol. 47, no. 4: The British Computer Society, pp. 399-403, 00 2004.  (455.96 KB)

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