Publications

Danalis, A., P. Luszczek, G. Marin, J. Vetter, and J. Dongarra, “BlackjackBench: Hardware Characterization with Portable Micro-Benchmarks and Automatic Statistical Analysis of Results,” IEEE International Parallel and Distributed Processing Symposium (submitted), Anchorage, AK, May 2011.

Davis, J., T. Gao, S. Chandrasekaran, H. Jagode, A. Danalis, P. Balaji, J. Dongarra, and M. Taufer, “Characterization of Power Usage and Performance in Data-Intensive Applications using MapReduce over MPI,” 2019 International Conference on Parallel Computing (ParCo2019), Prague, Czech Republic, September 2019.

Demmel, J., J. Dongarra, B.. Parlett, W. Kahan, M. Gu, D. Bindel, Y. Hida, X. Li, O. Marques, J. E. Riedy, et al., “Prospectus for the Next LAPACK and ScaLAPACK Libraries,” PARA 2006, Umea, Sweden, June 2006.

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Demmel, J., and J. Dongarra, LAPACK 2005 Prospectus: Reliable and Scalable Software for Linear Algebra Computations on High End Computers : LAPACK Working Note 164, January 2005.

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Demmel, J., J. Dongarra, V. Eijkhout, E. Fuentes, A. Petitet, R. Vuduc, C. Whaley, and K. Yelick, “Self Adapting Linear Algebra Algorithms and Software,” IEEE Proceedings (to appear), 00 2004.

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Demmel, J., J. Dongarra, A. Fox, S. Williams, V. Volkov, and K. Yelick, “Accelerating Time-To-Solution for Computational Science and Engineering,” SciDAC Review, 00 2009.

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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)

Dempsey, B., and D. Weiss, “Towards An Efficient, Scalable Replication Mechanism for the I2-DSI Project,” University of North Carolina School of Library and Information Science Technical Report, no. TR-1999-01, January 1999.

Deshmukh, S., R. Yokota, and G. Bosilca, “Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors,” ACM Transactions on Mathematical Software, vol. 49, issue 3, pp. 1 - 29, September 2023.

Deshmukh, S., R. Yokota, G. Bosilca, and Q. Ma, “O(N) distributed direct factorization of structured dense matrices using runtime systems,” 52nd International Conference on Parallel Processing (ICPP 2023), Salt Lake City, Utah, ACM, August 2023.

Dewolfs, D., J. Broeckhove, V. Sunderam, and G. Fagg, “FT-MPI, Fault-Tolerant Metacomputing and Generic Name Services: A Case Study,” Lecture Notes in Computer Science, vol. 4192, no. ICL-UT-06-14: Springer Berlin / Heidelberg, pp. 133-140, 00 2006.

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Donfack, S., J. Dongarra, M. Faverge, M. Gates, J. Kurzak, P. Luszczek, and I. Yamazaki, “On Algorithmic Variants of Parallel Gaussian Elimination: Comparison of Implementations in Terms of Performance and Numerical Properties,” University of Tennessee Computer Science Technical Report, no. UT-CS-13-715, July 2013, 2012.

(358.98 KB)

Donfack, S., S. Tomov, and J. Dongarra, “Performance evaluation of LU factorization through hardware counter measurements,” University of Tennessee Computer Science Technical Report, no. ut-cs-12-700, October 2012.

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Donfack, S., J. Dongarra, M. Faverge, M. Gates, J. Kurzak, P. Luszczek, and I. Yamazaki, “A Survey of Recent Developments in Parallel Implementations of Gaussian Elimination,” Concurrency and Computation: Practice and Experience, vol. 27, issue 5, pp. 1292-1309, April 2015.

(783.45 KB)

Donfack, S., S. Tomov, and J. Dongarra, “Dynamically balanced synchronization-avoiding LU factorization with multicore and GPUs,” University of Tennessee Computer Science Technical Report, no. ut-cs-13-713, July 2013.

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Donfack, S., S. Tomov, and J. Dongarra, “Dynamically balanced synchronization-avoiding LU factorization with multicore and GPUs,” Fourth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2014, May 2014.

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Dong, T., A. Haidar, S. Tomov, and J. Dongarra, “Optimizing the SVD Bidiagonalization Process for a Batch of Small Matrices,” International Conference on Computational Science (ICCS 2017), Zurich, Switzerland, Procedia Computer Science, June 2017.

(364.95 KB)

Dong, T., A. Haidar, S. Tomov, and J. Dongarra, “Accelerating the SVD Bi-Diagonalization of a Batch of Small Matrices using GPUs,” Journal of Computational Science, vol. 26, pp. 237–245, May 2018.

(2.18 MB)

Dong, T., V. Dobrev, T. Kolev, R. Rieben, S. Tomov, and J. Dongarra, “A Step towards Energy Efficient Computing: Redesigning A Hydrodynamic Application on CPU-GPU,” IPDPS 2014, Phoenix, AZ, IEEE, May 2014.

(1.01 MB)

Dong, T., A. Haidar, S. Tomov, and J. Dongarra, “A Fast Batched Cholesky Factorization on a GPU,” International Conference on Parallel Processing (ICPP-2014), Minneapolis, MN, September 2014.

(1.37 MB)

Dong, T., V. Dobrev, T. Kolev, R. Rieben, S. Tomov, and J. Dongarra, “Hydrodynamic Computation with Hybrid Programming on CPU-GPU Clusters,” University of Tennessee Computer Science Technical Report, no. ut-cs-13-714, July 2013.

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Dong, T., T. Kolev, R. Rieben, V. Dobrev, S. Tomov, and J. Dongarra, “Acceleration of the BLAST Hydro Code on GPU,” Supercomputing '12 (poster), Salt Lake City, Utah, SC12, November 2012.

Dong, T., A. Haidar, P. Luszczek, J. Harris, S. Tomov, and J. Dongarra, “LU Factorization of Small Matrices: Accelerating Batched DGETRF on the GPU,” 16th IEEE International Conference on High Performance Computing and Communications (HPCC), Paris, France, IEEE, August 2014.

(684.73 KB)

Dong, T., A. Haidar, P. Luszczek, S. Tomov, A. Abdelfattah, and J. Dongarra, “MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs,” Innovative Computing Laboratory Technical Report, no. ICL-UT-16-02: University of Tennessee, August 2016.

(929.79 KB)

Dongarra, J., “Performance of Various Computers Using Standard Linear Equations Software (Linpack Benchmark Report),” University of Tennessee Computer Science Department Technical Report, UT-CS-04-526, vol. –89-95, January 2006.

(6.42 MB)

Dongarra, J., and P. Luszczek, “How Elegant Code Evolves With Hardware: The Case Of Gaussian Elimination,” in Beautiful Code Leading Programmers Explain How They Think: O'Reilly Media, Inc., June 2007.

(257 KB)

Dongarra, J., P. Kacsuk, and N.. Podhorszki, “Recent Advances in Parallel Virtual Machine and Message Passing Interface,” Lecture Notes in Computer Science: Proceedings of 7th European PVM/MPI Users' Group Meeting 2000, (Hungary: Springer Verlag), pp. V1908, January 2000.

Dongarra, J., “Performance of Various Computers Using Standard Linear Equations Software (Linpack Benchmark Report),” University of Tennessee Computer Science Technical Report, UT-CS-89-85, 00 2010.

(6.42 MB)

Dongarra, J., T. Dong, M. Gates, A. Haidar, S. Tomov, and I. Yamazaki, MAGMA: A New Generation of Linear Algebra Library for GPU and Multicore Architectures , Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), Presentation, November 2012.

(4.69 MB)

Dongarra, J., D. Laforenza, and S.. Orlando, “Recent Advances in Parallel Virtual Machine and Message Passing Interface,” Lecture Notes in Computer Science, vol. 2840: Springer-Verlag, Berlin, January 2003.

Dongarra, J., “Performance of Various Computers Using Standard Linear Equations Software,” University of Tennessee Computer Science Technical Report, no. cs-89-85, February 2013.

(539.24 KB)

Dongarra, J., and S. Moore, “Empirical Performance Tuning of Dense Linear Algebra Software,” in Performance Tuning of Scientific Applications (to appear), 00 2010.

Dongarra, J., M. Faverge, T. Herault, J. Langou, and Y. Robert, “Hierarchical QR Factorization Algorithms for Multi-Core Cluster Systems,” IPDPS 2012, the 26th IEEE International Parallel and Distributed Processing Symposium, Shanghai, China, IEEE Computer Society Press, May 2012.

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Dongarra, J., S. Moore, and A. Trefethen, “Numerical Libraries and Tools for Scalable Parallel Cluster Computing,” International Journal of High Performance Applications and Supercomputing, vol. 15, no. 2, pp. 175-180, January 2001.

(37.38 KB)

Dongarra, J., and V. Eijkhout, “Self Adapting Numerical Algorithm for Next Generation Applications,” International Journal of High Performance Computing Applications, vol. 17, no. 2, pp. 125-132, January 2003.

(479.18 KB)

Dongarra, J., M. A. Heroux, and P. Luszczek, “A New Metric for Ranking High-Performance Computing Systems,” National Science Review, vol. 3, issue 1, pp. 30-35, January 2016.

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Dongarra, J., S. Hammarling, N. J. Higham, S. Relton, and M. Zounon, “Optimized Batched Linear Algebra for Modern Architectures,” Euro-Par 2017, Santiago de Compostela, Spain, Springer, August 2017.

(618.33 KB)

Dongarra, J., “Performance of Various Computers Using Standard Linear Equations Software (Linpack Benchmark Report),” University of Tennessee Computer Science Technical Report, no. CS-89-85, 00 2011.

(6.42 MB)

Dongarra, J., M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, and I. Yamazaki, “Accelerating Numerical Dense Linear Algebra Calculations with GPUs,” Numerical Computations with GPUs: Springer International Publishing, pp. 3-28, 2014.

(1.06 MB)

Dongarra, J., and V. Eijkhout, “Numerical Linear Algebra Algorithms and Software,” Journal of Computational and Applied Mathematics, vol. 123, no. 1-2, pp. 489-514, October 1999.

(258.62 KB)

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)

Dongarra, J., V. Eijkhout, and H. van der Vorst, “An Iterative Solver Benchmark,” Scientific Programming (to appear), 00 2002.

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Dongarra, J., T. Herault, and Y. Robert, “Revisiting the Double Checkpointing Algorithm,” 15th Workshop on Advances in Parallel and Distributed Computational Models, at the IEEE International Parallel & Distributed Processing Symposium, Boston, MA, May 2013.

(591.1 KB)

Dongarra, J., “Report on the Fujitsu Fugaku System,” Innovative Computing Laboratory Technical Report, no. ICL-UT-20-06: University of Tennessee, June 2020.

(3.3 MB)

Dongarra, J., T. Herault, and Y. Robert, “Fault Tolerance Techniques for High-performance Computing,” University of Tennessee Computer Science Technical Report (also LAWN 289), no. UT-EECS-15-734: University of Tennessee, May 2015.