Publications

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Nelson, J., Analyzing PAPI Performance on Virtual Machines,” VMWare Technical Journal, vol. Winter 2013, January 2014.
Nelson, J., Analyzing PAPI Performance on Virtual Machines,” ICL Technical Report, no. ICL-UT-13-02, August 2013.  (437.37 KB)
Song, F., S. Moore, and J. Dongarra, Analytical Modeling for Affinity-Based Thread Scheduling on Multicore Platforms,” University of Tennessee Computer Science Technical Report, UT-CS-08-626, January 2008.  (650.75 KB)
Song, F., S. Moore, and J. Dongarra, Analytical Modeling and Optimization for Affinity Based Thread Scheduling on Multicore Systems,” IEEE Cluster 2009, New Orleans, August 2009.  (395.53 KB)
Luszczek, P., and J. Dongarra, Analysis of Various Scalar, Vector, and Parallel Implementations of RandomAccess,” Innovative Computing Laboratory (ICL) Technical Report, no. ICL-UT-10-03, June 2010.  (226.9 KB)
Haidar, A., H. Ltaeif, A. YarKhan, and J. Dongarra, Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,” University of Tennessee Computer Science Technical Report, UT-CS-11-666, (also Lawn 243), 00 2011.  (1.65 MB)
Haidar, A., H. Ltaeif, A. YarKhan, and J. Dongarra, Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,” Submitted to Concurrency and Computations: Practice and Experience, November 2010.  (1.65 MB)
Andersson, U., and P. Mucci, Analysis and Optimization of Yee_Bench using Hardware Performance Counters,” Proceedings of Parallel Computing 2005 (ParCo), Malaga, Spain, January 2005.  (72.27 KB)
Abdelfattah, A., A. Haidar, S. Tomov, and J. Dongarra, Analysis and Design Techniques towards High-Performance and Energy-Efficient Dense Linear Solvers on GPUs,” IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 12, pp. 2700–2712, December 2018. DOI: 10.1109/TPDS.2018.2842785
Masliah, I., A. Abdelfattah, A. Haidar, S. Tomov, M. Baboulin, J. Falcou, and J. Dongarra, Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices,” Parallel Computing, vol. 81, pp. 1–21, January 2019. DOI: 10.1016/j.parco.2018.10.003  (3.27 MB)
Masliah, I., A. Abdelfattah, A. Haidar, S. Tomov, M. Baboulin, J. Falcou, and J. Dongarra, Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices,” Innovative Computing Laboratory Technical Report, no. ICL-UT-18-09: Innovative Computing Laboratory, University of Tennessee, September 2018.  (3.74 MB)
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)
Petitet, A., and J. Dongarra, Algorithmic Redistribution Methods for Block Cyclic Decompositions,” IEEE Transactions on Parallel and Distributed Computing, vol. 10, no. 12, pp. 201-220, October 2002.  (524.82 KB)
Boulet, P., J. Dongarra, F. Rastello, Y. Robert, and F. Vivien, Algorithmic Issues on Heterogeneous Computing Platforms,” Parallel Processing Letters, vol. 9, no. 2, pp. 197-213, January 1999.  (301.17 KB)
Dongarra, J., G. Bosilca, R. Delmas, and J. Langou, Algorithmic Based Fault Tolerance Applied to High Performance Computing,” Journal of Parallel and Distributed Computing, vol. 69, pp. 410-416, 00 2009.  (313.55 KB)
Bosilca, G., R. Delmas, J. Dongarra, and J. Langou, Algorithmic Based Fault Tolerance Applied to High Performance Computing,” University of Tennessee Computer Science Technical Report, UT-CS-08-620 (also LAPACK Working Note 205), January 2008.  (313.55 KB)
Chen, Z., and J. Dongarra, Algorithm-Based Fault Tolerance for Fail-Stop Failures,” IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 12, January 2008.  (340.49 KB)
Bouteiller, A., T. Herault, G. Bosilca, P. Du, and J. Dongarra, Algorithm-based Fault Tolerance for Dense Matrix Factorizations, Multiple Failures, and Accuracy,” ACM Transactions on Parallel Computing, vol. 1, issue 2, no. 10, pp. 10:1-10:28, January 2015. DOI: 10.1145/2686892  (1.14 MB)
Du, P., A. Bouteiller, G. Bosilca, T. Herault, and J. Dongarra, Algorithm-based Fault Tolerance for Dense Matrix Factorizations,” University of Tennessee Computer Science Technical Report, no. UT-CS-11-676, Knoxville, TN, August 2011.  (865.79 KB)
Du, P., A. Bouteiller, G. Bosilca, T. Herault, and J. Dongarra, Algorithm-Based Fault Tolerance for Dense Matrix Factorization,” Proceedings of the 17th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP 2012, New Orleans, LA, USA, ACM, pp. 225-234, February 2012. DOI: 10.1145/2145816.2145845  (865.79 KB)
Chen, Z., and J. Dongarra, Algorithm-Based Checkpoint-Free Fault Tolerance for Parallel Matrix Computations on Volatile Resources,” IPDPS 2006, 20th IEEE International Parallel and Distributed Processing Symposium, Rhodes Island, Greece, January 2006.  (266.54 KB)
Chen, Z., and J. Dongarra, Algorithm-Based Checkpoint-Free Fault Tolerance for Parallel Matrix Computations on Volatile Resources,” University of Tennessee Computer Science Department Technical Report, vol. –05-561, November 2005.  (266.54 KB)
Agullo, E., L. Giraud, A. Guermouche, A. Haidar, S. Lanteri, and J. Roman, Algebraic Schwarz Preconditioning for the Schur Complement: Application to the Time-Harmonic Maxwell Equations Discretized by a Discontinuous Galerkin Method.,” The Twentieth International Conference on Domain Decomposition Methods, La Jolla, California, February 2011.
Song, F., F. Wolf, N. Bhatia, J. Dongarra, and S. Moore, An Algebra for Cross-Experiment Performance Analysis,” 2004 International Conference on Parallel Processing (ICCP-04), Montreal, Quebec, Canada, August 2004.  (166.12 KB)
Casanova, H., M H. Kim, J. Plank, and J. Dongarra, Adaptive Scheduling for Task Farming with Grid Middleware,” International Journal of Supercomputer Applications and High-Performance Computing, vol. 13, no. 3, pp. 231-240, October 2002.  (461.08 KB)
Anzt, H., J. Dongarra, and E. S. Quintana-Ortí, Adaptive Precision Solvers for Sparse Linear Systems,” 3rd International Workshop on Energy Efficient Supercomputing (E2SC '15), Austin, TX, ACM, November 2015.
Anzt, H., J. Dongarra, G. Flegar, N. J. Higham, and E. S. Quintana-Ortí, Adaptive Precision in Block‐Jacobi Preconditioning for Iterative Sparse Linear System Solvers,” Concurrency Computation: Practice and Experience, March 2018. DOI: 10.1002/cpe.4460
Anzt, H., J. Dongarra, G. Flegar, N. J. Higham, and E. S. Quintana-Orti, Adaptive precision in block-Jacobi preconditioning for iterative sparse linear system solvers,” Concurrency and Computation: Practice and Experience, vol. 31, no. 6, pp. e4460, 2019. DOI: 10.1002/cpe.4460  (341.54 KB)
Luo, X., W. Wu, G. Bosilca, T. Patinyasakdikul, L. Wang, and J. Dongarra, ADAPT: An Event-Based Adaptive Collective Communication Framework,” The 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '18), Tempe, Arizona, ACM Press, June 2018. DOI: 10.1145/3208040.3208054  (493.65 KB)
Moore, S., A.J.. Baker, J. Dongarra, C. Halloy, and C. Ng, Active Netlib: An Active Mathematical Software Collection for Inquiry-based Computational Science and Engineering Education,” Journal of Digital Information special issue on Interactivity in Digital Libraries, vol. 2, no. 4, 00 2002.  (182.59 KB)
Beck, M., J. Dongarra, J. Huang, T. Moore, and J. Plank, Active Logistical State Management in the GridSolve/L,” 4th International Symposium on Cluster Computing and the Grid (CCGrid 2004)(submitted), Chicago, Illinois, January 2004.  (123.69 KB)
Dongarra, J., M. Faverge, H. Ltaeif, and P. Luszczek, Achieving Numerical Accuracy and High Performance using Recursive Tile LU Factorization,” University of Tennessee Computer Science Technical Report (also as a LAWN), no. ICL-UT-11-08, September 2011.  (618.53 KB)
Dongarra, J., M. Faverge, H. Ltaeif, and P. Luszczek, Achieving numerical accuracy and high performance using recursive tile LU factorization with partial pivoting,” Concurrency and Computation: Practice and Experience, vol. 26, issue 7, pp. 1408-1431, May 2014. DOI: 10.1002/cpe.3110  (1.96 MB)
Dongarra, J., S. Moore, P. Mucci, K. Seymour, and H. You, Accurate Cache and TLB Characterization Using Hardware Counters,” International Conference on Computational Science (ICCS 2004), Krakow, Poland, Springer, June 2004. DOI: 10.1007/978-3-540-24688-6_57  (167.1 KB)
Yamazaki, I., T. Mary, J. Kurzak, S. Tomov, and J. Dongarra, Access-averse Framework for Computing Low-rank Matrix Approximations,” First International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, Washington, DC, October 2014.
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.
Anzt, H., W. Sawyer, S. Tomov, P. Luszczek, and J. Dongarra, Acceleration of GPU-based Krylov solvers via Data Transfer Reduction,” International Journal of High Performance Computing Applications, 2015.
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.  (739.11 KB)
Gates, M., S. Tomov, and J. Dongarra, Accelerating the SVD Two Stage Bidiagonal Reduction and Divide and Conquer Using GPUs,” Parallel Computing, vol. 74, pp. 3–18, May 2018. DOI: 10.1016/j.parco.2017.10.004
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. DOI: 10.1016/j.jocs.2018.01.007  (2.18 MB)
Tomov, S., R. Nath, and J. Dongarra, Accelerating the Reduction to Upper Hessenberg, Tridiagonal, and Bidiagonal Forms through Hybrid GPU-Based Computing,” Parallel Computing, vol. 36, no. 12, pp. 645-654, 00 2010.  (1.39 MB)
Tomov, S., and J. Dongarra, Accelerating the Reduction to Upper Hessenberg Form through Hybrid GPU-Based Computing,” University of Tennessee Computer Science Technical Report, UT-CS-09-642 (also LAPACK Working Note 219), May 2009.  (2.37 MB)
Anzt, H., S. Tomov, and J. Dongarra, Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,” University of Tennessee Computer Science Technical Report, no. UT-EECS-14-731: University of Tennessee, October 2014.  (1.83 MB)
Anzt, H., S. Tomov, and J. Dongarra, Accelerating the LOBPCG method on GPUs using a blocked Sparse Matrix Vector Product,” Spring Simulation Multi-Conference 2015 (SpringSim'15), Alexandria, VA, SCS, April 2015.  (1.46 MB)
Anzt, H., M. Baboulin, J. Dongarra, Y. Fournier, F. Hulsemann, A. Khabou, and Y. Wang, Accelerating the Conjugate Gradient Algorithm with GPU in CFD Simulations,” VECPAR, 2016.
Abdelfattah, A., M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, T. Kolev, I. Masliah, et al., Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched , Atlanta, GA, SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation, March 2017.  (9.29 MB)
Haidar, A., A. Abdelfattah, V. Dobrev, I. Karlin, T. Kolev, S. Tomov, and J. Dongarra, Accelerating Tensor Contractions for High-Order FEM on CPUs, GPUs, and KNLs , Gatlinburg, TN, moky Mountains Computational Sciences and Engineering Conference (SMC16), Poster, September 2016.  (4.29 MB)
Jagode, H., A. Danalis, and J. Dongarra, Accelerating NWChem Coupled Cluster through Dataflow-Based Execution,” The International Journal of High Performance Computing Applications, pp. 1–13, January 2017. DOI: 10.1177/1094342016672543  (4.07 MB)
Jagode, H., A. Danalis, and J. Dongarra, Accelerating NWChem Coupled Cluster through dataflow-based Execution,” The International Journal of High Performance Computing Applications, vol. 32, issue 4, pp. 540--551, July 2018. DOI: 10.1177/1094342016672543  (1.68 MB)
Jagode, H., A. Danalis, G. Bosilca, and J. Dongarra, Accelerating NWChem Coupled Cluster through dataflow-based Execution,” 11th International Conference on Parallel Processing and Applied Mathematics (PPAM 2015), Krakow, Poland, Springer International Publishing, September 2015.  (452.82 KB)

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