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Bill Gropp

University of Illinois at Urbana-Champaign

Biological Sciences

2018

Gropp, W. (2018): Using Node Information to Implement MPI Cartesian Topologies, (submitted)
Bienz, A., W. Gropp, and L. Olson (2018): Improving Performance Models for Irregular Point-to-Point Communication, (submitted)
Jon Calhoun, Franck Cappello, Luke N. Olson, Marc Snir, and William D. Gropp (2018): Exploring the Feasibility of Lossy Compression for PDE Simulations, The International Journal of High Performance Computing Applications, SAGE Publications, pp109434201876203

2017

Amanda Bienz, William D. Gropp, Luke N. Olson (2017): Node Aware Sparse Matrix-Vector Multiplication, SIAM Journal on Scientific Computing (submitted)
Jon Calhoun, Marc Snir, Luke N. Olson, and William D. Gropp (2017): Towards a More Complete Understanding of SDC Propagation, ACM Press, Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '17), pp131, Washington, D.C., U.S.A.

2016

Paul R. Eller, and William Gropp (2016): Scalable Non-Blocking Preconditioned Conjugate Gradient Methods, IEEE, SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, pp204-215
William Gropp, Luke N. Olson, and Philipp Samfass (2016): Modeling MPI Communication Performance on SMP Nodes: Is it Time to Retire the Ping Pong Test?, ACM Press, Proceedings of the 23rd European MPI Users' Group Meeting (EuroMPI 2016), pp41-50, Edinburgh, Scotland, U.K.
Amanda Bienz, Robert D. Falgout, William Gropp, Luke N. Olson, and Jacob B. Schroder (2016): Reducing Parallel Communication in Algebraic Multigrid Through Sparsification, SIAM Journal on Scientific Computing, Society for Industrial & Applied Mathematics (SIAM), Vol 38, Num 5, ppS332--S357
D. Guo, W. Gropp, and L. N. Olson (2016): A Hybrid Format for Better Performance of Sparse Matrix-Vector Multiplication on a GPU, International Journal of High Performance Computing Applications, SAGE Publications, Vol 30, Num 1, pp103--120

2015

J. Calhoun, L. Olson, M. Snir, and W.D. Gropp (2015): Towards a More Fault Resilient Multigrid Solver, Society for Computer Simulation International, Proceedings of the High Performance Computing Symposium (HPC '15), pp1-8, Alexandria, Virginia, U.S.A.

2014

Dahai Guo, and William Gropp (2014): Applications of the Streamed Storage Format for Sparse Matrix Operations, The International Journal of High Performance Computing Applications, SAGE Publications, Vol 28, Num 1, pp3--12
Cappello, Franck and Al, Geist and Gropp, William and Kale, Sanjay and Kramer, Bill and Snir, Marc (2014): Toward Exascale Resilience: 2014 Update, Supercomputing Frontiers and Innovations, FSAEIHE South Ural State University (National Research University), Vol 1, Num 1, pp5--28

2013

Antonio J. Peña, Ralf G. Correa Carvalho, James Dinan, Pavan Balaji, Rajeev Thakur, and William Gropp (2013): Analysis of Topology-Dependent MPI Performance on Gemini Networks, Association for Computing Machinery (ACM), Proceedings of the 20th European MPI Users' Group Meeting (EuroMPI '13), pp61-66, Madrid, Spain

2012

Guo, Dahai and Gropp, William (2012): Adaptive Thread Distributions for SpMV on a GPU, University of Illinois at Urbana-Champaign, Proceedings of the Extreme Scaling Workshop, BW-XSEDE '12, pp2:1-2:5, Chicago, Illinois, U.S.A.

2011

Jack Dongarra, Pete Beckman, Terry Moore, Patrick Aerts, Giovanni Aloisio, Jean-Claude Andre, David Barkai, Jean-Yves Berthou, Taisuke Boku, Bertrand Braunschweig, Franck Cappello, Barbara Chapman, Xuebin Chi, Alok Choudhary, Sudip Dosanjh, Thom Dunning, Sandro Fiore, Al Geist, Bill Gropp, Robert Harrison, Mark Hereld, Michael Heroux, Adolfy Hoisie, Koh Hotta, Zhong Jin, Yutaka Ishikawa, Fred Johnson, Sanjay Kale, Richard Kenway, David Keyes, Bill Kramer, Jesus Labarta, Alain Lichnewsky, Thomas Lippert, Bob Lucas, Barney Maccabe, Satoshi Matsuoka, Paul Messina, Peter Michielse, Bernd Mohr, Matthias S. Mueller, Wolfgang E. Nagel, Hiroshi Nakashima, Michael E Papka, Dan Reed, Mitsuhisa Sato, Ed Seidel, John Shalf, David Skinner, Marc Snir, Thomas Sterling, Rick Stevens, Fred Streitz, Bob Sugar, Shinji Sumimoto, William Tang, John Taylor, Rajeev Thakur, Anne Trefethen, Mateo Valero, Aad van der Steen, Jeffrey Vetter, Peg Williams, Robert Wisniewski, and Kathy Yelick (2011): The International Exascale Software Project Roadmap, The International Journal of High Performance Computing Applications, SAGE Publications, Vol 25, Num 1, pp3--60
D. Guo, and W. Gropp (2011): Optimizing Sparse Data Structures for Matrix-Vector Multiply, The International Journal of High Performance Computing Applications, SAGE Publications, Vol 25, Num 1, pp115--131

2009

Franck Cappello, Al Geist, Bill Gropp, Laxmikant Kale, Bill Kramer, and Marc Snir (2009): Toward Exascale Resilience, The International Journal of High Performance Computing Applications, SAGE Publications, Vol 23, Num 4, pp374--388

2017

Bill Gropp (2017): Algorithms for extreme-scale systems, 2017 Blue Waters Annual Report, pp172-173

2016

William Gropp (2016): Algorithms for extreme-scale systems, 2016 Blue Waters Annual Report, pp162-163

2015

William Gropp (2015): Algorithms for extreme-scale systems, 2015 Blue Waters Annual Report, pp124

William Gropp: Algorithms for Extreme-Scale Systems


Blue Waters Symposium 2015, May 11, 2015

Pavan Balaji: System Software for Scalable Applications


Blue Waters Symposium 2014, May 15, 2014

Paul R. Eller: Non-Blocking Preconditioned Conjugate Gradient Methods for Extreme-Scale Computing


The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15); Austin, Texas, U.S.A., Nov 17, 2015

Amanda Bienz, Jon Calhoun, Luke Olson, Marc Snir, and William D. Gropp: Analyzing the Performance of a Sparse Matrix Vector Multiply for Extreme Scale Computers


The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15); Austin, Texas, U.S.A., Nov 17, 2015
William Gropp: Teaching for the Virtual School of Computational Science and Engineering
Blue Waters Symposium 2015, May 12, 2015

Paul Eller: Exploiting Nonblocking Collective Operations in Conjugate Gradient


17th Copper Mountain Conference on Multigrid Methods; Copper Mountain, Colorado, U.S.A., Mar 26, 2015

12 Illinois faculty awarded prestigious Blue Waters Professorships


Feb 4, 2014

Twelve University of Illinois faculty members from a range of fields have been selected as Blue Waters Professors, an honor that comes with substantial computing and data resources on the Blue Waters supercomputer at the university’s National Center for Supercomputing Applications (NCSA).


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Blue Waters Illinois allocations awarded to 26 research teams


Mar 7, 2017

Twenty-six research teams at the University of Illinois at Urbana-Champaign have been allocated computation time on the National Center for Supercomputing Application's (NCSA) sustained-petascale Blue Waters supercomputer after applying in Fall 2016. These allocations range from 25,000 to 600,000 node-hours of compute time over a time span of either six months or one year. The research pursuits of these teams are incredibly diverse, ranging anywhere from physics to political science.


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UIUC’s Supercomputer Has a Projected $1B Impact On Illinois’ Economy


May 12, 2017

Nestled on the outskirts of the University of Illinois at Urbana-Champaign campus — at the corner of Oak Street and St. Mary’s Road — is Blue Waters, a supercomputer that was first instituted as a result of a 2007 National Science Foundation grant and an initial $60 million investment from the State of Illinois. A report released this past week on the economic impact of this supercomputer — on the UIUC campus, its five surrounding counties, as well as nationwide spillover effects — puts a whole new meaning to the term “return on investment.”


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Blue Waters project to offer online course on Designing and Building Applications for Extreme-Scale


Oct 21, 2014

NCSA’s Blue Waters project will offer of an online graduate course on Designing and Building Applications for Extreme-Scale Systems in spring 2015 and is seeking university partners that are interested in offering the course for credit to their students. Many problems in the sciences require more computing power and I/O performance than is available on all but the largest machines. Using these systems effectively requires a quantitative approach to performance, applied from the selection of model and algorithm to the choice of programming languages and libraries. This course will introduce students to the features of extreme-scale systems and how to use performance modeling to design, implement, and tune large-scale applications in simulation and data-intensive science.


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