TNT-NN: A Fast Active Set Method for Solving Large Non-Negative Least Squares ProblemsMyre, J. M., Frahm, E., Lilja, D. J., and Saar, M. O.2017Journal Article Peer-ReviewedProcedia Computer Science 108, pp. 755–764Abstract ▾DOI: 10.1016/j.procs.2017.05.194Cite
Performance analysis of single‐phase, multiphase, and multicomponent lattice‐Boltzmann fluid flow simulations on GPU clustersMyre, J. M., Walsh, S. D. C., Lilja, D. J., and Saar, M. O.2010Journal Article Peer-ReviewedConcurrency and Computation: Practice and Experience 23, pp. 332–350Abstract ▾DOI: 10.1002/cpe.1645Cite
Accelerating geoscience and engineering system simulations on graphics hardwareWalsh, S. D. C., Saar, M. O., Bailey, P., and Lilja, D. J.2009Journal Article Peer-ReviewedComputers & Geosciences 35, pp. 2353–2364Abstract ▾(Paper accepted: 2009-12-01)DOI: 10.1016/j.cageo.2009.05.001Cite
TNT: A Solver for Large Dense Least-Squares Problems that Takes Conjugate Gradient from Bad in Theory, to Good in PracticeMyre, J. M., Frahm, E., Lilja, D. J., and Saar, M. O.2018Presentation Peer-Reviewed2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)Abstract ▾DOI: 10.1109/ipdpsw.2018.00153Cite
Accelerating Lattice Boltzmann Fluid Flow Simulations Using Graphics ProcessorsBailey, P., Myre, J. M., Walsh, S. D. C., Lilja, D. J., and Saar, M. O.2009Presentation Peer-Reviewed2009 International Conference on Parallel ProcessingAbstract ▾DOI: 10.1109/icpp.2009.38Cite
TNT: A Preconditioned Method for Applying Conjugate Gradient to Dense Least-Squares ProblemsMyre, J. M., Frahm, E., Lilja, D. J., and Saar, M. O.2016OtherCite
