Using TNT-NN to unlock the fast full spatial inversion of large magnetic microscopy data setsMyre, J. M., Lascu, I., Lima, E. A., Feinberg, J. M., Saar, M. O., and Weiss, B. P.2019Journal Article Peer-ReviewedEarth, Planets and Space 71Abstract ▾DOI: 10.1186/s40623-019-0988-8Cite
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
Thermal damping and retardation in karst conduitsLuhmann, A. J., Covington, M. D., Myre, J. M., Perne, M., Jones, S. A., Alexander Jr., E. C., and Saar, M. O.2015Journal Article Peer-ReviewedHydrology and Earth System Sciences 19, pp. 137–157Abstract ▾DOI: 10.5194/hess-19-137-2015Cite
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
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
