Kotter\u27s Model for Change and Distributed Leadership: A Multifaceted Approach for Canadian Colleges to Become Less Reliant on Operating Grant Funding

The emergence of performance-based frameworks for funding and declining government operating grant funding are contemporary challenges for Canadian public higher education institutions. Operating grants are a sizable portion of the funding institutions receive from the provincial government, and continued conditions on and declines in these grants pose significant risks to the sustainability and viability of these public institutions. Higher education institutions today need to become less reliant on government funds while remaining aligned with mandates to provide the programs and services necessary to meet the needs of the regions and communities they serve. Frontier College (a pseudonym) has revenue diversification strategies in place, but these strategies were developed with individual departmental needs in mind rather than an institutional focus. This Organizational Improvement Plan demonstrates how a distributed leadership approach with an iterative implementation of Kotter’s eight-step model for change can be used to institutionalize the college’s revenue diversification strategies. Because revenue diversification strategies may involve entrepreneurial activity that is outside typical college operations, the change initiative will be led through the lens of equity, diversity, inclusivity, and decolonization to ensure that all initiatives align with Frontier College’s strategic plan without compromising the institution’s mandates, vision, or mission. This plan also demonstrates how a balanced scorecard can be used as an effective monitoring, evaluation, and communication tool throughout the change process, allowing leaders to collaborate with employees to adjust, amend, and alter plans as they revisit Kotter’s steps together to successfully embed the change within the college’s culture

Krylov-based algebraic multigrid for edge elements

International audienceThis work tackles the evaluation of a multigrid cycling strategy using inner flexible Krylov subspace iterations. It provides a valuable improvement to the Reitzinger and Sch¨oberl algebraic multigrid method for systems coming from edge element discretization

Models for Metal Hydride Particle Shape, Packing, and Heat Transfer

A multiphysics modeling approach for heat conduction in metal hydride powders is presented, including particle shape distribution, size distribution, granular packing structure, and effective thermal conductivity. A statistical geometric model is presented that replicates features of particle size and shape distributions observed experimentally that result from cyclic hydride decreptitation. The quasi-static dense packing of a sample set of these particles is simulated via energy-based structural optimization methods. These particles jam (i.e., solidify) at a density (solid volume fraction) of 0.665+/-0.015 - higher than prior experimental estimates. Effective thermal conductivity of the jammed system is simulated and found to follow the behavior predicted by granular effective medium theory. Finally, a theory is presented that links the properties of bi-porous cohesive powders to the present systems based on recent experimental observations of jammed packings of fine powder. This theory produces quantitative experimental agreement with metal hydride powders of various compositions.Comment: 12 pages, 12 figures, 2 table

Model Integration and Coupling in A Hydroinformatics System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A simple and efficient segregated smoother for the discrete Stokes equations

We consider the multigrid solution of the generalized Stokes equations with a segre- gated (i.e., equationwise) Gauss–Seidel smoother based on a Uzawa-type iteration. We analyze the smoother in the framework of local Fourier analysis, and obtain an analytic bound on the smoothing factor showing uniform performance for a family of Stokes problems. These results are confirmed by the numerical computation of the two-grid convergence factor for different types of grids and dis- cretizations. Numerical results also show that the actual convergence of the W-cycle is approximately the same as that obtained by a Vanka smoother, despite this latter smoother being significantly more costly per iteration step

Adaptive precision in block-Jacobi preconditioning for iterative sparse linear system solvers

This is the peer reviewed version of the following article: Anzt, H, Dongarra, J, Flegar, G, Higham, NJ, Quintana-Ortí, ES. Adaptive precision in block-Jacobi preconditioning for iterative sparse linear system solvers. Concurrency Computat Pract Exper. 2019; 31:e4460, which has been published in final form at https://doi.org/10.1002/cpe.4460. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] We propose an adaptive scheme to reduce communication overhead caused by data movement by selectively storing the diagonal blocks of a block-Jacobi preconditioner in different precision formats (half, single, or double). This specialized preconditioner can then be combined with any Krylov subspace method for the solution of sparse linear systems to perform all arithmetic in double precision. We assess the effects of the adaptive precision preconditioner on the iteration count and data transfer cost of a preconditioned conjugate gradient solver. A preconditioned conjugate gradient method is, in general, a memory bandwidth-bound algorithm, and therefore its execution time and energy consumption are largely dominated by the costs of accessing the problem's data in memory. Given this observation, we propose a model that quantifies the time and energy savings of our approach based on the assumption that these two costs depend linearly on the bit length of a floating point number. Furthermore, we use a number of test problems from the SuiteSparse matrix collection to estimate the potential benefits of the adaptive block-Jacobi preconditioning scheme.Impuls und Vernetzungsfond of the Helmholtz Association, Grant/Award Number: VH-NG-1241; MINECO and FEDER, Grant/Award Number: TIN2014-53495-R; H2020 EU FETHPC Project, Grant/Award Number: 732631; MathWorks; Engineering and Physical Sciences Research Council, Grant/Award Number: EP/P020720/1; Exascale Computing Project, Grant/Award Number: 17-SC-20-SCAnzt, H.; Dongarra, J.; Flegar, G.; Higham, NJ.; Quintana Ortí, ES. (2019). Adaptive precision in block-Jacobi preconditioning for iterative sparse linear system solvers. Concurrency and Computation Practice and Experience. 31(6):1-12. https://doi.org/10.1002/cpe.4460S112316Saad, Y. (2003). Iterative Methods for Sparse Linear Systems. doi:10.1137/1.9780898718003Anzt H Dongarra J Flegar G Quintana-Ortí ES Batched Gauss-Jordan elimination for block-Jacobi preconditioner generation on GPUs 2017 Austin, TX http://doi.acm.org/10.1145/3026937.3026940Anzt H Dongarra J Flegar G Quintana-Ortí ES Variable-size batched LU for small matrices and its integration into block-Jacobi preconditioning 2017 Bristol, UK https://doi.org/10.1109/ICPP.2017.18Dongarra J Hittinger J Bell J Applied Mathematics Research for Exascale Computing [Technical Report] Washington, DC 2014 https://science.energy.gov/~/media/ascr/pdf/research/am/docs/EMWGreport.pdfDuranton M De Bosschere K Cohen A Maebe J Munk H HiPEAC Vision 2015 https://www.hipeac.org/publications/vision/ 2015Lucas R Top Ten Exascale Research Challenges http://science.energy.gov/~/media/ascr/ascac/pdf/meetings/20140210/Top10reportFEB14.pdf 2014Lavignon JF ETP4HPC Strategic Research Agenda Achieving HPC Leadership in Europe 2013 http://www.etp4hpc.eu/Carson, E., & Higham, N. J. (2017). A New Analysis of Iterative Refinement and Its Application to Accurate Solution of Ill-Conditioned Sparse Linear Systems. SIAM Journal on Scientific Computing, 39(6), A2834-A2856. doi:10.1137/17m1122918Carson E Higham NJ Accelerating the solution of linear systems by iterative refinement in three precisions July 2017 http://eprints.ma.man.ac.uk/2562 SIAM Journal on Scientific ComputingShalf J The evolution of programming models in response to energy efficiency constraints October 2013 Norman, OK http://www.oscer.ou.edu/Symposium2013/oksupercompsymp2013_talk_shalf_20131002.pdfGolub, G. H., & Ye, Q. (1999). Inexact Preconditioned Conjugate Gradient Method with Inner-Outer Iteration. SIAM Journal on Scientific Computing, 21(4), 1305-1320. doi:10.1137/s1064827597323415Barrett, R., Berry, M., Chan, T. F., Demmel, J., Donato, J., Dongarra, J., … van der Vorst, H. (1994). Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods. doi:10.1137/1.9781611971538Notay, Y. (2000). Flexible Conjugate Gradients. SIAM Journal on Scientific Computing, 22(4), 1444-1460. doi:10.1137/s1064827599362314Knyazev, A. V., & Lashuk, I. (2008). Steepest Descent and Conjugate Gradient Methods with Variable Preconditioning. SIAM Journal on Matrix Analysis and Applications, 29(4), 1267-1280. doi:10.1137/060675290CROZ, J. J. D., & HIGHAM, N. J. (1992). Stability of Methods for Matrix Inversion. IMA Journal of Numerical Analysis, 12(1), 1-19. doi:10.1093/imanum/12.1.1Higham, N. J. (2002). Accuracy and Stability of Numerical Algorithms. doi:10.1137/1.9780898718027Chow E Scott J On the use of iterative methods and blocking for solving sparse triangular systems in incomplete factorization preconditioning Swindon, UK Rutherford Appleton Laboratory 201

Hierarchical Schur complement preconditioner for the stochastic Galerkin finite element methods

Use of the stochastic Galerkin finite element methods leads to large systems of linear equations obtained by the discretization of tensor product solution spaces along their spatial and stochastic dimensions. These systems are typically solved iteratively by a Krylov subspace method. We propose a preconditioner which takes an advantage of the recursive hierarchy in the structure of the global matrices. In particular, the matrices posses a recursive hierarchical two-by-two structure, with one of the submatrices block diagonal. Each one of the diagonal blocks in this submatrix is closely related to the deterministic mean-value problem, and the action of its inverse is in the implementation approximated by inner loops of Krylov iterations. Thus our hierarchical Schur complement preconditioner combines, on each level in the approximation of the hierarchical structure of the global matrix, the idea of Schur complement with loops for a number of mutually independent inner Krylov iterations, and several matrix-vector multiplications for the off-diagonal blocks. Neither the global matrix, nor the matrix of the preconditioner need to be formed explicitly. The ingredients include only the number of stiffness matrices from the truncated Karhunen-Lo\`{e}ve expansion and a good preconditioned for the mean-value deterministic problem. We provide a condition number bound for a model elliptic problem and the performance of the method is illustrated by numerical experiments.Comment: 15 pages, 2 figures, 9 tables, (updated numerical experiments