Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2008


C.E. Goodyer, J. Wood, M. Berzins. “Mathematical modeling of chemical diffusion through skin using Grid-based PSEs,” In Modeling, Simulation and Optimization of Complex Processes: Proceedings of the Third International Conference on High Performance Scientific Computing, Edited by H.G. Bock and E. Kostina and H.X. Phu and R. Rannacher, Springer, pp. 249--258. 2008.



J. Luitjens, Q. Meng, M. Berzins, T. Henderson. “Improving the Load Balance of Parallel Adaptive Mesh Refined Simulations,” SCI Technical Report, No. UUSCI-2008-007, University of Utah School of Computing, 2008.



J. Luitjens, B. Worthen, M. Berzins, T. Henderson. “Scalable Parallel AMR for the Uintah Multiphysics Code,” In Petascale Computing Algorithms and Applications, Ch. 4, CRC Press LLC., pp. 67--82. 2008.



Q. Meng, J. Luitjens, M. Berzins. “A Comparison of Load Balancing Algorithms for AMR in Uintah,” SCI Technical Report, No. UUSCI-2008-006, University of Utah, 2008.



M. Steffen, R.M. Kirby, M. Berzins. “Analysis and Reduction of Quadrature Errors in the Material Point Method (MPM),” In International Journal for Numerical Methods in Engineering, Vol. 76, No. 6, pp. 922--948. 2008.
DOI: 10.1002/nme.2360



M. Steffen, P.C. Wallstedt, J.E. Guilkey, R.M. Kirby, M. Berzins. “Examination and Analysis of Implementation Choices within the Material Point Method (MPM),” In Computer Modeling in Engineering & Sciences, Vol. 31, No. 2, pp. 107--127. 2008.



L.T. Tran, J. Kim, M. Berzins. “An Introduction to the Material Point Method using a Case Study from Gas Dynamics,” In Numerical Analysis and Applied Mathematics: International Conference on Numerical Analysis and Applied Mathematics 2008. AIP Conference Proceedings, Vol. 1048, Edited by T.E. Simos and G. Psihoyios and Ch. Tsitouras, pp. 26--29. 2008.
ISBN: 978-0-7354-0576-9


2007


M. Berzins. “Is there Still More to Science than Computation?,” In Computing in Science and Engineering, Vol. 9, No. 1, pp. 98--101. 2007.



M. Berzins. “Adaptive Polynomial Interpolation on Evenly Spaced Meshes.,” In SIAM Review, Vol. 49, No. 4, pp. 604-627. 2007.



C.E. Goodyer, M. Berzins. “Parallelisation and Scalability Issues of a Multilevel EHL Solver,” In Concurrency and Computation: Practice and Experience, Vol. 19, No. 4, pp. 369--396. 2007.



C.W. Hamman, R.M. Kirby, M. Berzins. “Parallelization and Scalability of a Spectral Element Channel Flow Solver for Incompressible Navier-Stokes Equations,” In Concurrency and Computation: Practice and Experience, Vol. 14, No. 10, pp. 1403--1422. 2007.



C.R. Hamman, R.M. Kirby, M. Berzins. “Parallel Direct Simulation of Incompressible Navier Stokes Equations,” In Concurrency and Computation, Vol. 19, No. 10, pp. 1403-1427. 2007.



J. Luitjens, M. Berzins, T.C. Henderson. “Parallel Space Filling Curve Generation Through Sorting,” In Journal of Concurrency and Computation, Vol. 19, No. 10, pp. 1387--1402. 2007.



J. Luitjens, B. Worthen, M. Berzins, T.C. Henderson. “Scalable Parallel AMR for the Uintah Multiphysics Code,” In Petascale Computing Algorithms and Applications, Edited by D. Bader, Chapman and Hall/CRC, 2007.



J. Luitjens, M. Berzins, T. Henderson. “Parallel space-filling curve generation through sorting,” In Concurrency and Computation: Practice and Experience, Vol. 19, No. 10, pp. 1387--1402. July, 2007.
DOI: 10.1002/cpe.1179

ABSTRACT

In this paper we consider the scalability of parallel space-filling curve generation as implemented through parallel sorting algorithms. Multiple sorting algorithms are studied and results show that space-filling curves can be generated quickly in parallel on thousands of processors. In addition, performance models are presented that are consistent with measured performance and offer insight into performance on still larger numbers of processors. At large numbers of processors, the scalability of adaptive mesh refined codes depends on the individual components of the adaptive solver. One such component is the dynamic load balancer. In adaptive mesh refined codes, the mesh is constantly changing resulting in load imbalance among the processors requiring a load-balancing phase. The load balancing may occur often, requiring the load balancer to perform quickly. One common method for dynamic load balancing is to use space-filling curves. Space-filling curves, in particular the Hilbert curve, generate good partitions quickly in serial. However, at tens and hundreds of thousands of processors serial generation of space-filling curves will hinder scalability. In order to avoid this issue we have developed a method that generates space-filling curves quickly in parallel by reducing the generation to integer sorting.



L.T. Tran, J. Kim, M. Berzins. “Solving Time-Dependent PDEs using the Material Point Method, A Case Study from Gas Dynamics,” SCI Institute Technical Report, No. UUSCI-2007-010, University of Utah, 2007.


2006


M. Berzins. “Is there Still More to Science than Simulation?,” No. UUSCI-2006-031, SCI Institute, University of Utah, November, 2006.



M. Berzins. “Adaptive Polynomial Interpolation on Evenly Spaced Meshes,” SCI Institute Technical Report, No. UUSCI-2006-033, University of Utah, 2006.



C.E. Goodyer, M. Berzins, P.K. Jimack, L.E. Scales. “A Grid-enabled Problem Solving Environment for Parallel Computational Engineering Design,” In Advances in Engineering Software, Vol. 37, No. 7, pp. 439--449. 2006.



C.E. Goodyer, M. Berzins. “Solving Computationally Intensive Engineering Problems on the Grid using Problem Solving Environments,” In Grid Computing: Software Environments and Tools, Edited by J.C. Cunha and O.F. Rana, Springer Verlag, pp. 284--301. 2006.