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.

Scientific Computing

Numerical simulation of real-world phenomena provides fertile ground for building interdisciplinary relationships. The SCI Institute has a long tradition of building these relationships in a win-win fashion – a win for the theoretical and algorithmic development of numerical modeling and simulation techniques and a win for the discipline-specific science of interest. High-order and adaptive methods, uncertainty quantification, complexity analysis, and parallelization are just some of the topics being investigated by SCI faculty. These areas of computing are being applied to a wide variety of engineering applications ranging from fluid mechanics and solid mechanics to bioelectricity.


martin

Martin Berzins

Parallel Computing
GPUs
mike

Mike Kirby

Finite Element Methods
Uncertainty Quantification
GPUs
pascucci

Valerio Pascucci

Scientific Data Management
chris

Chris Johnson

Problem Solving Environments
amir

Amir Arzani

Scientific machine learning
Data-driven fluid flow modeling

Funded Research Projects:


Publications in Scientific Computing:


Finite Element Refinements for Inverse Electrocardiography: Hybrid-Shaped Elements, High-Order Element Truncation and Variational Gradient Operator
D.F. Wang, R.M. Kirby, C.R. Johnson. In Proceeding of Computers in Cardiology 2009, Park City, September, 2009.



A Framework for Exploring Numerical Solutions of Advection Reaction Diffusion Equations using a GPU Based Approach
A.R. Sanderson, M.D. Meyer, R.M. Kirby, C.R. Johnson. In Journal of Computing and Visualization in Science, Vol. 12, pp. 155--170. 2009.
DOI: 10.1007/s00791-008-0086-0



Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples
R.S. MacLeod, J.G. Stinstra, S. Lew, R.T. Whitaker, D.J. Swenson, M.J. Cole, J. Krüger, D.H. Brooks, C.R. Johnson. In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.



Hexahedral Mesh Generation for Biomedical Models in SCIRun
J.F. Shepherd, C.R. Johnson. In Engineering with Computers, Vol. 25, No. 1, pp. 97--114. 2009.



Comparison of Consistent Integration Versus Adaptive Quadrature for Taming Aliasing Errors
SCI Technical Report, H. Mirzaee, C. Eskilsson, S.J. Sherwin, R.M. Kirby. No. UUSCI-2009-008, SCI Institute, University of Utah, 2009.



The SCIJump Framework for Parallel and Distributed Scientific Computing
S.G. Parker, K. Damevski, A. Khan, A. Swaminathan, C.R. Johnson. In Advanced Computational Infrastructures for Parallel and Distributed Adaptive Applications, Edited by Manish Parashar and Xiaolin Li and Sumir Chandra, Wiley-Blackwell, pp. 149--170. 2009.
DOI: 10.1002/9780470558027.ch9



A Meshing Pipeline for Biomedical Models
M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. In Engineering with Computers, Vol. 25, No. 1, SpringerLink, pp. 115-130. 2009.
DOI: 10.1007/s00366-008-0106-1



Formal Verification of Practical MPI Programs
A. Vo, S. Vakkalanka, M. Delisi, G. Gopalakrishnan, R.M. Kirby, R. Thakur. In Proceedings of 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), Raleigh, NC, pp. 261--270. February 14-18, 2009.



Particle-based Sampling and Meshing of Surfaces in Multimaterial Volumes
M.D. Meyer, R.T. Whitaker, R.M. Kirby, C. Ledergerber, H. Pfister. In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1539--1546. 2008.



Hexahedral Mesh Generation Constraints
J.F. Shepherd, C.R. Johnson. In Journal of Engineering with Computers, Vol. 24, No. 3, pp. 195--213. 2008.



Filtering in Legendre Spectral Methods
J.S. Hesthaven, R.M. Kirby. In Mathematics of Computation, Vol. 77, No. 263, pp. 1425--1452. 2008.



An Approach to Formalization and Analysis of Message Passing Libraries
R. Palmer, M. DeLisi, G. Gopalakrishnan, R.M. Kirby. In Proceedings of the 12th International Workshop on Formal Methods for Industrial Critical Systems (FMICS 2007), Berlin, Germany, Vol. 4916/2008, Note: Awarded Best Paper., pp. 164--181. 2008.



Application of Stochastic Finite Element Methods to Study the Sensitivity of ECG Forward Modeling to Organ Conductivity
S.E. Geneser, R.M. Kirby, R.S. MacLeod. In IEEE Transations on Biomedical Engineering, Vol. 55, No. 1, pp. 31--40. January, 2008.



A Comparison of Implicit Solvers for the Immersed Boundary Equations
E.P. Newren, A.L. Fogelson, R.D. Guy, R.M. Kirby. In Computer Methods in Applied Mechanics and Engineering, Vol. 197, No. 25--28, pp. 2290--2304. 2008.
DOI: 10.1016/j.cma.2007.11.030



Investigation of Smoothness-Increasing Accuracy-Conserving Filters for Improving Streamline Integration Through Discontinuous Fields
M. Steffen, S. Curtis, R.M. Kirby, J.K. Ryan. In IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 3, pp. 680--692. 2008.



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



Volumetric Parameterization and Trivariate B-spline Fitting using Harmonic Functions
T. Martin, E. Cohen, R.M. Kirby. In Proceedings of ACM Solid and Physical Modeling, Stony Brook, NY, Note: Awarded Best Paper, pp. 269-280. 2008.



On the Lamb Vector Divergence in Navier-Stokes Flows
C.W. Hamman, J.C. Klewicki, R.M. Kirby. In Journal of Fluid Mechanics, Vol. 610, pp. 261--284. 2008.



Unified Volume Format: A General System For Efficient Handling Of Large Volumetric Datasets
J. Krüger, K. Potter, R.S. MacLeod, C.R. Johnson. In Proceedings of IADIS Computer Graphics and Visualization 2008 (CGV 2008), pp. 19--26. 2008.
PubMed ID: 20953270

With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.



Implementing Efficient Dynamic Formal Verification Methods for MPI Programs
S. Vakkalanka, M. DeLisi, G. Gopalakrishnan, R.M. Kirby, R. Thakur, W. Gropp. In Recent Advances in Parallel Virtual Machine and Message Passing Interface Lecture Notes in Computer Science, Dublin, Ireland, Vol. 5205, pp. 248--256. September, 2008.

We examine the problem of formally verifying MPI programs for safety properties through an efficient dynamic (runtime) method in which the processes of a given MPI program are executed under the control of an interleaving scheduler. To ensure full coverage for given input test data, the algorithm must take into consideration MPI’s out-of-order completion semantics. The algorithm must also ensure that nondeterministic constructs (e.g., MPI wildcard receive matches) are executed in all possible ways. Our new algorithm rewrites wildcard receives to specific receives, one for each sender that can potentially match with the receive. It then recursively explores each case of the specific receives. The list of potential senders matching a receive is determined through a runtime algorithm that exploits MPI’s operation ordering semantics. Our verification tool ISP that incorporates this algorithm efficiently verifies several programs and finds bugs missed by existing informal verification tools.