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

2009


 J. Guilkey, T. Harman, J. Luitjens, J. Schmidt, J. Thornock, J.D. de St. Germain, S. Shankar, J. Peterson, C. Brownlee. “Uintah User Guide Version 1.1,” SCI Technical Report, No. UUSCI-2009-007, SCI Institute, University of Utah, 2009.

2004


 R.S. Macleod, D.M. Weinstein, J.D. de St. Germain, D.H. Brooks, C.R. Johnson, S.G. Parker. “SCIRun/BioPSE: Integrated Problem Solving Environment for Bioelectric Field Problems and Visualization,” In Proceedings of the Int. Symp. on Biomed. Imag., Arlington, Va, pp. 640--643. April, 2004.

2003


 J.D. de St. Germain, S.G. Parker. “Software Integration in an Academic Environment,” In Software Quality Forum (SQF), Arlington, Virginia, pp. (published on CD). March, 2003.


J.D. de St. Germain, A. Morris, S.G. Parker, A.D. Malony, S. Shende. “Performance Analysis Integration in the Uintah Software Development Cycle,” In International Journal of Parallel Programming, Vol. 31, No. 1, pp. 35--53. 2003.

ABSTRACT

The increasing complexity of high-performance computing environments and programming methodologies presents challenges for empirical performance evaluation. Evolving parallel and distributed systems require performance technology that can be flexibly configured to observe different events and associated performance data of interest. It must also be possible to integrate performance evaluation techniques with the programming paradigms and software engineering methods. This is particularly important for tracking performance on parallel software projects involving many code teams over many stages of development. This paper describes the integration of the TAU and XPARE tools in the Uintah Computational Framework (UCF). Discussed is the use of performance mapping techniques to associate low-level performance data to higher levels of abstraction in UCF and the use of performance regression testing to provide a historical portfolio of the evolution of application performance. A scalability study shows the benefits of integrating performance technology in building large-scale parallel applications.

Keywords: uintah

2002


 J.D. de St. Germain, A. Morris, S.G. Parker, A.D. Malony, S. Shende. “Integrating Performance Analysis in the Uintah Software Development Cycle,” In Proceedings of The 4th International Symposium on High Performance Computing, pp. 190--206. May 15-17, 2002.

2001


 J. McCorquodale, J.D. de St. Germain, S.G. Parker, C.R. Johnson. “The Uintah Parallelism Infrastructure: A Performance Evaluation on the SGI Origin 2000,” In Proceedings of The 5th International Conference on High-Performance Computing, Seattle, Mar, 2001.

2000


 J.D. de St. Germain, J. McCorquodale, S.G. Parker, C.R. Johnson. “Uintah: A Massively Parallel Problem Solving Environment,” In Ninth IEEE International Symposium on High Performance and Distributed Computing, IEEE, Piscataway, NJ, pp. 33--41. Nov, 2000.