Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
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

2014


S. Kumar, J. Edwards, P.-T. Bremer, A. Knoll, C. Christensen, V. Vishwanath, P. Carns, J.A. Schmidt, V. Pascucci. “Efficient I/O and storage of adaptive-resolution data,” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE Press, pp. 413--423. 2014.
DOI: 10.1109/SC.2014.39

ABSTRACT

We present an efficient, flexible, adaptive-resolution I/O framework that is suitable for both uniform and Adaptive Mesh Refinement (AMR) simulations. In an AMR setting, current solutions typically represent each resolution level as an independent grid which often results in inefficient storage and performance. Our technique coalesces domain data into a unified, multiresolution representation with fast, spatially aggregated I/O. Furthermore, our framework easily extends to importance-driven storage of uniform grids, for example, by storing regions of interest at full resolution and nonessential regions at lower resolution for visualization or analysis. Our framework, which is an extension of the PIDX framework, achieves state of the art disk usage and I/O performance regardless of resolution of the data, regions of interest, and the number of processes that generated the data. We demonstrate the scalability and efficiency of our framework using the Uintah and S3D large-scale combustion codes on the Mira and Edison supercomputers.


2013


S. Kumar, A. Saha, V. Vishwanath, P. Carns, J.A. Schmidt, G. Scorzelli, H. Kolla, R. Grout, R. Latham, R. Ross, M.E. Papka, J. Chen, V. Pascucci. “Characterization and modeling of PIDX parallel I/O for performance optimization,” In Proceedings of SC13: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 67. 2013.

ABSTRACT

Parallel I/O library performance can vary greatly in response to user-tunable parameter values such as aggregator count, file count, and aggregation strategy. Unfortunately, manual selection of these values is time consuming and dependent on characteristics of the target machine, the underlying file system, and the dataset itself. Some characteristics, such as the amount of memory per core, can also impose hard constraints on the range of viable parameter values. In this work we address these problems by using machine learning techniques to model the performance of the PIDX parallel I/O library and select appropriate tunable parameter values. We characterize both the network and I/O phases of PIDX on a Cray XE6 as well as an IBM Blue Gene/P system. We use the results of this study to develop a machine learning model for parameter space exploration and performance prediction.

Keywords: I/O, Network Characterization, Performance Modeling


2008


J.A. Schmidt. “Uintah Application Development,” SCI Technical Report, No. UUSCI-2008-005, University of Utah, 2008.


1997


C.R. Johnson, D.M. Beazley, Y. Livnat, S.G. Parker, J.A. Schmidt, H.W. Shen, D.M. Weinstein. “Applications of Large-Scale Computing and Scientific Visualization in Medicine,” SCI Institute Technical Report, No. UUSCI-1997-001, University of Utah, 1997.


1996


C.R. Johnson, D.M. Beazley, Y. Livnat, S.G. Parker, J.A. Schmidt, H.W. Shen, D.M. Weinstein. “Applications of Large-Scale Computing and Scientific Visualization in Medicine,” In International Journal on Supercomputer Applications and High Performance Computing, 1996.


1995


C.R. Johnson, R.S. MacLeod, J.A. Schmidt. “Software Tools for Modeling, Computation, and Visualization in Medicine,” In CompMed 94 Proceedings, World Scientific, 1995.



J.A. Schmidt, C.R. Johnson, J.C. Eason, R.S. MacLeod. “Applications of Automatic Mesh Generation and Adaptive Methods in Computational Medicine,” In Modeling, Mesh Generation, and AdaptiveMethodsforPartial Differential Equations, Edited by I. Babuska and J.E. Flaherty and W.D. Henshaw and J.E. Hopcroft and J.E. Oliger and T. Tezduyar, Springer-Verlag, pp. 367--390. 1995.



J.A. Schmidt, C.R. Johnson. “Defibsim: An Interactive Defibrillation Device Design Tool,” In IEEE Engineering in Medicine and Biology Society 17th Annual International Conference, IEEE Press, pp. 305--306. 1995.



J.A. Schmidt, C.R. Johnson, R.S. MacLeod. “An Interactive Computer Model for Defibrillation Device Design,” In International Congress on Electrocardiology, ICE, pp. 160--161. 1995.



D.M. Weinstein, C.R. Johnson, J.A. Schmidt. “Effects of Adaptive Refinement on the Inverse EEG Solution,” In SPIE, In Experimental and Numerical Methods for Solving Ill-Posed Inverse Problems, Vol. 2570, SPIE, pp. 2--11. 1995.