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 |
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. |
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 |
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. |
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 |
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 |
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. |