Parallel Computing for 3D Data Visualisation and Transmission in Grid based Applications

Parallel Computing for 3D Data Visualisation and Transmission

in Grid based Applications

Daniele D’Agostino

CNR - IMATI - Sezione di Genova

DISI - Università di Genova

Objective

Optimization of the process of isosurface extraction for huge data sets, to subsequently allow an efficient transmission of the result for remote visualization using a compression and simplification steps and high performance techniques.

GRID

Multitier technology for distributed computing

• P2P networks are built on the analogy of providing services in a community of peers.

• GRID are built on the analogy of users tapping into ubiquitous computing and information resources, similar to plugging into an electrical grid.

The problem that underlies GRID is coordinated resource sharing and problem solving in dynamic, multi institutional virtual organizations.

DATA GRIDOften large data collections are the most important community resources

54 Gigabyte

100 Terabyte

3,5 Petabyte every year

But this combination of large dataset size, geographic distribution of users and resources, and also computationally intensive analysis results in complex performance demands that are not satisfied by any existing data management infrastructure.

Visualization process

One of the better instruments to represent resultsobtained from data analysis is their visualization.

For example meteorology.

Computational chain

Transmission

Data

Analysis

Data acquisition

ofphenomenon

Visualization

Volumetrical data

Result Visualization

VisualizationSimplification

Even the result, with present technologies, can be huge…

InternetCompression

…and sometimes there is need to remote visualization

3D Result model

Objective: the real time

Visualization

Internet

Simplification

Compression

Query and analysis

Data

With HPC techniques

3D ResultModel

Isosurface extractionGiven a scalar field f ( x, y, z, v ) an isosurface is a surface consisting of points in the space in which f’s value is constant.

Classical approach:

• Marching Cubes (Lorensen e Cline 1987)

Based on assumption: a contour can only pass through a cell in a finite number of ways

Idea: A case table is constructed and enumerates all possible topological states of a cell, given combinations of scalar values at the cell points

But there are disadvantages...

Problems

Such algorithm is no much efficient... • algorithm “brute force”

• computational intensive

• I/O bounded

…and, sometimes, it produces topological ambiguities, like wholes,but this problem can be solved with few additional checks.

OptimizationFor a given isovalue, only a smaller portion of cells are intersected by an isosurface So it is suitable to use search structure like:

• Adaptive Partition Trees (binary trees or octrees)

• Interval Tree

• K-D Trees (balanced binary trees)

If I subdivide the data I can perform this research with parallel processes

Simplification

Often data models are represented with more accuracy that it is necessary, especially forobjects sufficiently far from the virtual viewpoint (Hoppe 1996).

423.640 triangles 147.562 triangles

Reduction in number of geometric primitives means reduction of waiting time.

Parallel Simplification

Vertex decimation method (Schroeder 1992), that is simple and efficient, can be run in parallel by deleting sets of vertex that are really independent :

Super Independent Set (Franc e Skala 2000).

CompressionGeneral purpose compression techniques have worse performance in respect to specific techniques.

The Stanford Bunny is composed of 69.674 triangles: an ASCII files representing the uncompressed triangle table requires 133*T bit.

The connectivity may be compressed with GZIP down to 51*T, but with Edgebreaker (Rossignack 1999) less than 2*T!

Conclusions

The research activity is made in collaboration between the IMATI - CNR of Genova and the GVU Center of Georgia Institute of Technology.

The parallelization of the process and the interaction with the GRID are the aims of my PhD thesis in Computer Science.

References• W. E. Lorensen, H. E. Cline: Marching cubes: A high resolution 3D surface construction algorithm. In Computer Graphics, 21(4) pp.163-169, Luglio 1987.

• G. M. Nielson, B.Hamann: The asymptotic decider: Resolving the ambiguity in the marching cubes. In Proceedings Visualization '91 - sponsored by the IEEE Computer Society, pp. 83-91, 1991

• J. Rossignac: Edgebreaker: Connectivity compression for triangle meshes. In IEEE Transactions on Visualization and Computer Graphics, Vol. 5, No. 1, pp. 47-61, 1999.

• R. Oldfield: Summary of existing and developing data grids. Unpublished, Marzo 2002.

• W. J. Schroeder, J. A. Zarge, W. Lorensen: Decimation of triangle mesh. In ACM Computer Graphics, 26(2) pp. 65-70, Luglio 1992.

• M. Franc, V. Skala: Parallel Triangular Mesh Decimation. In SCCG 2000 Conference Proceedings, Maggio 2000.

• H. Hoppe: Progressive Meshes In SIGGRAPH Conference Proceedings, pp. 99-108, 1996.