1. Interactive Terascale Particle Visualization Ellsworth, Green, Moran (NASA Ames Research Center)

2. Motivation Produce an interactive visualization of a 2TB CFD data set using particle tracing. –Precomputation of a large number of particles on a PC cluster –Out-of-core interactive visualization

3. Overview Computing streaklines and storing them on disk for a visualization application to retrieve data as needed. Computed streakline data contains position and several scalar values like particle age, pressure, etc.

4. Algorithm Overview Computes particle traces and scalar values One file per timestep, streaklines are stored contiguously (single disk read). Variable-length traces are stored according to Morton (space filling curve) order for locality. Coordinates are downsampled to 16bit, so are scalar values at each position. Scalar values are stored in separate files.

5. Curvilinear Grid Seedpoints on a regular grid might fall outside the curvilinear grid (86 % outside in this case. Thus, check seedpoint in every timestep and mark active seedpoints (active in all timesteps)

6. Particle Computation done on a 49-node Beowolf cluster. Streakline computation is independent, but input data is too big (2 TB). Each node gets chunks of seedpoints to compute. What can be done about reducing the memory footprint?

7. Exploiting Mesh Regularities Some zones of the mesh do not change over time, others are rotated copies of other zones, etc. Automatically find regularities and replace mesh with a new version. Replacement mesh cuts down the amount of data by a factor of over 5000.

8. Demand-Paging Data As only parts of the domain are needed, it is divided up into a number of fixed-size blocks. Then use demand-paging algorithm (with LRU replacement) to keep memory footprint reasonable. Use several threads per node to keep CPU utilization high. Prefetch data for newly loaded timestep.

9. Computation Performance Fairly slow: 5 days for the 2 TB dataset, producing 1.7 GB of uncompressed particle data (293 billion particles) Threading library seems to have a huge impact on performance…

10. Particle Trace Compression Use previous values in sequence to predict future values (0 th, 1 st, 2 nd order prediction) and compress using zlib.

11. Viewer Program Client with file server architecture Viewer allows viewpoint manipulation and seedpoint selection (using an axis-aligned selection box) Very similar to our “dmqr” in terms of server/client communication for queries.

12. Results Equipment used includes: –49-node Beowolf cluster, 1.7GHz Athlon MP, 1GB memory, Fast Ethernet. –Master node: 1.7GHz Athlon MP, 2GB memory, Gigabit Ethernet. –File servers: 3GHz Dual Xeon, 4GB memory, Gigabit Ethernet, 21250GB storage (RAID 5 => 4.5 TB total storage available) –Viewer ran on: 3GHz Dual Xeon, 4GB memory, Gigabit Ethernet, NVIDIA Quattro FX

13. Results