A parallel pipelined renderer for time-varying volume data

This paper presents a strategy for efficiently rendering time-varying volume data on a distributed-memory parallel computer. Visualizing time-varying volume data take both large storage space and long computation time. Instead of employing all processors to render one volume at a time, a pipelined rendering approach partitions processors into groups so that multiple volumes can be rendered concurrently. The overall rendering time is greatly minimized because rendering is overlapped with I/O required to load the volume data sets. Moreover, parallelization overhead may be reduced as a result of partitioning the processors. We modify an existing parallel volume renderer to exploit various levels of rendering parallelism and to study how the partitioning of processors may lead to optimal rendering performance. We find that two factors affecting the overall execution time are resource utilization efficiency and pipeline startup latency. The optimal partitioning configuration is the one that balances these two factors. Tests on Intel Paragon computers show that in general optimal partitionings do exist for a given rendering task and result in 40-50% saving in overall rendering time