A parallel multiresolution volume rendering algorithm for large data visualization ?
We present a new parallel multiresolution volume rendering algorithm for visualizing large data sets. Using the wavelet transform, the raw data is first converted to a multiresolution wavelet tree. To eliminate the data dependency between processors at run-time, and achieve load-balanced rendering, we design a novel algorithm to partition the tree and distribute the data along a hierarchical space-filling curve with error-guided bucketization. Further optimization is achieved by storing reconstructed data at pre-selected tree nodes for each processor based on the available storage resources to reduce the overall wavelet reconstruction cost. At run time, the wavelet tree is first traversed according to the user-specified error tolerance. Data blocks of different resolutions that satisfy the error tolerance are then decompressed and rendered to compose the final image in parallel. Experimental results showed that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of data with arbitrary error tolerances.
multiresolution volume rendering wavelet transform space-filling curve large scale data load balancing parallel rendering