Volume rendering is a process with high computation demands. Time-Varying Volume Data (TVVD) increases the challenge not only for computing but also for loading and storing of the data in real time. Scientific fields are generating data at a much faster rate than processing power; thus parallel solutions are necessary for rendering such large datasets. Furthermore, tile displays provide more pixels to present the features of the phenomena under study with finer detail. However, current object-space partitioning schemes for parallel rendering can make load imbalance across processors very severe. This work provides an alternative based on wavelet theory. The size reduction of the data allows data migration and makes image-space partitioning schemes possible for commodity processors deployed in PC-clusters.
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