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We address the problem of rendering large unstructured volumetric grids on machines with limited memory. This problem is particularly interesting because such datasets are likely to come from computations generated on supercomputers, that is, machines with superior resources than even the most powerful workstations. Here, we present a set of techniques which can be used to render arbitrarily large datasets on machines with very little memory. In particular, we present two techniques which vary in rendering speed, disk and memory usage, ease of implementation, and preprocessing costs. The first technique is completely disk-based, and requires a small amount (actually, constant) main memory. It works by performing one scan over the file containing the unstructured grid (assuming this file has been normalized as a pre-processing step), one externalmemory sort, and a final accumulation scan which computes the image. The second technique is based on our ZSWEEP algorithm, and it is more involved both in its preprocessing, implementation, and main memory requirements, but it is substantially faster, in some cases up to an order of magnitude faster. We have implemented both techniques, and we present results on rendering a few large datasets under different conditions (image resolutions, main memory configurations, etc), and discuss the tradeoffs of using the techniques presented in this paper.

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