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A new approach for large dataset isosurface extraction is presented. The approach's aim is efficient parallel isosurfacing when the dataset cannot be processed entirely in-core. The approach focuses on reducing the memory requirement and optimizing disk I/O while achieving a balanced load. In particular, an accurate model of isosurface extraction time is exploited to evenly distribute work across processors. The approach achieves processing efficiency by also avoiding unnecessary processing for portions of the dataset that are not intersected by the isosurface. To reduce the redundant computations and the storage requirements, a flexible, variably-granular data structure is utilized, thereby achieving excellent time and space performance.