|Brian Bunch Christensen||Michael Bang Nielsen||Ken Museth|
|Alexandra Institute||Weta Digital||DreamWorks Animation|
|Aarhus University||Aarhus University||Aarhus University|
We propose a storage efﬁcient, fast and parallelizable out-of-core framework for streaming computations of high resolution level sets. The fundamental techniques are skewing and tiling transformations of streamed level set computations which allow for the combination of interface propagation, re-normalization and narrow-band rebuild into a single pass over the data stored on disk. When combined with a new data layout on disk, this improves the overall performance when compared to previous streaming level set frameworks that require multiple passes over the data for each time-step. As a result, streaming level set computations are now CPU bound and consequently the overall performance is unaffected by disk latency and bandwidth limitations. We demonstrate this with several benchmark tests that show sustained out-of-core throughputs close to that of in-core level set simulations.