Sequential algorithms to split and merge ultra-high resolution 3D images

Splitting and merging data is a requirement for many parallel or distributed processing operations. Naive algorithms to split and merge 3D blocks from ultra-high resolution images perform very poorly, as a result of seek times. In contrast, naive algorithms to split and merge 3D slabs perform optimally as seek time is significantly minimized. We introduce and analyze sequential algorithms (Clustered reads, Multiple reads, Clustered writes, and Multiple writes) that leverage memory buffering to address this issue. Clustered reads and Clustered writes, access image chunks only once, but they have to seek in the reconstructed image. Multiple reads and Multiple writes minimize seeks in the reconstructed image, but they access image chunks multiple times. Evaluation on a 3850x3025x3500 brain image shows that our algorithms perform similarly to the optimal configuration provided that enough memory is available. Additionally, Multiple reads supports on-the-fly compression of the merged image transparently but Clustered reads does not, due to its use of negative seeking. We conclude that splitting and merging large 3D images can be done efficiently without relying on complex data formats.