Handling Massive 3D Data: Two-Level Dataloaders and Memory Mapping
capetorch · x · 2026-08-12
Discusses the costly data loading challenges when working with massive 3D datasets. Since loading raw data just to discard 99% of points is highly inefficient, the author recommends tricks like memmap, pinmemory, and pseudo-random sampling with case re-use.
Additionally, the traditional concept of an epoch becomes ill-defined in this context. Because only a tiny fraction of points are seen at a time and there's an asymmetry between volume and surface points, developers typically need a two-level dataloader: first sampling a case, then sub-sampling its surface and volume.
Related event: Efficient 3D Data Sampling Strategies for CFD Surrogate Models(2 posts)→
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