extfuse VS mindcastle.io

FUSE does not work well with a large number of small files (due to high metadata ops such as inode/dentry lookups).

ExtFUSE (optimized FUSE with eBPF) [1] can offer you a high performance. It caches metadata in the kernel to avoid lookups in user space.

1. https://github.com/extfuse/extfuse

I don’t know who came first, but https://github.com/jacobgorm/mindcastle.io also uses the rsync/LBFS rolling hashes trick to split the tree data into chunks. I presented the idea at Usenix Vault 2019 https://m.youtube.com/watch?v=QgOkDiP0C4c&embeds_referring_e...

It is not how you would want do it for a typical ML workload, where the samples have to get randomly permuted each epoch.

Instead, tar up the files in some random order, and put the tar file on a web server or bucket, then stream then in during the first epoch, while keeping track of their byte offsets in the tar file, which you cache locally, assuming ample local Flash storage. Then permute the list of offsets and use those when reading samples for the next epoch.

If you only have local HDD then you will need a more advanced data structure like the one provided by https://github.com/jacobgorm/mindcastle.io , which will allow you to write out permuted samples at close to disk sequential write bandwidth. See my talk at USENIX Vault 2019 for a full explanation, linked from https://vertigo.ai/mindcastle/