HN Explorer

Inside FAISS: Billion-Scale Similarity Search (fremaconsulting.ch)

74 points by tohms ↗ HN
Author here. I wrote this as a visual companion to the 2017 FAISS paper (https://arxiv.org/abs/1702.08734), focused on the parts I found hardest to grok from text alone.

The article covers a subset of what FAISS does, with the paper as the source of truth. NSG, FastScan, IMI are not covered here, they'll get their own articles. I'd be especially interested in feedback on:

- the IVFPQ / IVFADC explanation, particularly the LUT reuse argument

- whether the GPU part captures enough of the actual complexity

Happy to answer questions.

5 comments

[ 6.1 ms ] story [ 20.8 ms ] thread
[deleted]
(comment deleted)
runamuck
Phenomenal interactive website. Thank you.
tohms
Thank you, the interactive parts were the actual reason I started the article, glad it helped.
rooftopzen
Great viz; the original paper wasn't peer reviewed; it might be great but I've learned its a waste of time to read those in current times (sorry and take this as one data point that suggests they should have done that).

That said, I've found FAISS great for certain use cases; wanted to say thx for surfacing - its not updated to work with most packages these days outside of faiss-cpu - curious why Meta dropped its maintenance; was it due to its slower speed or otherwise priorities?