3 ms·
Interesting project. Would be nice to see how it compares to the built-in operations provided in Faiss. https://github.com/facebookresearch/faiss/wiki/How-to-m
by dmezzetti 3y ago
Interesting project. Would be nice to see how it compares to the built-in operations provided in Faiss.
https://github.com/facebookresearch/faiss/wiki/How-to-make-Faiss-run-faster#avx-512-support https://github.com/facebookresearch/faiss/wiki/How-to-make-F...
- ashvardanian 3y agohttps://news.ycombinator.com/reply?id=39115268&goto=item%3Fid%3D39111114%2339115268 https://news.ycombinator.com/reply?id=39115268&goto=item%3Fi...
- dmezzetti 3y agoThinking more of a scientific comparison with metrics. Definitely looks interesting overall, appreciate you sharing this library as open source.
- ashvardanian 3y agoYou are welcome :) There are more materials in my blog, including 4 articles from October 2023: https://ashvardanian.com/archives/ https://ashvardanian.com/archives/ Benchmarks are quite easy to reproduce, especially in Python. The numbers for AWS Graviton 3, Apple M2 and Intel Sapphire Rapids CPUs can be found here: https://ashvardanian.com/posts/simsimd-faster-scipy/ https://ashvardanian.com/posts/simsimd-faster-scipy/
- dmezzetti 3y agoGreat, I'll take a look!