This blogpost is a mirror of the original post on Modular.com. Modern CPUs have an incredible superpower: super-scalar operations, made available through single instruction, multiple data (SIMD) parallel processing. Instead of doing one operation at a time, a single core can do up to 4, 8, 16, or even 32 operations in parallel. In a way, a modern CPU is like a mini GPU, able to perform a lot of simultaneous calculations. Yet, because it’s so tricky to write parallel operations, almost all t...| Ash's Blog
A deep dive into the complexities of optimizing code for SIMD instruction sets across multiple platforms.| www.modular.com
TLDR: I’ve finally finished a project that involved gathering 7 billion small molecules, each represented in SMILES notation and having fewer than 50 “heavy” non-hydrogen atoms. Those molecules were “fingerprinted”, producing 28 billion structural embeddings, using MACCS, PubChem, ECFP4, and FCFP4 techniques. These embeddings were indexed using Unum’s open-source tool USearch, to accelerate molecule search. This extensive dataset is now made available globally for free, thanks to ...| ashvardanian.com
In this fourth article of the “Less Slow” series, I’m accelerating Unum’s open-source Vector Search primitives used by some great database and cloud providers to replace Meta’s FAISS and scale-up search in their products. This time, our focus is on the most frequent operation for these tasks - computing the the Cosine Similarity/Distance between two vectors. It’s so common, even doubling it’s performance can have a noticeable impact on applications economics.| ashvardanian.com
