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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.

Related Keywords

,Xeon Cpus ,Unum Usearch ,Unum Uform ,Jensen Shannon ,Google ,Intel ,Architectures Software Developer ,Vector Search ,Machine Learning ,Information Retrieval ,Gen Intel Xeon ,Sapphire Rapids ,Between Python ,Quantization Aware Training ,Precision Support ,Basic Linear Algebra Subroutines ,Cosine Similarity ,Google Benchmark ,Ash Vardanian ,Software Developer ,Tech ,Less Slow ,

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