In the first part of the series, we presented an overview of the IVF-PQ algorithm and explained how it builds on top of the IVF-Flat algorithm, using the Product Quantization (PQ) technique to compress the index and support larger datasets. In this part two of the IVF-PQ post, we cover the practical aspects of tuning IVF-PQ performance. It’s worth noting again that IVF-PQ uses a lossy…
]]>In this post, we continue the series on accelerating vector search using NVIDIA cuVS. Our previous post in the series introduced IVF-Flat, a fast algorithm for accelerating approximate nearest neighbors (ANN) search on GPUs. We discussed how using an inverted file index (IVF) provides an intuitive way to reduce the complexity of the nearest neighbor search by limiting it to only a small subset of…
]]>Performing an exhaustive exact k-nearest neighbor (kNN) search, also known as brute-force search, is expensive, and it doesn’t scale particularly well to larger datasets. During vector search, brute-force search requires the distance to be calculated between every query vector and database vector. For the frequently used Euclidean and cosine distances, the computation task becomes equivalent to a…
]]>