NVIDIA Delves into RAPIDS cuVS IVF-PQ for Accelerated Vector Search
Zach Anderson Jul 18, 2024 20:12
NVIDIA explores the RAPIDS cuVS IVF-PQ algorithm, enhancing vector search performance through compression and GPU acceleration.
In a detailed blog post, NVIDIA has provided insights into their RAPIDS cuVS IVF-PQ algorithm, which aims to accelerate vector search by leveraging GPU technology and advanced compression techniques. This is part one of a two-part series that continues from their previous exploration of the IVF-Flat algorithm.
IVF-PQ Algorithm Introduction
The blog post introduces IVF-PQ (Inverted File Index with Product Quantization), an algorithm designed to enhance search performance and reduce memory usage by storing data in a compressed form. This method, however, comes at the cost of some accuracy, a trade-off that will be further explored in the second part of the series.
IVF-PQ builds upon the concepts of IVF-Flat, which uses an inverted file index to limit the search complexity to a smaller subset of data through clustering. Product quantization (PQ) adds another layer of compression by encoding database vectors, making the process more efficient for large datasets.
Performance Benchmarks
NVIDIA shared benchmarks using the DEEP dataset, which contains a billion records and 96 dimensions, amounting to 360 GiB in size. A typical IVF-PQ configuration compresses this into an index of 54 GiB without significantly impacting search performance, or as small as 24 GiB with a slight slowdown. This compression allows the index to fit into GPU memory.
Comparisons with the popular CPU algorithm HNSW on a 100-million subset of the DEEP dataset show that cuVS IVF-PQ can significantly accelerate both index building and vector search.
Algorithm Overview
IVF-PQ follows a two-step process: a coarse search and a fine search. The coarse search is identical to IVF-Flat, while the fine search involves calculating distances between query points and vectors in probed clusters, but with the vectors stored in a compressed format.
This compression is achieved through PQ, which approximates a vector using two-level quantization. This allows IVF-PQ to fit more data into GPU memory, enhancing memory bandwidth utilization and speeding up the search process.
Optimizations and Performance
NVIDIA has implemented various optimizations in cuVS to ensure the IVF-PQ algorithm performs efficiently on GPUs. These include:
- Fusing operations to reduce output size and optimize memory bandwidth utilization.
- Storing the lookup table (LUT) in GPU shared memory when possible for faster access.
- Using a custom 8-bit floating point data type in the LUT for faster data conversion.
- Aligning data in 16-byte chunks to optimize data transfers.
- Implementing an “early stop” check to avoid unnecessary distance computations.
NVIDIA’s benchmarks on a 100-million scale dataset show that IVF-PQ outperforms IVF-Flat, particularly with larger batch sizes, achieving up to 3-4 times the number of queries per second.
Conclusion
IVF-PQ is a robust ANN search algorithm that leverages clustering and compression to enhance search performance and throughput. The first part of NVIDIA’s blog series provides a comprehensive overview of the algorithm’s workings and its advantages on GPU platforms. For more detailed performance tuning recommendations, NVIDIA encourages readers to explore the second part of their series.
For more information, visit the NVIDIA Technical Blog.
Image source: Shutterstock