Optimizing IVF-PQ Performance with RAPIDS cuVS: Key Tuning Techniques
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Tuning Parameters for Index Building
In the first part of this series, we explored the foundation of the IVF-PQ algorithm and its use of Product Quantization (PQ) to compress the index and support larger datasets. Now, let’s focus on tuning parameters for index building to optimize performance. One critical parameter is n_lists, which determines the number of partitions into which the dataset is clustered. Experimentation suggests that setting n_lists in the range of 10K to 50K can yield good performance across different recall levels.
Another important parameter is pq_dim, which controls compression. Adjusting this parameter by starting with one fourth of the number of features in the dataset and incrementally increasing it can improve performance. Additionally, the pq_bits parameter, ranging from 4 to 8, affects search speed and recall by determining the number of bits used in each PQ code.
Additional Parameters
Parameters like codebook_kind, kmeans_n_iters, and kmeans_trainset_fraction also play a crucial role in optimizing IVF-PQ performance. These parameters impact training time, GPU shared memory utilization, and recall levels, making them essential for achieving the best results efficiently.
Tuning Parameters for Search
For accurate and efficient search operations, parameters like n_probes, internal_distance_dtype, and lut_dtype are vital in controlling representation and storage during the search process. Adjusting these parameters can significantly impact performance, especially for datasets with high dimensionality.
Improving Recall with Refinement
If tuning parameters alone are not sufficient to achieve the desired recall, refinement offers a promising solution. This post-search operation recomputes exact distances for selected candidates and reranks them to improve recall levels. Refinement can be a powerful tool in enhancing search results, though it requires access to the source dataset.
Summary
Through this series on accelerating vector search with inverted-file indexes, we have delved into the IVF-PQ algorithm and provided practical tips on optimizing its performance using RAPIDS cuVS. By fine-tuning parameters for index building and search, data practitioners can achieve optimal results when handling billion-scale datasets efficiently. The RAPIDS cuVS library offers a diverse range of vector search algorithms to meet various use cases, from exact searches to high-throughput ANN methods.
For hands-on experience with tuning IVF-PQ parameters, check out the IVF-PQ notebook on GitHub. For detailed API information, refer to the cuVS documentation available on our website.
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