用Dask实现大规模产品量化与倒排索引并行，降低计算开销。
Large-Scale Data Parallelization of Product Quantization and Inverted Indexing Using Dask
- 基于Dask将数据分块，实现产品量化与倒排索引的分布式处理
- 在保持精度前提下，使大规模数据搜索计算量降至中等规模水平
- 适合处理海量高维数据的相似性搜索场景
大规模最近邻（NN）搜索虽广泛应用于相似性搜索领域，但受限于处理海量数据时的计算瓶颈。为降低计算成本，近似最近邻（ANN）搜索常被用于对精度要求不高的场景。产品量化（PQ）是一种高效的内存型ANN方法，适用于各类规模的数据集。然而，对大规模高维数据进行聚类需消耗大量内存和计算时间。本文提出一种基于Python的新型分治策略，结合产品量化、倒排索引与Dask框架，将大规模数据分割处理，最终合并结果，在不损失精度的前提下，将计算需求降低至中等规模数据的水平。
原文摘要 · Abstract (English)
Large-scale Nearest Neighbor (NN) search, though widely utilized in the similarity search field, remains challenged by the computational limitations inherent in processing large scale data. In an effort to decrease the computational expense needed, Approximate Nearest Neighbor (ANN) search is often used in applications that do not require the exact similarity search, but instead can rely on an approximation. Product Quantization (PQ) is a memory-efficient ANN effective for clustering all sizes of datasets. Clustering large-scale, high dimensional data requires a heavy computational expense, in both memory-cost and execution time. This work focuses on a unique way to divide and conquer the large scale data in Python using PQ, Inverted Indexing and Dask, combining the results without compromising the accuracy and reducing computational requirements to the level required when using medium-scale data.
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