Large-Scale Data Parallelization of Product Quantization and Inverted Indexing Using Dask

• URL: http://arxiv.org/abs/2604.21645v1
• Date: Thu, 23 Apr 2026 12:59:30 GMT
• Title: Large-Scale Data Parallelization of Product Quantization and Inverted Indexing Using Dask
• Abstract summary: 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.<n>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.
• Score: 0.33554367023486936
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• Cardinality Estimation for High Dimensional Similarity Queries with Adaptive Bucket Probing [9.718824826201201]
  In this work, we address the problem of cardinality estimation for similarity search in high-dimensional spaces.<n>We leverage locality-sensitive hashing (LSH) to partition the vector space while preserving distance proximity.<n>We employ progressive sampling to reduce the number of distance computations and utilize asymmetric distance computation in product quantization to accelerate distance calculations in high-dimensional spaces.
  arXiv  Detail & Related papers  (2026-04-06T11:46:47Z)
• Value-Based Deep RL Scales Predictably [100.21834069400023]
  We show that value-based off-policy RL methods are predictable despite community lore regarding their pathological behavior.<n>We validate our approach using three algorithms: SAC, BRO, and PQL on DeepMind Control, OpenAI gym, and IsaacGym.
  arXiv  Detail & Related papers  (2025-02-06T18:59:47Z)
• LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search [4.194768796374315]
  We propose a new supervised score computation method based on the observation that inner product approximation is a multi-output regression problem. Our experiments show that the proposed reduced-rank regression (RRR) method is superior to PQ in both query latency and memory usage. We also introduce LoRANN, a clustering-based ANN library that leverages the proposed score computation method.
  arXiv  Detail & Related papers  (2024-10-24T17:13:39Z)
• Learning-Augmented K-Means Clustering Using Dimensional Reduction [1.7243216387069678]
  We propose a solution to reduce the dimensionality of the dataset using Principal Component Analysis (PCA) PCA is well-established in the literature and has become one of the most useful tools for data modeling, compression, and visualization.
  arXiv  Detail & Related papers  (2024-01-06T12:02:33Z)
• Data Aggregation for Hierarchical Clustering [0.3626013617212666]
  BETULA is a numerically stable version of the well known BIRCH data aggregation algorithm. It can be used to make HAC viable on systems with constrained resources with only small losses on clustering quality.
  arXiv  Detail & Related papers  (2023-09-05T19:39:43Z)
• Scalable Batch Acquisition for Deep Bayesian Active Learning [70.68403899432198]
  In deep active learning, it is important to choose multiple examples to markup at each step. Existing solutions to this problem, such as BatchBALD, have significant limitations in selecting a large number of examples. We present the Large BatchBALD algorithm, which aims to achieve comparable quality while being more computationally efficient.
  arXiv  Detail & Related papers  (2023-01-13T11:45:17Z)
• Communication-Efficient Adam-Type Algorithms for Distributed Data Mining [93.50424502011626]
  We propose a class of novel distributed Adam-type algorithms (emphi.e., SketchedAMSGrad) utilizing sketching. Our new algorithm achieves a fast convergence rate of $O(frac1sqrtnT + frac1(k/d)2 T)$ with the communication cost of $O(k log(d))$ at each iteration.
  arXiv  Detail & Related papers  (2022-10-14T01:42:05Z)
• Generalizable Mixed-Precision Quantization via Attribution Rank Preservation [90.26603048354575]
  We propose a generalizable mixed-precision quantization (GMPQ) method for efficient inference. Our method obtains competitive accuracy-complexity trade-off compared with the state-of-the-art mixed-precision networks.
  arXiv  Detail & Related papers  (2021-08-05T16:41:57Z)
• Learning Optical Flow from a Few Matches [67.83633948984954]
  We show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it. Experiments show that our method can reduce computational cost and memory use significantly, while maintaining high accuracy.
  arXiv  Detail & Related papers  (2021-04-05T21:44:00Z)
• IRLI: Iterative Re-partitioning for Learning to Index [104.72641345738425]
  Methods have to trade between obtaining high accuracy while maintaining load balance and scalability in distributed settings. We propose a novel approach called IRLI, which iteratively partitions the items by learning the relevant buckets directly from the query-item relevance data. We mathematically show that IRLI retrieves the correct item with high probability under very natural assumptions and provides superior load balancing.
  arXiv  Detail & Related papers  (2021-03-17T23:13:25Z)
• Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing [34.69732430310801]
  We propose Berrut Approximated Coded Computing (BACC) as an alternative approach to deal with stragglers effect. BACC is proven to be numerically stable with low computational complexity. In particular, BACC is used to train a deep neural network on a cluster of servers.
  arXiv  Detail & Related papers  (2020-09-17T14:23:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.