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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Spatial-Aware and Load Balancing Distributed Data Partitioning Strategies for Content-Based Multimedia Retrieval.

Gabriel Pereira¹, Willian Barreiros¹, Renato Ferreira¹

¹Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.

Research Square

|October 14, 2024

Summary

This summary is machine-generated.

New data partitioning algorithms (SABBS and SABBSR) significantly enhance performance for large-scale Approximate Nearest Neighbour Search (ANNS) in distributed systems. These methods minimize load imbalance and improve data locality, achieving up to 1.64x speedup on billion-scale datasets.

Keywords:

Approximate Nearest Neighbors Content-Based Multimedia Retrieval Data Partitioning Distributed Computing Load Imbalance Product Quantization Similarity Search

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Area of Science:

Computer Science
Data Science
Information Retrieval

Background:

Content-Based Multimedia Retrieval (CBMR) relies on efficient querying of large multimedia datasets.
Approximate Nearest Neighbour Search (ANNS) is crucial for handling high-dimensional data and large scales, trading accuracy for speed.
Existing ANNS algorithms are often designed for single-node execution, insufficient for massive datasets and high query loads.

Purpose of the Study:

To evaluate existing data partitioning strategies for distributed memory ANNS.
To propose novel partitioning algorithms that address load imbalance and enhance data locality.
To achieve high performance in large-scale ANNS on distributed systems.

Main Methods:

Evaluation of commonly used data partitioning strategies in ANNS.
Development and proposal of a novel class of partitioning algorithms (SABBS and SABBSR).
Experimental analysis of performance, load imbalance, and data locality on distributed memory systems.

Main Results:

Proposed algorithms (SABBS, SABBSR) improved search performance by up to 1.64x compared to prior methods.
Performance gains were maintained during weak scaling evaluations with up to 12 billion descriptors across 60 nodes.
Demonstrated significant reduction in load imbalance and improved data locality.

Conclusions:

The novel partitioning algorithms are efficient for billion-scale ANNS.
Effective data partitioning requires careful consideration of both data locality and load/data imbalance.
The proposed methods offer a scalable solution for high-performance distributed ANNS.