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This article presents a parallel-distributed implementation of the Word2Vec algorithm for training word embeddings, a relevant problem in natural language processing. A client-server implementation is developed, adapted to execute on non-dedicated commodity clusters. The server implements load balancing to deal with different processing times of processes and nodes. The experimental evaluation of the developed implementation was performed on a commodity cluster of workstations, analyzing the performance for different problem instances and varying the number of computing resources. Accurate speedup values and a proper scalability behavior are reported.

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High Performance Computing for Auto Supervised Machine Learning Training: Parallel-Distributed Implementation of the Word2Vec Algorithm for Training Word Embeddings

  • 会议论文

  • 发表日期：

    2025年

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  • Rafael Castelli Ottati,
  • Ignacio Viscardi,
  • Sergio Nesmachnow

摘要

This article presents a parallel-distributed implementation of the Word2Vec algorithm for training word embeddings, a relevant problem in natural language processing. A client-server implementation is developed, adapted to execute on non-dedicated commodity clusters. The server implements load balancing to deal with different processing times of processes and nodes. The experimental evaluation of the developed implementation was performed on a commodity cluster of workstations, analyzing the performance for different problem instances and varying the number of computing resources. Accurate speedup values and a proper scalability behavior are reported.