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This study examines three inference strategies—serial, multi-core parallel (Joblib), and distributed memory (MPI)—to address scalability challenges in applying machine learning models to large tabular datasets. Using the HIGGS dataset as a representative benchmark of high-volume scientific data, a Random Forest classifier was trained and validated with performance metrics of 73.3% accuracy and 81.25% ROC-AUC. The results offer concrete guidance on selecting appropriate inference backends based on system architecture and dataset size, particularly for practitioners in high-performance computing and scientific machine learning domains.

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A Comprehensive Benchmark for Evaluation Inference Strategies on Large Scale Tabular Datasets

  • 会议论文

  • 发表日期：

    2026年

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  • Tushaar Yenduri,
  • S. S. R. Subramanya Hemant Konduri,
  • Kalyan Netti

摘要

This study examines three inference strategies—serial, multi-core parallel (Joblib), and distributed memory (MPI)—to address scalability challenges in applying machine learning models to large tabular datasets. Using the HIGGS dataset as a representative benchmark of high-volume scientific data, a Random Forest classifier was trained and validated with performance metrics of 73.3% accuracy and 81.25% ROC-AUC. The results offer concrete guidance on selecting appropriate inference backends based on system architecture and dataset size, particularly for practitioners in high-performance computing and scientific machine learning domains.