The rapid growth of large-scale machine learning (ML) models has led numerous commercial companies to utilize ML models for generating predictive results to help business decision-making. As two primary components in traditional predictive pipelines, data processing, and model predictions often operate in separate execution environments, leading to redundant engineering and computations. Additionally, the diverging mathematical foundations of data processing and machine learning hinder cross-optimizations by combining these two components, thereby overlooking potential opportunities to expedite predictive pipelines. In this paper, we propose an operator fusing method based on GPU-accelerated linear algebraic evaluation of relational queries. Our method leverages linear algebra computation properties to merge operators in machine learning predictions and data processing, significantly accelerating predictive pipelines by up to 317x. We perform a complexity analysis to deliver quantitative insights into the advantages of operator fusion, considering various data and model dimensions. Furthermore, we extensively evaluate matrix multiplication query processing utilizing the widely-used Star Schema Benchmark. Through comprehensive evaluations, we demonstrate the effectiveness and potential of our approach in improving the efficiency of data processing and machine learning workloads on modern hardware.

翻译：大规模机器学习模型的快速增长，促使众多商业公司利用这些模型生成预测结果以辅助业务决策。在传统预测流程中，数据处理与模型预测作为两大核心环节，通常运行于独立的执行环境，导致工程与计算的冗余。此外，数据处理与机器学习的数学基础存在分歧，阻碍了二者的跨领域优化组合，从而忽略了加速预测流程的潜在可能。本文提出一种基于GPU加速的线性代数关系查询评估的算子融合方法。该方法利用线性代数计算特性，将机器学习预测与数据处理中的算子进行融合，实现预测流程最高达317倍的加速。我们通过复杂度分析，从数据与模型的多维视角量化揭示了算子融合的优势。此外，基于广泛使用的星型模式基准测试，对矩阵乘法查询处理进行了全面评估。综合评估结果证明了该方法在现代硬件上提升数据处理与机器学习工作负载效率的有效性与潜力。