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个核心组件，通常运行于独立的执行环境，导致工程与计算层面的冗余。此外，数据处理与机器学习的数学基础存在差异，阻碍了通过融合这两个组件实现跨领域优化，从而忽视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合并，显著加速预测流程（最高达317倍）。我们通过复杂度分析，针对不同数据与模型维度，定量揭示了算子融合的优势。进一步地，我们利用广泛使用的Star Schema Benchmark对矩阵乘法查询处理进行了全面评估。综合实验结果表明，该方法在提升现代硬件上数据处理与机器学习工作负载效率方面具有显著效果与巨大潜力。