利用线性代数查询处理加速机器学习查询
Accelerating Machine Learning Queries with Linear Algebra Query Processing
AI总结：
针对数据处理与 ML 预测分离导致的冗余和优化受限问题，提出基于 GPU 线性代数求值的算子融合方法，最高实现 317 倍加速。
摘要：
大规模机器学习（ML）模型的快速增长，促使众多商业公司使用 ML 模型生成预测结果，以辅助业务决策。作为传统预测流水线中的两个主要组成部分，数据处理与模型预测通常运行在相互独立的执行环境中，导致冗余的工程工作和计算。此外，数据处理与机器学习在数学基础上的差异，阻碍了通过结合这两个组成部分进行跨层优化，从而忽视了加速预测流水线的潜在机会。本文提出一种基于 GPU 加速的关系查询线性代数求值的算子融合方法。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合并，使预测流水线最高显著加速 317 倍。我们进行了复杂度分析，并在考虑多种数据维度和模型维度的情况下，对算子融合的优势给出定量洞见。此外，我们使用广泛采用的 Star Schema Benchmark，对矩阵乘法查询处理进行了广泛评估。通过全面评测，我们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方面的有效性和潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
