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The rapid growth of large-scale machine learning (ML) models has led numerouscommercial companies to utilize ML models for generating predictive results tohelp business decision-making. As two primary components in traditionalpredictive pipelines, data processing, and model predictions often operate inseparate execution environments, leading to redundant engineering andcomputations. Additionally, the diverging mathematical foundations of dataprocessing and machine learning hinder cross-optimizations by combining thesetwo components, thereby overlooking potential opportunities to expeditepredictive pipelines.  In this paper, we propose an operator fusing method based on GPU-acceleratedlinear algebraic evaluation of relational queries. Our method leverages linearalgebra computation properties to merge operators in machine learningpredictions and data processing, significantly accelerating predictivepipelines by up to 317x. We perform a complexity analysis to deliverquantitative insights into the advantages of operator fusion, consideringvarious data and model dimensions. Furthermore, we extensively evaluate matrixmultiplication query processing utilizing the widely-used Star SchemaBenchmark. Through comprehensive evaluations, we demonstrate the effectivenessand potential of our approach in improving the efficiency of data processingand machine learning workloads on modern hardware.

Accelerating Machine Learning Queries with Linear Algebra Query Processing