Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) targe...

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Bibliographic Details
Published inarXiv.org
Main Authors Asada, Yuki, Fu, Victor, Gandhi, Apurva, Gemawat, Advitya, Zhang, Lihao, He, Dong, Gupta, Vivek, Ehi Nosakhare, Banda, Dalitso, Sen, Rathijit, Interlandi, Matteo
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.09.2022
Subjects
Central processing units
Computer Science - Databases
Computer Science - Learning
CPUs
Graphics processing units
Ingestion
Machine learning
Mathematical analysis
Microprocessors
Operators
Queries
Tensors
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Summary:We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to support the TPC-H benchmark, and it provides performance that is comparable to, and often better than, that of specialized CPU and GPU query processors.
ISSN:2331-8422
DOI:10.48550/arxiv.2209.04579