[2209.04579] Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
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Computer Science > Databases

arXiv:2209.04579 (cs)
[Submitted on 10 Sep 2022]

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

Authors:Yuki Asada, Victor Fu, Apurva Gandhi, Advitya Gemawat, Lihao Zhang, Dong He, Vivek Gupta, Ehi Nosakhare, Dalitso Banda, Rathijit Sen, Matteo Interlandi
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Abstract: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.
Subjects: Databases (cs.DB); Machine Learning (cs.LG)
Cite as: arXiv:2209.04579 [cs.DB]
  (or arXiv:2209.04579v1 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2209.04579
Journal reference: PVLDB, 15(12): 3598 - 3601, 2022
Related DOI: https://doi.org/10.14778/3554821.3554853

Submission history

From: Apurva Gandhi [view email]
[v1] Sat, 10 Sep 2022 03:10:57 UTC (2,473 KB)
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