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.
@article{arxiv.2209.04579,
title = {Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem},
author = {Yuki Asada and Victor Fu and Apurva Gandhi and Advitya Gemawat and Lihao Zhang and Dong He and Vivek Gupta and Ehi Nosakhare and Dalitso Banda and Rathijit Sen and Matteo Interlandi},
journal= {arXiv preprint arXiv:2209.04579},
year = {2022}
}