Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem - Asada, Yuki; Fu, Victor; Gandhi, Apurva; Gemawat, Advitya; Zhang, Lihao; He, Dong; Gupta, Vivek; Nosakhare, Ehi; Banda, Dalitso; Sen, Rathijit; Interlandi, Matteo (VLDB, 2022)
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-toend accelerate queries containing both relational and ML operators. TQP is generic enough to supports the TPC-H benchmark, and it provides performance that are comparable to, and often better than, that of specialized CPU and GPU query processors.