论文标题
分享张量茶：数据库如何利用机器学习生态系统
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
论文作者
论文摘要
我们演示了张量查询处理器（TQP）：一种自动将关系运算符编译到张量程序中的查询处理器。通过利用Tensor Runtime（例如Pytorch），TQP能够：（1）与ML工具集成（例如，用于数据摄取的PANDAS，可视化的张量）； （2）针对不同的硬件（例如CPU，GPU）和软件（例如浏览器）后端； （3）端到端加速了包含关系和ML操作员的查询。 TQP足以支持TPC-H基准测试，并且提供的性能与专门的CPU和GPU查询处理器相当，而且通常更好。
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.
