Query Processing on Tensor Computation Runtimes

TL;DR

This paper introduces Tensor Query Processor (TQP), a system that transforms SQL queries into tensor programs to leverage tensor computation runtimes, significantly improving query performance on AI hardware.

Contribution

The paper presents TQP, a novel system that translates SQL into tensor programs, enabling efficient query processing on diverse AI hardware with minimal development effort.

Findings

TQP can run the full TPC-H benchmark.

TQP improves query execution time by up to 10×.

TQP accelerates ML-augmented SQL queries by up to 9×.

Abstract

The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space. We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms SQL queries into tensor programs and executes them on TCRs. TQP is able to run the full TPC-H benchmark by implementing novel algorithms for relational operators on the tensor routines. At the same time, TQP can support various hardware while only requiring a fraction of the usual development effort. Experiments show that TQP can improve query execution time by up to 10$\times$ over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9$\times$ speedup over CPU baselines.