  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.
