  There is a large body of recent work applying machine learning (ML)
techniques to query optimization and query performance prediction in relational
database management systems (RDBMSs). However, these works typically ignore the
effect of \textit{intra-parallelism} -- a key component used to boost the
performance of OLAP queries in practice -- on query performance prediction. In
this paper, we take a first step towards filling this gap by studying the
problem of \textit{tuning the degree of parallelism (DOP) via ML techniques} in
Microsoft SQL Server, a popular commercial RDBMS that allows an individual
query to execute using multiple cores.
  In our study, we cast the problem of DOP tuning as a {\em regression} task,
and examine how several popular ML models can help with query performance
prediction in a multi-core setting. We explore the design space and perform an
extensive experimental study comparing different models against a list of
performance metrics, testing how well they generalize in different settings:
$(i)$ to queries from the same template, $(ii)$ to queries from a new template,
$(iii)$ to instances of different scale, and $(iv)$ to different instances and
queries. Our experimental results show that a simple featurization of the input
query plan that ignores cost model estimations can accurately predict query
performance, capture the speedup trend with respect to the available
parallelism, as well as help with automatically choosing an optimal per-query
DOP.
