  The Apache Spark framework for distributed computation is popular in the data
analytics community due to its ease of use, but its MapReduce-style programming
model can incur significant overheads when performing computations that do not
map directly onto this model. One way to mitigate these costs is to off-load
computations onto MPI codes. In recent work, we introduced Alchemist, a system
for the analysis of large-scale data sets. Alchemist calls MPI-based libraries
from within Spark applications, and it has minimal coding, communication, and
memory overheads. In particular, Alchemist allows users to retain the
productivity benefits of working within the Spark software ecosystem without
sacrificing performance efficiency in linear algebra, machine learning, and
other related computations.
  In this paper, we discuss the motivation behind the development of Alchemist,
and we provide a detailed overview its design and usage. We also demonstrate
the efficiency of our approach on medium-to-large data sets, using some
standard linear algebra operations, namely matrix multiplication and the
truncated singular value decomposition of a dense matrix, and we compare the
performance of Spark with that of Spark+Alchemist. These computations are run
on the NERSC supercomputer Cori Phase 1, a Cray XC40.
