Alchemist: An Apache Spark <=> MPI Interface

TL;DR

Alchemist enables seamless integration of MPI libraries into Spark applications, significantly improving performance for linear algebra tasks without sacrificing Spark's ease of use.

Contribution

This work introduces Alchemist, a system that allows Spark to efficiently call MPI-based libraries, reducing overheads and enhancing performance for large-scale data computations.

Findings

Alchemist reduces computation time for matrix operations.

Performance improvements are demonstrated on the NERSC Cori supercomputer.

Alchemist maintains Spark's productivity benefits while enhancing efficiency.

Abstract

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.