Spark Cyclone

Spark Cyclone is an Apache Spark plug-in that accelerates the performance of Spark by using the SX-Aurora TSUBASA "Vector Engine" (VE). The plugin enables Spark users to accelerate their existing jobs by generating optimized C++ code and executing it on the VE, with minimal or no effort.

Spark Cyclone currently offers three pathways to accelerate Spark on the VE:

• Spark SQL: The plugin leverages Spark SQL's extensibility to rewrite SQL queries on the fly and executes dynamically-generated C++ code on the VE with no user code changes necessary.
• RDD: For more direct control, the plugin's VERDD API API provides Scala macros that can be used to transpile normal Scala code into C++ and thus execute common RDD operations such as map() on the VE.
• MLlib: CycloneML is a fork of MLlib that uses Spark Cyclone to accelerate many of the ML algorithms using either the VE or CPU.

Spark Cyclone Homepage

Plugin Usage

Integrating the Spark Cyclone plugin into an existing Spark job is very straightforward. The following is the minimum set of flags that need to be added to an existing Spark job configuration:

$ $SPARK_HOME/bin/spark-submit \
    --name YourSparkJobName \
    --master yarn \
    --deploy-mode cluster \
    --num-executors=8 --executor-cores=1 --executor-memory=8G \                                 # Specify 1 executor per VE core
    --jars /path/to/spark-cyclone-sql-plugin.jar \                                              # Add the Spark Cyclone plugin JAR
    --conf spark.executor.extraClassPath=/path/to/spark-cyclone-sql-plugin.jar \                # Add Spark Cyclone libraries to the classpath
    --conf spark.plugins=io.sparkcyclone.plugin.AuroraSqlPlugin \                               # Specify the plugin's main class
    --conf spark.executor.resource.ve.amount=1 \                                                # Specify the number of VEs to use
    --conf spark.resources.discoveryPlugin=io.sparkcyclone.plugin.DiscoverVectorEnginesPlugin \ # Specify the class used to discover VE resources
    --conf spark.cyclone.kernel.directory=/path/to/kernel/directory \                           # Specify a directory where the plugin builds and caches C++ kernels
    YourSparkJob.py

Configuration

Please refer to the Plugin Configuration guide for an overview of the configuration options available to Spark Cyclone.

Plugin Development

System Setup

While parts of the codebase can be developed on a standard x86 machine running Linux or MacOS, building and testing the plugin requires a system that has VEs properly installed and set up - please refer to the VE Documentation for more information on this. The following guides contain all the necessary setup and installation steps:

• SX-Aurora TSUBASA Installation Guide
• SX-Aurora TSUBASA Setup Guide

In particular, the system should have the following software ready after setup:

• VEOS, the set of daemons and commands providing operating system functionality to VE programs
• AVEO, the offloading framework for running code on the VE
• NCC, NEC's C compiler for building code to VE target

Development Guide

The following pages cover all aspects of Spark Cyclone development:

• Usage:
  • Plugin Configuration
  • VERDD API
  • CycloneML with Spark Cyclone
• Development:
  • Building the Plugin
  • C++ Cyclone Library
  • Testing and Continuous Integration
  • Performance Profiling
  • Data Types
  • Plugin Lifecycle
  • JVM-VE Interface
  • SQL Planning
  • Expression Evaluation
• External Dependencies:
  • Frovedis Library
  • JavaCPP Layer Around AVEO

License

Spark Cyclone is licensed under the Apache License, Version 2.0.

For additional information, please see the LICENSE and NOTICE files.