[jira] [Created] (SPARK-12620) Proposal of GPU exploitation for Spark

["Kazuaki Ishizaki (JIRA)" <ji...@apache.org>]

Kazuaki Ishizaki created SPARK-12620:
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             Summary: Proposal of GPU exploitation for Spark
                 Key: SPARK-12620
                 URL: https://issues.apache.org/jira/browse/SPARK-12620
             Project: Spark
          Issue Type: New Feature
          Components: Spark Core
            Reporter: Kazuaki Ishizaki


I created a new JIRA entry to move from SPARK-3875

Exploiting GPUs can allow us to shorten the execution time of a Spark job and to reduce the number of machines in a cluster. We are working to effectively and easily exploit GPUs on Spark at  [http://github.com/kiszk/spark-gpu]. Our project page is [http://kiszk.github.io/spark-gpu/]. A design document is [here|https://docs.google.com/document/d/1bo1hbQ7ikdUA9LYtYh6kU_TwjFK2ebkHsH66QlmbYP8/edit?usp=sharing]

Our ideas for exploiting GPUs are
# adding a new format for a partition in an RDD, which is a column-based structure in an array format, in addition to the current Iterator\[T\] format with Seq\[T\]
# generating parallelized GPU native code to access data in the new format from a Spark application program by using an optimizer and code generator (this is similar to [Project Tungsten|https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html]) and pre-compiled library

The motivation of idea 1 is to reduce the overhead of serializing/deserializing partition data for copy between CPU and GPU. The motivation of idea 2 is to avoid writing hardware-dependent code by application programmers. At first, we are working for idea A (For idea B, we need to write [CUDA|https://en.wikipedia.org/wiki/CUDA] code for now). 

This prototype achieved [3.15x performance improvement|https://github.com/kiszk/spark-gpu/wiki/Benchmark] of logistic regression ([SparkGPULR|https://github.com/kiszk/spark-gpu/blob/dev/examples/src/main/scala/org/apache/spark/examples/SparkGPULR.scala]) in examples on a 16-thread IvyBridge box with an NVIDIA K40 GPU card over that with no GPU card

You can download the pre-build binary for x86_64 and ppc64le from [here|https://github.com/kiszk/spark-gpu/wiki/Downloads]. You can run this on Amazon EC2 by [the procedure|https://github.com/kiszk/spark-gpu/wiki/How-to-run-%28local-or-AWS-EC2%29], too.



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