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https://issues.apache.org/jira/browse/SPARK-12620?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15081332#comment-15081332
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Kazuaki Ishizaki commented on SPARK-12620:
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[~srowen] would it be better to continue discussion on SPARK-3785 while we keep
the JIRA entry as RESOLVED or CLOSED? As [~rxin] said, SPARK-3785 may be used
as a venue for discussion.
> 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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