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https://issues.apache.org/jira/browse/SPARK-3785?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15080653#comment-15080653
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Kazuaki Ishizaki edited comment on SPARK-3785 at 1/4/16 3:44 AM:
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Let us reopen this thread :)
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.
was (Author: kiszk):
Let us reopen this thread :)
We are working for effectively and easily exploiting 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.
> Support off-loading computations to a GPU
> -----------------------------------------
>
> Key: SPARK-3785
> URL: https://issues.apache.org/jira/browse/SPARK-3785
> Project: Spark
> Issue Type: Brainstorming
> Components: MLlib
> Reporter: Thomas Darimont
> Priority: Minor
>
> Are there any plans to adding support for off-loading computations to the
> GPU, e.g. via an open-cl binding?
> http://www.jocl.org/
> https://code.google.com/p/javacl/
> http://lwjgl.org/wiki/index.php?title=OpenCL_in_LWJGL
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