You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Kazuaki Ishizaki (JIRA)" <ji...@apache.org> on 2016/01/04 17:38:40 UTC
[jira] [Commented] (SPARK-12620) Proposal of GPU exploitation for
Spark
[ https://issues.apache.org/jira/browse/SPARK-12620?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15081332#comment-15081332 ]
Kazuaki Ishizaki commented on SPARK-12620:
------------------------------------------
[~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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org