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https://issues.apache.org/jira/browse/SPARK-22229?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16200664#comment-16200664
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Yuval Degani commented on SPARK-22229:
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Yes, transitioning from a ShuffleManager to a BlockTransferService will allow
significant reuse of the original code, as the code is well organized in
self-contained facilities.
All RDMA client/server facilities can be instantly reused.
Management code will have to be moved and adjusted, but on the other hand may
allow reuse of other Spark facilities such as BlockStoreShuffleReader and
ShuffleBlockFetcherIterator.
> SPIP: RDMA Accelerated Shuffle Engine
> -------------------------------------
>
> Key: SPARK-22229
> URL: https://issues.apache.org/jira/browse/SPARK-22229
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.3.0
> Reporter: Yuval Degani
> Attachments:
> SPARK-22229_SPIP_RDMA_Accelerated_Shuffle_Engine_Rev_1.0.pdf
>
>
> An RDMA-accelerated shuffle engine can provide enormous performance benefits
> to shuffle-intensive Spark jobs, as demonstrated in the “SparkRDMA” plugin
> open-source project ([https://github.com/Mellanox/SparkRDMA]).
> Using RDMA for shuffle improves CPU utilization significantly and reduces I/O
> processing overhead by bypassing the kernel and networking stack as well as
> avoiding memory copies entirely. Those valuable CPU cycles are then consumed
> directly by the actual Spark workloads, and help reducing the job runtime
> significantly.
> This performance gain is demonstrated with both industry standard HiBench
> TeraSort (shows 1.5x speedup in sorting) as well as shuffle intensive
> customer applications.
> SparkRDMA will be presented at Spark Summit 2017 in Dublin
> ([https://spark-summit.org/eu-2017/events/accelerating-shuffle-a-tailor-made-rdma-solution-for-apache-spark/]).
> Please see attached proposal document for more information.
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