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Posted to issues@lucene.apache.org by "Rinka Singh (Jira)" <ji...@apache.org> on 2019/10/07 06:22:00 UTC
[jira] [Commented] (LUCENE-7745) Explore GPU acceleration
[ https://issues.apache.org/jira/browse/LUCENE-7745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16945620#comment-16945620 ]
Rinka Singh commented on LUCENE-7745:
-------------------------------------
A very quick update on this. I have been progressing on the histogram and should take another month or so to put something out. Basically.
# It is a sorted histogram of words.
# Like I said before, I can scale to the size of the device memory (I think 1/6th the total free mem) for one card
# Again, (:) hopefully) it should be straightforward to save the host memory location + the word to the stored data.
Thanks,
> Explore GPU acceleration
> ------------------------
>
> Key: LUCENE-7745
> URL: https://issues.apache.org/jira/browse/LUCENE-7745
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Ishan Chattopadhyaya
> Assignee: Ishan Chattopadhyaya
> Priority: Major
> Labels: gsoc2017, mentor
> Attachments: TermDisjunctionQuery.java, gpu-benchmarks.png
>
>
> There are parts of Lucene that can potentially be speeded up if computations were to be offloaded from CPU to the GPU(s). With commodity GPUs having as high as 12GB of high bandwidth RAM, we might be able to leverage GPUs to speed parts of Lucene (indexing, search).
> First that comes to mind is spatial filtering, which is traditionally known to be a good candidate for GPU based speedup (esp. when complex polygons are involved). In the past, Mike McCandless has mentioned that "both initial indexing and merging are CPU/IO intensive, but they are very amenable to soaking up the hardware's concurrency."
> I'm opening this issue as an exploratory task, suitable for a GSoC project. I volunteer to mentor any GSoC student willing to work on this this summer.
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