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https://issues.apache.org/jira/browse/LUCENE-7745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16701879#comment-16701879
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Adrien Grand commented on LUCENE-7745:
--------------------------------------
(Unrelated to your comment Rinka, but seeing activity on this issue reminded me
that I wanted to share something) There are limited use-cases for GPU
accelelation in Lucene due to the fact that query processing is full of
branches, especially since we added support for impacts and WAND. That said
Mike initially mentioned that BooleanScorer might be one scorer that could
benefit from GPU acceleration as it scores large blocks of documents at once. I
just attached a specialization of a disjunction over term queries that should
make it easy to experiment with Cuda, see the TODO in the end on top of the
{{computeScores}} method.
> 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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