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https://issues.apache.org/jira/browse/LUCENE-7745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15931975#comment-15931975
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Ishan Chattopadhyaya commented on LUCENE-7745:
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Here are some ideas on things to start out with:
# Copy over and index lots of points and corresponding docids to the GPU as an
offline, one time operation. Then, given a query point, return top-n nearest
indexed points.
# Copy over and index lots of points and corresponding docids to the GPU as an
offline, one time operation. Then, given a polygon (complex shape), return all
points that lie inside the polygon.
In both the cases, compare performance against existing Lucene spatial search.
One would need to choose the most suitable algorithm for doing these as
efficiently as possible. Any GPGPU API can be used for now (OpenCL, CUDA) for
initial exploration.
[~dsmiley], [[email protected]], [~nknize], [~mikemccand], given your
depth and expertise in this area, do you have any suggestions? Any other area
of Lucene that comes to mind which should be easiest to start with, in terms of
exploring GPU based parallelization?
> Explore GPU acceleration
> ------------------------
>
> Key: LUCENE-7745
> URL: https://issues.apache.org/jira/browse/LUCENE-7745
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Ishan Chattopadhyaya
> Labels: gsoc2017, mentor
>
> 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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