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https://issues.apache.org/jira/browse/LUCENE-7745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15953521#comment-15953521
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Ishan Chattopadhyaya commented on LUCENE-7745:
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Hi Vikash,
I have reviewed the proposal. It is still extremely disorganized and it is not
clear what your goals are and how you have split them up into tasks. It
contains lots of copy paste of comments/statements from this JIRA or comments
from the proposal itself. The level of details still seems inadequate to me.
I had proposed a possible way to structure your proposal (by splitting the
three months into three different areas of focus, all of them I specified in
the comments), but I don't see that you've done so. I asked you to find out, at
least, what the default Similarity in Lucene is called (and to attempt to
simulate the scoring for that on the GPU). It seems you have not done so.
At this point, I don't think much can be done (just 2 hours to go for
submission). Wish you the best.
> 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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