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Posted to dev@lucene.apache.org by "Ishan Chattopadhyaya (JIRA)" <ji...@apache.org> on 2018/06/27 10:41:00 UTC
[jira] [Comment Edited] (LUCENE-7745) Explore GPU acceleration
[ https://issues.apache.org/jira/browse/LUCENE-7745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16524890#comment-16524890 ]
Ishan Chattopadhyaya edited comment on LUCENE-7745 at 6/27/18 10:40 AM:
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Here [0] are some very initial experiments that I ran, along with Kishore Angani, a colleague at Unbxd.
1. Generic problem: Given a result set (of document hits) and a scoring function, return a sorted list of documents along with the computed scores.
2. Specific problem: Given (up to 11M) points and associated docids, compute the distance from a given query point. Return the sorted list of documents based on these distances.
3. GPU implementation based on Thrust library (C++ based Apache 2.0 licensed library), called from JNI wrapper. Timings include copying data (scores and sorted docids) back from GPU to host system and access from Java (via DirectByteBuffer).
4. CPU implementation was based on SpatialExample [1], which is perhaps not the fastest (points fields are better, I think).
5. Hardware: CPU is i7 5820k 4.3GHz (OC), 32GB RAM @ 2133MHz. GPU is Nvidia GTX 1080, 11GB GDDR5 memory.
Results seem promising. The GPU is able to score 11M documents in ~50ms!. Here, blue is GPU and red is CPU (Lucene).
!Screenshot from 2018-06-27 15-33-37.png!width=800!
[0] - https://github.com/chatman/gpu-benchmarks
[1] - https://github.com/apache/lucene-solr/blob/master/lucene/spatial-extras/src/test/org/apache/lucene/spatial/SpatialExample.java
was (Author: ichattopadhyaya):
Here [0] are some very initial experiments that I ran, along with Kishore Angani, a colleague at Unbxd.
1. Generic problem: Given a result set (of document hits) and a scoring function, return a sorted list of documents along with the computed scores.
2. Specific problem: Given (up to 11M) points and associated docids, compute the distance from a given query point. Return the sorted list of documents based on these distances.
3. GPU implementation based on Thrust library (C++ based Apache 2.0 licensed library), called from JNI wrapper. Timings include copying data (scores and sorted docids) back from GPU to host system and access from Java (via DirectByteBuffer).
4. CPU implementation was based on SpatialExample [1], which is perhaps not the fastest (points fields are better, I think).
5. Hardware: CPU is i7 5820k 4.3GHz (OC), 32GB RAM @ 2133MHz. GPU is Nvidia GTX 1080, 11GB GDDR5 memory.
Results seem promising. The GPU is able to score 11M documents in ~50ms!. Here, blue is GPU and red is CPU (Lucene).
!Screenshot from 2018-06-27 15-33-37.png!
[0] - https://github.com/chatman/gpu-benchmarks
[1] - https://github.com/apache/lucene-solr/blob/master/lucene/spatial-extras/src/test/org/apache/lucene/spatial/SpatialExample.java
> Explore GPU acceleration
> ------------------------
>
> Key: LUCENE-7745
> URL: https://issues.apache.org/jira/browse/LUCENE-7745
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Ishan Chattopadhyaya
> Priority: Major
> Labels: gsoc2017, mentor
> Attachments: Screenshot from 2018-06-27 15-33-37.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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