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https://issues.apache.org/jira/browse/SPARK-1405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14157605#comment-14157605
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Guoqiang Li edited comment on SPARK-1405 at 10/22/14 3:28 PM:
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Hi everyone
This is the latest performance test results 
All tests were run on precisely the same 4 node cluster.
36 executors(a total of36 cores, 216g memory).
Training iteration 150 times.
The spark configuration:
{noformat}
spark.akka.frameSize   20
spark.executor.instances 36
spark.rdd.compress true
spark.executor.memory   6g
spark.default.parallelism  72
spark.broadcast.blockSize  8192
spark.storage.memoryFraction 0.2
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.kryo.registrator 
org.apache.spark.mllib.feature.TopicModelingKryoRegistrator
{noformat}

Time-consuming in the following table:

||The number of topics||[PR 2388|https://github.com/apache/spark/pull/2388]
|2000 |42.26
|10000|49.47
|100000|58.20
|1000000|125.43



was (Author: gq):
This should be the checkpoint without work. 
You can merge the PR.
https://github.com/apache/spark/pull/2631 .

My data set is Chinese text, may not be suitable for sharing.

> parallel Latent Dirichlet Allocation (LDA) atop of spark in MLlib
> -----------------------------------------------------------------
>
>                 Key: SPARK-1405
>                 URL: https://issues.apache.org/jira/browse/SPARK-1405
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Xusen Yin
>            Assignee: Guoqiang Li
>              Labels: features
>         Attachments: performance_comparison.png
>
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> Latent Dirichlet Allocation (a.k.a. LDA) is a topic model which extracts 
> topics from text corpus. Different with current machine learning algorithms 
> in MLlib, instead of using optimization algorithms such as gradient desent, 
> LDA uses expectation algorithms such as Gibbs sampling. 
> In this PR, I prepare a LDA implementation based on Gibbs sampling, with a 
> wholeTextFiles API (solved yet), a word segmentation (import from Lucene), 
> and a Gibbs sampling core.



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