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https://issues.apache.org/jira/browse/MAHOUT-227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12793487#action_12793487
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Ted Dunning commented on MAHOUT-227:
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{quote}
I understand this concern. Actually, if we set the parameter k to 1,000,000
or higher, do you think it is reasonable to take advantage of Map-reduce
framework? I mean, from system implementation's view.
{quote}
If you increase the value of k to very large values, you will be able to get a
bit more computation, but if you follow my small cluster example I think that
increasing k from 1000 to 1,000,000 will likely increase efficiency from 0.1%
to less than 50% and will drive the algorithm well beyond the region were kT is
constant. You will still have quite a lot of I/O per cycle which may prevent
you from achieving even 10% efficiency.
For larger clusters, the problem will be much worse.
Go ahead and try it, though. Your real results count for more than my
estimates.
And as I said before, getting a good sequential implementation is of real value
as well.
> Parallel SVM
> ------------
>
> Key: MAHOUT-227
> URL: https://issues.apache.org/jira/browse/MAHOUT-227
> Project: Mahout
> Issue Type: Task
> Components: Classification
> Reporter: zhao zhendong
> Attachments: ParallelPegasos.doc, ParallelPegasos.pdf
>
>
> I wrote a proposal of parallel algorithm for SVM training. Any comment is
> welcome.
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