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https://issues.apache.org/jira/browse/MAHOUT-227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12793088#action_12793088
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Ted Dunning commented on MAHOUT-227:
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Here are a few formatting suggestions:
a) when cutting and pasting from somebody else's work, it is good to point this
out. You should directly credit figure 3 and the algorithm pseudo-code which
are cut-and-pasted directly from the original paper.
b) text in your diagram got resized and is now only partially readable. This
makes it a bit harder to follow exactly what you intend.
More importantly, the parameter k in the original paper is a batch size. You
propose to parallelize the computation of each batch, but otherwise leave the
main structure of the computation in place. If we assume a small cluster with,
say 100 cores (12 machines or so), then if you set k to 1000, each core will
get to do about a dozen vector operations. This is likely to be no more than a
microsecond of computation per core per iteration. My guess is that this will
result in very, very poor CPU utilization since you will require on map-reduce
invocation per iteration. Concretely put, you will have about a millisecond of
useful computation every 10 seconds or so.
You approach would probably work much better if applied to a single multi-core
machine where the very high rendezvous rate would be more achievable. I don't
expect that this proposed approach will work with map-reduce.
On the other hand, Pegasos is a pretty scalable algorithm even on a single
machine. If you were able to produce a high quality sequential implementation,
that would be a substantial contribution to Mahout.
> 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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