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https://issues.apache.org/jira/browse/MAHOUT-376?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12965968#action_12965968
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Dmitriy Lyubimov commented on MAHOUT-376:
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Also, if we consider wide matrices instead of tall matrices, then maximum
number of mappers might be reduced which would affect parallelism on big
clusters.
Another consideration for extremely wide matrices i guess is that the A block
in this case will certainly overshoot several standard DFS blocks so it may
only be efficient if we force data collocation for a big number of blocks (or
just increase number of blocks). I am not sure if hadoop quite there yet to
tweak that on individual file basis.
-d
> Implement Map-reduce version of stochastic SVD
> ----------------------------------------------
>
> Key: MAHOUT-376
> URL: https://issues.apache.org/jira/browse/MAHOUT-376
> Project: Mahout
> Issue Type: Improvement
> Components: Math
> Reporter: Ted Dunning
> Assignee: Ted Dunning
> Fix For: 0.5
>
> Attachments: MAHOUT-376.patch, Modified stochastic svd algorithm for
> mapreduce.pdf, QR decomposition for Map.pdf, QR decomposition for Map.pdf, QR
> decomposition for Map.pdf, sd-bib.bib, sd.pdf, sd.pdf, sd.pdf, sd.pdf,
> sd.tex, sd.tex, sd.tex, sd.tex, SSVD working notes.pdf, SSVD working
> notes.pdf, SSVD working notes.pdf, ssvd-CDH3-or-0.21.patch.gz,
> ssvd-m1.patch.gz, ssvd-m2.patch.gz, ssvd-m3.patch.gz, Stochastic SVD using
> eigensolver trick.pdf
>
>
> See attached pdf for outline of proposed method.
> All comments are welcome.
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