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https://issues.apache.org/jira/browse/MAHOUT-376?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12966019#action_12966019
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Ted Dunning commented on MAHOUT-376:
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{quote}
We could scale n even further by splitting the vector into slices as said
before, but not before we solve the problem of code-data collocation in
'supersplits' for wide matrices. If we don't do that, it will cause a lot of IO
in mappers and kind of defeats the purpose of MR imo.
{quote}
I think that my suggested approach handles this already.
The block decomposition of Q via the blockwise QR decomposition implies a
breakdown of B into column-wise blocks which can each be handled separately.
The results are then combined using concatenation.
> 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-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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