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https://issues.apache.org/jira/browse/MAHOUT-165?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12778944#action_12778944
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Sean Owen commented on MAHOUT-165:
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I generally favor including stuff as an intact library rather than cracking it
open and modifying it, all else equal. Because this is a first step down the
road to forking, and I'd hate to fork Colt without good reason.
So I suppose the easiest thing is a .jar file with the unusable classes
stripped out. That should be it. We don't need to remove classes that merely
depend on hep.aida.*, unless a class we need depends directly or indirectly on
a class that references it. Ideally that's not true; we'll see in practice.
Even then, just a matter of taking those out too.
We don't necessarily need source and I'd favor not incorporating as source, and
not modifying the package names. Yes its dependencies could be changed and
updated but I'd say let's not bother until there is a case for doing so. What
would we do when Colt is updated, re-port the updates? We'd have a fork on our
hands and might as well just decide that's what we're doing.
> Using better primitives hash for sparse vector for performance gains
> --------------------------------------------------------------------
>
> Key: MAHOUT-165
> URL: https://issues.apache.org/jira/browse/MAHOUT-165
> Project: Mahout
> Issue Type: Improvement
> Components: Matrix
> Affects Versions: 0.2
> Reporter: Shashikant Kore
> Assignee: Grant Ingersoll
> Fix For: 0.3
>
> Attachments: colt.jar, mahout-165-trove.patch,
> MAHOUT-165-updated.patch, mahout-165.patch, MAHOUT-165.patch, mahout-165.patch
>
>
> In SparseVector, we need primitives hash map for index and values. The
> present implementation of this hash map is not as efficient as some of the
> other implementations in non-Apache projects.
> In an experiment, I found that, for get/set operations, the primitive hash of
> Colt performance an order of magnitude better than OrderedIntDoubleMapping.
> For iteration it is 2x slower, though.
> Using Colt in Sparsevector improved performance of canopy generation. For an
> experimental dataset, the current implementation takes 50 minutes. Using
> Colt, reduces this duration to 19-20 minutes. That's 60% reduction in the
> delay.
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