Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/15730
Looks pretty good overall. Left one major comment about documentation and
one about tests.
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Github user brkyvz commented on the issue:
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@WeichenXu123 Thanks! Will take a look once I get back from vacation (in a
week). Happy new year!
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Github user WeichenXu123 commented on the issue:
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@brkyvz I update code and attach a running result screenshot, waiting for
your review, thanks!
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Github user AmplabJenkins commented on the issue:
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Github user WeichenXu123 commented on the issue:
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@brkyvz All right, I'll update code ASAP. Thanks!
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Github user brkyvz commented on the issue:
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This way, all that needs to change in the implementation of multiply is:
```scala
val intermediatePartitioner = new Partitioner {
override def numPartitions: Int =
Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/15730
@WeichenXu123 How about if we only add:
```scala
def multiply(other: BlockMatrix, numMidDimSplits: Integer): BlockMatrix
```
as the public API. Users shouldn't have to define
Github user WeichenXu123 commented on the issue:
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@brkyvz
Good question about the shuffling data in the sparse case. Now I give some
simple analysis on it (maybe not very strict):
as discussed above, the shuffling data contains
Github user brkyvz commented on the issue:
https://github.com/apache/spark/pull/15730
Hi @WeichenXu123 Thank you for this PR. Sorry for taking so long to get
back to you. Your optimization would be very helpful. I have a couple thoughts
though. Your examples always take into account
Github user sethah commented on the issue:
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ping @brkyvz who wrote these libraries.
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Github user WeichenXu123 commented on the issue:
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@yanboliang @sethah I will be very pleasure to hear your opinions, thanks!
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