Github user brkyvz commented on the pull request:
https://github.com/apache/spark/pull/3200#issuecomment-62982228
@mengxr
> If we have two block matrices, A and B, and A's column block partitioning
matches B's row block partitioning, can we take advantage of this fact in
computing A * B? I support having only one block matrix partitioner
implementation. Then we do the following:
>
> if (A.partitioner.colBlockPartitioner ==
B.partitioner.rowBlockPartitioner) {
> // zip ...
> } else {
> ...
> }
By `partitioner.rowBlockPartitioner` and `partitioner.colBlockPartitioner`,
are you talking about the number of blocks that form the rows and the number of
rows per block match?
One problem with zip was that I couldn't guarantee data locality. I tried
to force it, but the best way to force it turns out to be a join...
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