Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-146733572
@brkyvz Can you please add "[MLLIB]" to the PR title?
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Github user SparkQA commented on the pull request:
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Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-146723162
Minor comments only. Other than that, it looks fine to me.
@avulanov In your "Block-diagonal matrix multiplication" tests, do you know
if data were shuffled d
Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8757#discussion_r41586262
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -352,6 +353,30 @@ class BlockMatrix @Since("1.3.0") (
Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8757#discussion_r41586254
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -352,6 +353,30 @@ class BlockMatrix @Since("1.3.0") (
Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8757#discussion_r41586259
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -352,6 +353,30 @@ class BlockMatrix @Since("1.3.0") (
Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8757#discussion_r41586252
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
---
@@ -60,6 +60,7 @@ private[mllib] class GridPartitioner(
Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-146720466
Making a pass now
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Github user SparkQA commented on the pull request:
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Github user avulanov commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-140554085
Thank you for the update. Indeed, the tests take finite time to finish now.
Let's add @mengxr to the discussion.
Distributed matrix multiplication makes sense w
Github user brkyvz commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-140470928
More results. Tests performed on 4 executors each with 30 GB RAM, and 4
cores each:
- Size of A: 1e5 x 1e5
- Size of B: 1e5 x 1e5
- Block Size: 1024
Github user brkyvz commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-140235259
@avulanov Feel free to benchmark it in anyway. The same code is also
useful. I'm interested in how it would scale, and how it would perform if the
matrix is fully dense.
Github user avulanov commented on the pull request:
https://github.com/apache/spark/pull/8757#issuecomment-140234877
@brkyvz Thank you for notifying me. I would be interested to benchmark this
PR. Should I use the same code from the mailing list? It can be found here as
well https://g
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GitHub user brkyvz opened a pull request:
https://github.com/apache/spark/pull/8757
[SPARK-10599] Lower communication for block matrix multiplication
This PR aims to decrease communication costs in BlockMatrix multiplication
in two ways:
- Simulate the multiplication on the dri
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