[GitHub] spark pull request #16279: [SPARK-18471][MLLIB][BACKPORT-2.0] In LBFGS, avoi...

[asfgit]

Github user asfgit closed the pull request at:

https://github.com/apache/spark/pull/16279


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[GitHub] spark pull request #16279: [SPARK-18471][MLLIB][BACKPORT-2.0] In LBFGS, avoi...

[AnthonyTruchet]

GitHub user AnthonyTruchet opened a pull request:

https://github.com/apache/spark/pull/16279

[SPARK-18471][MLLIB][BACKPORT-2.0] In LBFGS, avoid sending huge vectors of 
0 

# What changes were proposed in this pull request? #

CostFun used to send a dense vector of zeroes as a closure in a
treeAggregate call. To avoid that, we change the aggregation operations
to convert sparse vectors into dense vectors on the fly if needed and we 
pass a sparse 0 vector which is lightweight.

# How was this patch tested? #

Unit test for module mllib run locally for correctness.

As for performance we run an heavy optimization on our production data (50 
iterations on 128 MB weight vectors) and have seen significant decrease in 
terms both of runtime and container being killed by lack of off-heap memory.

Author: Anthony Truchet 
Author: sethah 

You can merge this pull request into a Git repository by running:

$ git pull https://github.com/criteo-forks/spark SPARK-18471-branch-2.0

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/16279.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #16279


commit 27f5796831ccb7931303a337d6125714aaa66b5a
Author: Anthony Truchet 
Date:   2016-12-13T21:30:57Z

[SPARK-18471][MLLIB][BACKPORT-2.0] In LBFGS, avoid sending huge vectors of 0

CostFun used to send a dense vector of zeroes as a closure in a
treeAggregate call. To avoid that, we replace treeAggregate by
mapPartition + treeReduce, creating a zero vector inside the mapPartition
block in-place.

Unit test for module mllib run locally for correctness.

As for performance we run an heavy optimization on our production data (50 
iterations on 128 MB weight vectors) and have seen significant decrease in 
terms both of runtime and container being killed by lack of off-heap memory.

Author: Anthony Truchet 
Author: sethah 




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