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https://issues.apache.org/jira/browse/SPARK-10387?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14726392#comment-14726392
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DB Tsai commented on SPARK-10387:
---------------------------------
Here is the current research result from us.
We implemented a prototype of code generation for trees, and here is the
implementation of code-gen.
https://github.com/dbtsai/tree/blob/master/macros/src/main/scala/Tree.scala
1) We found the performance of code-gen is 4x to 6x faster than naive binary
tree when the # of trees used in GBDT are small. But with around 500x trees,
the performance is slightly worse.
2) We're also benchmarking the flatten trees idea described here,
http://tullo.ch/articles/decision-tree-evaluation/
3) Finally, QuickScorer: A Fast Algorithm to Rank Documents with Additive
Ensembles of Regression Trees
http://delivery.acm.org/10.1145/2770000/2767733/p73-lucchese.pdf is being
implemented, we will benchmark it as well.
> Code generation for decision tree
> ---------------------------------
>
> Key: SPARK-10387
> URL: https://issues.apache.org/jira/browse/SPARK-10387
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: DB Tsai
>
> Provide code generation for decision tree and tree ensembles. Let's first
> discuss the design and then create new JIRAs for tasks.
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