You are viewing a plain text version of this content. The canonical link for it is here.

Posted to issues@spark.apache.org by "zhengruifeng (Jira)" <ji...@apache.org> on 2020/09/17 07:22:00 UTC

[jira] [Created] (SPARK-32907) adaptively blockify instances

zhengruifeng created SPARK-32907:
------------------------------------

             Summary: adaptively blockify instances
                 Key: SPARK-32907
                 URL: https://issues.apache.org/jira/browse/SPARK-32907
             Project: Spark
          Issue Type: Sub-task
          Components: ML
    Affects Versions: 3.1.0
            Reporter: zhengruifeng


According to the performance test in https://issues.apache.org/jira/browse/SPARK-31783, the performance gain is mainly related to the nnz of block.

So it is reasonable to control the size of block.

 

I had some offline discuss with [~weichenxu123], then we think following changes are worthy：

1, infer an appropriate blockSize (MB) based on numFeatures and nnz by default;

2, impls should use a relative small memory footprint when processing one block, and should not use a large pre-allocated buffer, so we need to revert gmm;

3, use new blockify strategy in LinearSVC/LoR/LiR/AFT;

 

 



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org