[jira] [Updated] (SPARK-31976) use MemoryUsage to control the size of block

["Hyukjin Kwon (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-31976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-31976:
---------------------------------
    Target Version/s: 3.2.0  (was: 3.1.0)

> use MemoryUsage to control the size of block
> --------------------------------------------
>
>                 Key: SPARK-31976
>                 URL: https://issues.apache.org/jira/browse/SPARK-31976
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML, PySpark
>    Affects Versions: 3.1.0
>            Reporter: zhengruifeng
>            Priority: Major
>
> 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 maybe reasonable to control the size of block by memory usage, instead of number of rows.
>  
> note1: param blockSize had already used in ALS and MLP to stack vectors (expected to be dense);
> note2: we may refer to the {{Strategy.maxMemoryInMB}} in tree models;
>  
> There may be two ways to impl:
> 1, compute the sparsity of input vectors ahead of train (this can be computed with other statistics computation, maybe no extra pass), and infer a reasonable number of vectors to stack;
> 2, stack the input vectors adaptively, by monitoring the memory usage in a block;



--
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