[jira] [Assigned] (SPARK-31976) use MemoryUsage to control the size of block
[
https://issues.apache.org/jira/browse/SPARK-31976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
zhengruifeng reassigned SPARK-31976:
Assignee: zhengruifeng
> 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
>Assignee: 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;
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[jira] [Assigned] (SPARK-31976) use MemoryUsage to control the size of block
[
https://issues.apache.org/jira/browse/SPARK-31976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-31976:
Assignee: Apache Spark
> 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
>Assignee: Apache Spark
>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;
--
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[jira] [Assigned] (SPARK-31976) use MemoryUsage to control the size of block
[
https://issues.apache.org/jira/browse/SPARK-31976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-31976:
Assignee: (was: Apache Spark)
> 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;
--
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