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    • Sub-task
    • Status: Resolved
    • Major
    • Resolution: Resolved
    • 3.1.0
    • None
    • ML, PySpark
    • None

    Description

      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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            podongfeng zhengruifeng
            podongfeng zhengruifeng
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