[jira] [Commented] (SYSTEMML-1782) Extend parfor block indexing analysis

["Fei Hu (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SYSTEMML-1782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16149439#comment-16149439 ] 

Fei Hu commented on SYSTEMML-1782:
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[~mboehm7] and [~dusenberrymw] I'm working on this issue. the result of {{min}} function can be computed using {{org.apache.sysml.hops.rewrite.RewriteConstantFolding}}, but the problem is that the value of DataOps can not be derived, for example: {{beg = ((j-1) * batch_size) %% nrow(X_group_batch) + 1}} where {{j}} is the {{TRANSIENTREAD }} DataOps. Do you have ideas for this problem?
 

> Extend parfor block indexing analysis
> -------------------------------------
>
>                 Key: SYSTEMML-1782
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1782
>             Project: SystemML
>          Issue Type: Task
>            Reporter: Matthias Boehm
>            Assignee: Fei Hu
>
> This task aims to better support arbitrary block indexing in a parfor context, for expressions as this example from SYSTEMML-1774:
> {code}
>  parfor (j in 1:parallel_batches, log=DEBUG) {
>         # Get a mini-batch in this group
>         beg = ((j-1) * batch_size) %% nrow(X_group_batch) + 1
>         end = min(nrow(X_group_batch), beg + batch_size - 1)
>         X_batch = X_group_batch[beg:end,]
>         y_batch = y_group_batch[beg:end,]
> {code}
> Currently, we're unable to determine a constant size for X_batch and y_batch. However, since the parfor body is anyway recompiled on parfor entry, we can improve upon this as follows:
> * Leverage known constants and index ranges to perform a limit analysis for linear functions in indexing expressions. For example, the above expression for beg and end would simplify to {{(j-1) * 32 + 1}} and  {{beg + batch_size -1}}.
> * Better handling of linear functions in right indexing HOPs - this would allow us to determine batch_size as the number of rows in the output.



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