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Posted to issues@systemml.apache.org by "Matthias Boehm (JIRA)" <ji...@apache.org> on 2017/12/07 04:41:00 UTC
[jira] [Updated] (SYSTEMML-2039) Memory-inefficiency due to
breadth-first instruction generation
[ https://issues.apache.org/jira/browse/SYSTEMML-2039?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Matthias Boehm updated SYSTEMML-2039:
-------------------------------------
Description:
In large DAGs, the strict topological ordering (breadth-first) instruction generation causes unnecessary memory-inefficiency and unnecessary evictions. Depth-first instruction generation ensures that all consumers of a DAG input (bound to a logical variable name) are executed before the logical variable is overwritten by an output. For example, in below script
{code}
E = A * B + C * D
F = (A + B) + (C + D)
{code}
we compute A*B, C*D, A+B, C+D, and subsequently AB+CD, (A+B)+(C+D), which causes unnecessary memory pressure in the lower levels of the DAG. Furthermore, this also causes poor temporal locality.
Instead, we should use a two-level approach where all intermediates are computed in a depth-first manner, and all transient writes are scheduled after these operations.
was:
In large DAGs, the strict topological ordering (depth-first) instruction generation causes unnecessary memory-inefficiency and unnecessary evictions. Depth-first instruction generation ensures that all consumers of a DAG input (bound to a logical variable name) are executed before the logical variable is overwritten by an output. For example, in below script
{code}
E = A * B + C * D
F = (A + B) + (C + D)
{code}
we compute A*B, C*D, A+B, C+D, and subsequently AB+CD, (A+B)+(C+D), which causes unnecessary memory pressure in the lower levels of the DAG. Furthermore, this also causes poor temporal locality.
Instead, we should use a two-level approach where all intermediates are computed in a depth-first manner, and all transient writes are scheduled after these operations.
Summary: Memory-inefficiency due to breadth-first instruction generation (was: Memory-inefficiency due to breath-first instruction generation )
> Memory-inefficiency due to breadth-first instruction generation
> ----------------------------------------------------------------
>
> Key: SYSTEMML-2039
> URL: https://issues.apache.org/jira/browse/SYSTEMML-2039
> Project: SystemML
> Issue Type: Bug
> Reporter: Matthias Boehm
>
> In large DAGs, the strict topological ordering (breadth-first) instruction generation causes unnecessary memory-inefficiency and unnecessary evictions. Depth-first instruction generation ensures that all consumers of a DAG input (bound to a logical variable name) are executed before the logical variable is overwritten by an output. For example, in below script
> {code}
> E = A * B + C * D
> F = (A + B) + (C + D)
> {code}
> we compute A*B, C*D, A+B, C+D, and subsequently AB+CD, (A+B)+(C+D), which causes unnecessary memory pressure in the lower levels of the DAG. Furthermore, this also causes poor temporal locality.
> Instead, we should use a two-level approach where all intermediates are computed in a depth-first manner, and all transient writes are scheduled after these operations.
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