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From "Matthias Boehm (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SYSTEMML-2169) Spark nary cbind/rbind with broadcasts
Date Wed, 07 Mar 2018 05:44:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-2169?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Matthias Boehm updated SYSTEMML-2169:
-------------------------------------
    Labels: beginner  (was: )

> Spark nary cbind/rbind with broadcasts
> --------------------------------------
>
>                 Key: SYSTEMML-2169
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-2169
>             Project: SystemML
>          Issue Type: Task
>            Reporter: Matthias Boehm
>            Priority: Major
>              Labels: beginner
>
> The introduction of nary cbind and rbinds in SYSTEMML-1986 added support for operations
like {{E = cbind(A,B,C,D)}} which concatenates the matrices A, B, C, D column-wise without
the need for intermediates as requires by traditional binary cbind operations ({{cbind(cbind(cbind(A,B),C),D)}}).
SystemML also provides rewrites to automatically collapse chains of cbind or rbind operations
into their nary counter-parts. 
> However, for distributed spark operations, the binary cbind is still much better optimized
than the nary operation, which only provides a general case operation based on repartition
joins. 
> This tasks aims to address this by extending {{BuiltinNarySPInstruction}} at runtime
level. Given the unlimited number of inputs, this runtime approach seems more appropriate
than dedicated physical operations at compiler level. In detail, we need to evaluate if a
subset of input fits into the broadcast budget, and if so provide alternative code path for
nary cbind/rbind operations with broadcast joins.
> Note that distributed codegen operations have a similar characteristics of unlimited
inputs and already leverage broadcast variables when possible. Hence, we can probably use
a similar approach as done in {{SpoofSPInstruction}}.



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