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From "Chris Olivier (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MXNET-4) Refactor Random and ParallelRandom resources to use MKL for MKL builds
Date Mon, 05 Feb 2018 17:53:00 GMT

     [ https://issues.apache.org/jira/browse/MXNET-4?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Chris Olivier updated MXNET-4:
------------------------------
    Summary: Refactor Random and ParallelRandom resources to use MKL for MKL builds  (was:
Performance: Refactor Random and ParallelRandom resources to use MKL for MKL builds)

> Refactor Random and ParallelRandom resources to use MKL for MKL builds
> ----------------------------------------------------------------------
>
>                 Key: MXNET-4
>                 URL: https://issues.apache.org/jira/browse/MXNET-4
>             Project: Apache MXNet
>          Issue Type: Improvement
>            Reporter: Chris Olivier
>            Priority: Major
>              Labels: mkl, performance
>
> Refactor Random and ParallelRandom resources to use MKL for MKL builds
> Things such as RngUniform, etc.  Similarly to what is done for dropout operator.
> It may need to allocate some temporary memory and generate random numbers in batches,
then serving them out from that batch. 
> Also the Random classes could export a "fill buffer with randoms" function, which seems
to be a common use-case and fits the MKL API more closely.
> Care must be taken regarding MKL's fixed output types for some of the API functions.



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