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From "ASF subversion and git services (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SINGA-131) Implement and optimize hybrid training using both CPU and GPU
Date Wed, 06 Apr 2016 12:29:25 GMT

    [ https://issues.apache.org/jira/browse/SINGA-131?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15228181#comment-15228181
] 

ASF subversion and git services commented on SINGA-131:
-------------------------------------------------------

Commit 040cbb2e121164268284c4a8d005bd9aea83f40c in incubator-singa's branch refs/heads/master
from WANG Sheng
[ https://git-wip-us.apache.org/repos/asf?p=incubator-singa.git;h=040cbb2 ]

SINGA-131 Implement and optimize hybrid training using both CPU and GPU

update test files
checked with cpplint


> Implement and optimize hybrid training using both CPU and GPU
> -------------------------------------------------------------
>
>                 Key: SINGA-131
>                 URL: https://issues.apache.org/jira/browse/SINGA-131
>             Project: Singa
>          Issue Type: Improvement
>            Reporter: wangwei
>              Labels: CPU, GPU, hybrid
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> We discussed with researchers from Stanford on implementing hybrid training before
> http://mail-archives.apache.org/mod_mbox/singa-dev/201507.mbox/%3CCAJz0iLsd5iSCqqVU4QHLKzMO2o%2BFt-40kN8RgWkYhDn%3D6Qqqbw%40mail.gmail.com%3E.
> Now with the GPU training supported, we can move on to this feature.
> The distributed training framework is natural for hybrid training with CPU and GPU. The
first n workers would be assigned with GPU cards (n is the number of cards configured by users),
and the rest workers would run on CPU.
> Some code may need updates and optimization to consider the memory transferring between
GPU workers and CPU workers. Most of them is in worker.cc, param.cc and stub.cc.
> Automatically Tuning the workload among GPU and CPU could be designed and implemented
in this ticket or a new ticket.



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