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https://issues.apache.org/jira/browse/SINGA-19?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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wangwei resolved SINGA-19.
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Resolution: Fixed
> Slice large Param objects for load-balance
> ------------------------------------------
>
> Key: SINGA-19
> URL: https://issues.apache.org/jira/browse/SINGA-19
> Project: Singa
> Issue Type: New Feature
> Reporter: wangwei
> Assignee: wangwei
>
> Some Param objects in deep learning models are much larger than other Param
> objects. For example, a weight matrix is usually 100 times larger than a bias
> vector. The difference in Param size causes two problems,
> 1. if there are multiple servers in one server group, then the servers may be
> assigned different number of parameters to update.
> 2. if there are multiple server groups, e.g., in distributed Hogwild
> framework, then these server groups may be assigned different number of
> parameters to maintain.
> This ticket its to slice large Param objects to solve the load-balance
> problem. The slicing operations are done in the stub thread to make them
> transparent to both workers and servers.
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