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https://issues.apache.org/jira/browse/SYSTEMML-2083?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16363016#comment-16363016
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Govinda Malavipathirana edited comment on SYSTEMML-2083 at 2/14/18 6:01 AM:
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Hi

I went through the documentation, built up the SystemML locally and ran some 
samples as well, observing on paper yet to be done. What I have seen it not 
only specific on machine learning use cases but works on iterative algorithms 
(linear-algebraic) as well? and What could be the next step for me?


was (Author: mpgovinda):
Hi

I went through the documentation, built up the SystemML locally and ran some 
samples as well, observing on paper yet to done. What I have seen it not only 
specific on machine learning use cases but works on iterative algorithms 
(linear-algebraic) as well? and What could be the next step for me?

> Language and runtime for parameter servers
> ------------------------------------------
>
>                 Key: SYSTEMML-2083
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-2083
>             Project: SystemML
>          Issue Type: Epic
>            Reporter: Matthias Boehm
>            Priority: Major
>              Labels: gsoc2018
>
> SystemML already provides a rich set of execution strategies ranging from 
> local operations to large-scale computation on MapReduce or Spark. In this 
> context, we support both data-parallel (multi-threaded or distributed 
> operations) as well as task-parallel computation (multi-threaded or 
> distributed parfor loops). This epic aims to complement the existing 
> execution strategies by language and runtime primitives for parameter 
> servers, i.e., model-parallel execution. We use the terminology of 
> model-parallel execution with distributed data and distributed model to 
> differentiate them from the existing data-parallel operations. Target 
> applications are distributed deep learning and mini-batch algorithms in 
> general. These new abstractions will help making SystemML a unified framework 
> for small- and large-scale machine learning that supports all three major 
> execution strategies in a single framework.
>  
> A major challenge is the integration of stateful parameter servers and their 
> common push/pull primitives into an otherwise functional (and thus, 
> stateless) language. We will approach this challenge via a new builtin 
> function \{{paramserv}} which internally maintains state but at the same time 
> fits into the runtime framework of stateless operations.
> Furthermore, we are interested in providing (1) different runtime backends 
> (local and distributed), (2) different parameter server modes (synchronous, 
> asynchronous, hogwild!, stale-synchronous), (3) different update frequencies 
> (batch, multi-batch, epoch), as well as (4) different architectures for 
> distributed data (1 parameter server, k workers) and distributed model (k1 
> parameter servers, k2 workers). 



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