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From "Govinda Malavipathirana (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (SYSTEMML-2083) Language and runtime for parameter servers
Date Mon, 12 Feb 2018 21:50:00 GMT

    [ https://issues.apache.org/jira/browse/SYSTEMML-2083?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16361498#comment-16361498
] 

Govinda Malavipathirana edited comment on SYSTEMML-2083 at 2/12/18 9:49 PM:
----------------------------------------------------------------------------

Hi’
  I am Govinda Malavipathirana, 4th year undergraduate from University of Moratuwa, Faculty
of Information Technology in Sri Lanka. I read the initial documentation and found it exciting
and every interesting. I would like to contribute to this project. I have good knowledge in
deep learning, neural networks, machine learning, python, related technologies like numpy,
pandas and Git as well. I really enthuse about deep learning driven software development and
love to contribute to a DL open source project. Could you describe the project in much more
detail like current approach, expected extensions. Thank you.

Sincerely,
Govinda.
 



> 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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