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From "ASF subversion and git services (JIRA)" <>
Subject [jira] [Commented] (SINGA-8) Implement distributed Hogwild
Date Thu, 25 Jun 2015 13:45:05 GMT


ASF subversion and git services commented on SINGA-8:

Commit 884b9d70a631bee4961fb3907e47a747c5dd2b89 in incubator-singa's branch refs/heads/master
from wang wei
[;h=884b9d7 ]

SINGA-8 Implement distributed Hogwild

The original Param objects are sliced to make the size of parameters mastered by server groups
(roughly) equal.
Following Caffe's implementation, we let each server group master a subset of param slices.
Each server group updates all model parameters for the corresponding worker groups and synchronize
with other server groups on their mastered slices.
Tested on single node with multiple processes, each of which has one server group with one
server and one worker group with one worker.
The training loss decreases not as fast as shared-memory hogwild. TODO optimize and test on
multiple nodes.

> Implement distributed Hogwild
> -----------------------------
>                 Key: SINGA-8
>                 URL:
>             Project: Singa
>          Issue Type: New Feature
>            Reporter: wangwei
>            Assignee: wangwei
>              Labels: distributed, features, hogwild
> Generally, both the Downpour framework of Google Brain [1] and the Caffe's distributed
Hogwild implementation are extensions of the shared memory Hogwild training. In this ticket,
we refer to the second one.
> In specific, each server group masters a subset of parameters (i.e., Param objects) when
synchronizing with other server groups. It aggregates all updates for its subset and sends
back (e.g., broadcast) the updated parameters back to all other server groups. The synchronization
is conducted asynchronously. The frequency can be fixed in the first implementations. Finally,
it should be tuned automatically to fully utilize the network bandwidth.
> [1]J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato,
A. W. Senior, P. A. Tucker, K. Yang, and A. Y. Ng. Large scale
> distributed deep networks. In NIPS, pages 1232{1240, 2012.

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