You are viewing a plain text version of this content. The canonical link for it is here.

Posted to issues@spark.apache.org by "Shivaram Venkataraman (JIRA)" <ji...@apache.org> on 2014/10/14 19:55:34 UTC

[jira] [Assigned] (SPARK-3434) Distributed block matrix

     [ https://issues.apache.org/jira/browse/SPARK-3434?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Shivaram Venkataraman reassigned SPARK-3434:
--------------------------------------------

    Assignee: Shivaram Venkataraman

> Distributed block matrix
> ------------------------
>
>                 Key: SPARK-3434
>                 URL: https://issues.apache.org/jira/browse/SPARK-3434
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Xiangrui Meng
>            Assignee: Shivaram Venkataraman
>
> This JIRA is for discussing distributed matrices stored in block sub-matrices. The main challenge is the partitioning scheme to allow adding linear algebra operations in the future, e.g.:
> 1. matrix multiplication
> 2. matrix factorization (QR, LU, ...)
> Let's discuss the partitioning and storage and how they fit into the above use cases.
> Questions:
> 1. Should it be backed by a single RDD that contains all of the sub-matrices or many RDDs with each contains only one sub-matrix?



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org