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https://issues.apache.org/jira/browse/SPARK-3434?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14140160#comment-14140160
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Gaurav Mishra commented on SPARK-3434:
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A matrix being represented by multiple RDDs of sub-matrices may be helpful when
an operation on the matrix requires computation over only a small set of its
sub-matrices. However, operations like matrix multiplication require
computation over all elements in the matrix (i.e. all elements need to be
read). Therefore, at least in the case of matrix multiplication, keeping a
single RDD seems to be a better idea. Keeping multiple RDDs in that case will
only burden us further with the task of keeping track of all sub matrices.
> 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
>
> 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?
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