GML is a fast, distributed, in-memory sparse (and dense) matrix libraries.
It does not use RDDs for resilience. Instead we have examples that use
Resilient X10 (which provides recovery of distributed control structures
in case of node failure) and Hazelcast for stable storage.
We are looking to benchmark with RDDs to compare overhead, and also
looking to see how the same ideas could be realized on top of RDDs.
On 2/28/15 7:25 PM, Joseph Bradley wrote:
Hi Shahab,
There are actually a few distributed Matrix types which support sparse
representations: RowMatrix, IndexedRowMatrix, and CoordinateMatrix.
The documentation has a bit more info about the various uses:
http://spark.apache.org/docs/latest/mllib-data-types.html#distributed-matrix
The Spark 1.3 RC includes a new one: BlockMatrix.
But since these are distributed, they are represented using RDDs, so
they of course will not be as fast as computations on smaller, locally
stored matrices.
Joseph
On Fri, Feb 27, 2015 at 4:39 AM, Ritesh Kumar Singh
riteshoneinamill...@gmail.com mailto:riteshoneinamill...@gmail.com
wrote:
try using breeze (scala linear algebra library)
On Fri, Feb 27, 2015 at 5:56 PM, shahab shahab.mok...@gmail.com
mailto:shahab.mok...@gmail.com wrote:
Thanks a lot Vijay, let me see how it performs.
Best
Shahab
On Friday, February 27, 2015, Vijay Saraswat
vi...@saraswat.org mailto:vi...@saraswat.org wrote:
Available in GML --
http://x10-lang.org/x10-community/applications/global-matrix-library.html
We are exploring how to make it available within Spark.
Any ideas would be much appreciated.
On 2/27/15 7:01 AM, shahab wrote:
Hi,
I just wonder if there is any Sparse Matrix
implementation available in Spark, so it can be used
in spark application?
best,
/Shahab
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