Github user akaltsikis commented on the issue:
https://github.com/apache/spark/pull/16732
> After running some more experiments I was able to reduce the runtime by
another 1.5x factor. So currently the
"toCoordinateMatrix().toIndexedRowMatrix()" is better by a bi
Github user akaltsikis commented on the issue:
https://github.com/apache/spark/pull/16732
> Looks good @uzadude ; just saw this very old PR. However what about
@akaltsikis 's comment?
@srowen Tbh after 1 year and half i really can't recall many details.
I
Github user akaltsikis commented on the issue:
https://github.com/apache/spark/pull/16732
Hey guys i was implementing that as an external function in jar to work
with spark 2.1.1. Even if @uzadude improved performance of the 2.x
implementation by creating 2 seperate cases for sparse
Github user akaltsikis commented on the issue:
https://github.com/apache/spark/pull/16732
has the issue been resolved ?
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Github user akaltsikis commented on the issue:
https://github.com/apache/spark/pull/14068
@uzadude Looking forward to make it work for matrixes bigger than 1Mx1M and
sparsity of 0.001-0.002.
It means that i need to use distributed linear algebra methods. I am using
a
Github user akaltsikis commented on the issue:
https://github.com/apache/spark/pull/14068
@uzadude hey, i am looking to multiply VERY LARGE AND VERY SPARSE matrixes
using Spark. I would love some discussion over it. Can you give me a way to
contact you?
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