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How is nd4j compared to Breeze and jblas, which are used in Spark MLlib[1]?

[1] - http://spark.apache.org/docs/latest/mllib-data-types.html



See my post here: https://news.ycombinator.com/edit?id=11275071

We have this concept of a backend where we had net lib java and jblas, but both are horribly slow/limited and need some updating which is why we added our approach to this.

If a new matrix framework comes out, I will just write a backend for it and allow people to keep the same dsl.

Breeze also isn't usable from java: http://stackoverflow.com/questions/27246348/using-breeze-fro...

FWIW, we also support both row and column major (you can specify the data as well as ordering) very similar to numpy.

If you come use nd4j, it will mainly be for a dsl that encourages vectorization just like any other numerical language.


ND4J supports n-dimensional arrays, while Breeze does not. jblas is great but it's not fast enough. We've relied on netlibblas in the past, and we're moving to faster computation libs now. We'd love for Spark MLlib to plug into ND4J at some point.




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