I find it interesting in the Numpy comparison no mention of the BLAS Numpy is linked to is mentioned, but it is for Nd4j. Numpy is highly dependent on a good BLAS and the basic Netlib one isn't that great.
Yes we have double precision - we have a default data type with the data buffer.
If you're curious how we do storage: https://github.com/deeplearning4j/nd4j/blob/master/nd4j-buff...
We have allocation types and data types.
Data types are double/float/int (int is mainly for storage)
Allocation types are the storage medium which can be arrays,byte buffers or what have you.
If you have a problem with the docs - I highly suggest filing an issue on our site: https://github.com/deeplearning4j/nd4j/issues
We actually appreciate eedback like this thank you.
For net lib java, it links against any blas implementation you give it. It has this idea of a JNILoader which can dynamically link against the fallback blas (which you mentioned)
or typically openblas or mkl. The problem there can actually be licensing though. The spark project runs in to this: https://issues.apache.org/jira/browse/SPARK-4816
If we don't mention on the site, it's probably because we haven't thought about it or haven't gotten enough feedback on something.
Unfortunately, we're still in heavy development mode.
FWIW, we have one of the most active gitter channels out there. You can come find me anytime if you're interested in getting involved.
I'm aware the software engineering logistics that go into doing sparse right which is why I held off
We are mainly targeting deep learning with this but sparse is becoming important enough for us to add it.
As for disparate standards I managed to work past that for cublas/blas.
I'm not going to let it stop me from doing it right. If you want to help us fix it we are hiring ;).
There's the NIST sparse blas, and MKL has a similar but not exactly compatible version. These never really took off in adoption (MKL's widely used of course, but I'd wager these particular functions are not). What sparse lapack are you talking about?
> If you want to help us fix it we are hiring ;).
We were at the same dinner a couple weeks ago actually. I'm enjoying where I am using Julia and LLVM, not sure if you could pay me enough to make me want to work on the JVM.