Github user mikiobraun commented on the pull request:
https://github.com/apache/incubator-spark/pull/575#issuecomment-35073967
@fommil @MLnick Hello, I'm the author of jblas, and I'd like to clarify
what fommil said about netlib-java exceeding my original goals for JBLAS,
because I don't quite agree with that statement.
netlib-java provides BLAS/LAPACK for Java. If native libraries are found,
it uses those, otherwise, it will fall back to a (very slow) implementation of
Fortran which is basically a Java version of the netlib blas and lapack
implementations (no optimization whatsoever). netlib-java has full coverage of
the BLAS and LAPACK routines while jblas only includes what I needed.
netlib-java also has the fallback mode (which I honestly think you wouldn't
want to use because it's quite slow).
jblas however, is not just a wrapper around BLAS/LAPACK but a matrix
library on top of that, so it's netlib-java wrong to say that it exceeds the
goals of jblas, because netlib-java has different goals.
In my view, the biggest feature of jblas is the way I've packaged the ATLAS
libraries for Windows, Mac, and Linux, 32bit and 64bit into the jar so you can
really just put in a maven dependency and get native performance. I don't think
other libraries have gone through that pain, mainly to recompile ATLAS on all
those platforms, and so on. So I guess jblas is the simplest way to get native
performance on Java.
It has no support for sparse matrices, however, and I had relatively little
time to work on it lately, so I'm not mad if you choose to go another way ;)
