I understand the concept core.matrix tries to achieve, and would be extremely happy if I thought it would be possible, since I would be able to use it and spend time on some other stuff instead of writing C, JNI, OpenCL and such low-level code. Thanks for the pointer to your neural networks experience and benchmark. I have taken a look at the thread you started about that issue, and it clearly shows what (in my opinion) is wrong is core.matrix: it is extremely easy to shoot yourself in the foot with it by (unintentionally) using the backing implementation in a wrong way. And, when you need to be specific an exploit the strength of the implementation, core.matrix gets in your way by making it more difficult in best cases and even impossible. Moreover, the optimizations that you manage to achieve with one implementation, often turn to be hogs with another, with "just one configuration change". For example, if you look at the benchmark on the neanderthal's web site, you'd see that for 512x512 matrices, matrix multiplication is 5x faster with clatrix (jblas) than vectorz. Yet, in your implementation, you managed to turn that 5x speedup into a 1000x slowdown (in the 500x500 case) without even one change in code. Quite impressive on the core.matrix side ;) I do not even claim that an unified api is not possible. I think that to some extent it is. I just doubt in core.matrix eligibility for THE api in numerical computing. For it makes easy things easy and hard things impossible.
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