Thanks for the informative explanations. I was indeed talking about argsort (specifically I use it for nearest-neighbor calculations), which I could not find. I come from data-science/machine learning where Numpy is very common, so a lot of the implementation/suggestions we encounter are designed with that in mind. I have used ND4J in the past two years and had great experience with it, but sometimes we run into things that are more challenging. I wonder what would be a good way to address such cases - roll-out our own solution (probably not optimized, since I am not an expert in linear algebra)? call Numpy?
Such discussion could be useful for others who happen to run into a corner case.
PS In our internal benchmarks ND4J was faster than Numpy, and distribution of JARs was much easier than distributing Numpy packages when working with enterprise customers with mixed Windows/Linux environment.