ann-benchmark | artificial neural network library for Spark MLlib | Machine Learning library

I'm a bit skeptical of benchmarks such as the one you have linked, as in my experience I have found that the definition of the problem at hand far outweighs in importance the merits of any one algorithm across a set of other (possibly similar looking) problems.

More simply put, an algorithm being a high performer on a given benchmark does not imply it will be a higher performer on the problem you care about. Even small or apparently trivial changes to the formulation of your problem can significantly change the performance of any fixed set of algorithms.

That said, given the specifics of the problem you care about I would recommend the following:

• use the cascading approach described in the paper [1]
• use SIMD operations (either SSE on intel chips or GPUs) to accelerate, the nearest neighbour problem is one where operations closer to the metal and parallelism can really shine
• tune the parameters of the algorithm to maximize your objective; in particular, the algorithm of [1] has a few easy to tune parameters which will dramatically trade performance for accuracy, make sure you perform a grid search over these parameters to set them to the sweet spot for your problem

Note: I have recommended the paper [1] because I have tried many of the algorithms listed in the benchmark you linked and found them all inferior (for the task of image reconstruction) to the approach listed in [1] while at the same time being much more complicated than [1], both undesirable properties. YMMV depending on your problem definition.