High Performance Compute for the JVM - A Prerequisite for DL4J (Part 2)

CUDA is of special interest to our users, since it dramatically boosts performance in parallel computations. This in turn significantly lowers the time required for tuning and training models. While we seamlessly support NVIDIA's cuDNN library of deep learning primitives, we also make the power and performance of the GPUs accessible to end users without cuDNN installed. This kind of stack - all in Java with a "native" backend, however, comes with it's own set of unique challenges. These native operations are essentially a set of independent individual operations applied to the same data.