Beyond CUDA: GPU Accelerated Python for Machine Learning on Cross-Vendor Graphics Cards Made Simple

Machine learning algorithms -- together with many other advanced data processing paradigms -- fit incredibly well to the parallel-architecture that GPU computing offers. This has driven massive growth in the advancement and adoption of graphics cards for accelerated computing in recent years. This has also driven exciting research around techniques that optimize towards concurrency, such as model parallelism and data parallelism. In this article you'll learn how to write your own GPU accelerated algorithms in Python, which you will be able to run on virtually any GPU hardware -- including non-NVIDIA GPUs. We'll introduce core concepts and show how you can get started with the Kompute Python framework with only a handful of lines of code. First we will be building a simple GPU Accelerated Python script that will multiply two arrays in parallel which this will introduce the fundamentals of GPU processing.