GPUs are renowned for their ability to render high resolution, fast action video games. They are able to crunch together the millions of necessary calculations per second in order to ensure that every vertex of your game's 3D models are in the right place, and that they are updated every few milliseconds in order to ensure a smooth 60 FPS.
Generally speaking, GPUs are incredibly good at performing the same task in parallel, millions upon millions of times per minute. But if GPUs are so performant, then why do we not employ them instead of our CPUs? While GPUs may be incredibly performant at graphics processing, they aren't however designed for handling the intricacies of running an operating system and general purpose computing. CPUs have fewer cores, which are specifically designed for speed when it comes to switching context between operating tasks. If GPUs were given the same tasks, you would see a considerable degradation in your computer's overall performance.
But how can we utilize these high-powered graphics cards for something other than graphical programming? This is where libraries such as PyCUDA, OpenCL, and Theano come into play. These libraries try to abstract away the complicated low-level code that graphics APIs have to interact with in order to utilize the GPU. They make it far simpler for us to repurpose the thousands of smaller processing cores available on the GPU, and utilize them for our computationally expensive programs:
These Graphics Processing Units (GPU) encapsulate everything that scripting languages are not. They are highly parallelizable, and built for maximum throughput. By utilizing these in Python, we are able to get the best of both worlds. We can utilize a language that is favored by millions due to its ease of use, and also make our programs incredibly performant.
In the following sections, we will have a look at the various libraries that are available to us, which expose the power of the GPU.
