GPUs have become increasingly programmable. In fact, their set of instructions has been extended to allow the execution of a greater number of tasks.

Today, on a GPU, it is possible to execute classic CPU programming instructions, such as cycles and conditions, memory access, and floating-point calculations. The two major discrete video card manufacturers—NVIDIA and AMD—have developed their GPU architectures, providing developers with related development environments that allow programming in different programming languages, including Python.

At present, developers have valuable tools for programming software that uses GPUs in contexts that aren't purely graphics-related. Among the main development environments for heterogeneous computing, we have CUDA and OpenCL.

Let's now have a look at them in detail.