Like PyCUDA, PyOpenCL can help us solve a number of computational problems based on GPU computations via Python, with or without OpenCL-C/C++ code. PyOpenCL is important in regards to OpenCL because it significantly minimizes the latter's code complexity and makes it much easier and user-friendly—thanks to the simplicity of Python code. We will try to understand this through an example in this section. All PyOpenCL code in this chapter has been tested on a new AMD Radeon VII GPU.
Following our first C++ versus CUDA and OpenCL examples, we will look into a very simple PyOpenCL example with a similar approach, hands-on with PyCharm. Following this, we'll again shift our focus toward actual GPU-accelerated computations for solving specific computational problems with PyOpenCL.
We will now write a PyOpenCL program to initialize...
