In this chapter, the general syntax of HIP and OpenCL code was explained with documented examples. The steps to install PyOpenCL with or without Anaconda were illustrated within an existing NVIDIA or AMD OpenCL environment. The configuration measures to set up PyOpenCL were explained step by step, we learned how computing works in Python, and the significance of computational problem solving was highlighted. With a comparison of PyOpenCL, HIP, and OpenCL, the concept of parallel reduction was revisited.
Now that this chapter is at its end, you should now be able to test your own HIP or OpenCL program. You should also be able to install and configure PyOpenCL within an existing OpenCL environment. Porting your own CUDA code to a cross-platform HIP format that can be run on both NVIDIA and AMD GPUs will also be very convenient from now on. You can now start experimenting...
