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This article is a sequel to "GPU implementation of a ray-surface intersectionalgorithm in CUDA" (arXiv:2209.02878) [1]. Its main focus is PyCUDA whichrepresents a Python scripting approach to GPU run-time code generation in theCompute Unified Device Architecture (CUDA) framework. It accompanies theopen-source code distributed in GitHub which provides a PyCUDA implementationof a GPU-based line-segment, surface-triangle intersection test. The objectiveis to share a PyCUDA learning experience with people who are new to PyCUDA.Using the existing CUDA code and foundation from [1] as the starting point, wedocument the key changes made to facilitate a transition to PyCUDA. As the CUDAsource for the ray-surface intersection test contains both host and device codeand uses multiple kernel functions, these notes offer a substantive example andreal-world perspective of what it is like to utilize PyCUDA. It delves intocustom data structures such as binary radix tree and highlights some possiblepitfalls. The case studies present a debugging strategy which may be used toexamine complex C structures in device memory using standard Python toolswithout the CUDA-GDB debugger.

An experience with PyCUDA: Refactoring an existing implementation of a ray-surface intersection algorithm