Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping into C++ to write custom kernels and to maintain bindings back to Python. For most Python developers and researchers, this is a significant barrier to entry. Frameworks like PyTorch address this by implementing kernels in CUDA C++—either handwritten or by leveraging libraries like the NVIDIA CUDA Core Compute Libraries . Handwritten kernels are time-consuming and require deep, low-level architectural expertise. Using CUB, a C++ library within CCCL, is often better, since its primitives are highly optimized per architecture and are rigorously tested.…