想学 GPU 编程但被 CUDA C++ 劝退的 Python 开发者，这个教程让你直接在 Colab 里跑分块内核，还能对比 PyTorch 验证结果，值得动手试试。
本教程介绍了 NVIDIA cuTile Python，一个基于分块的 GPU 编程接口，允许在 Python 中编写类 CUDA 的内核。教程在 Google Colab 环境中逐步实现向量加法、矩阵加法和矩阵乘法的分块内核，并包含 PyTorch 回退以确保可执行性。每个阶段都通过 PyTorch 验证正确性并基准测试中位运行时间。该教程适合希望学习 GPU 编程但不想深入 CUDA C++ 的 Python 开发者。
NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab
In this tutorial, we implement a hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for CUDA-style kernels in Python. We prepare a Colab-friendly environment and check GPU, driver, CUDA, and cuTile availability before running kernels. We then build tiled vector addition, matrix addition, and matrix multiplication, keeping a PyTorch fallback so the notebook stays executable. We validate correctness against PyTorch and benchmark median runtimes at every stage. The post NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab appeared first on MarkTechPost .