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[Description](#description)\n2. [Information](#information)\n3. [Certificate](#certificate)\n\n\u003ca name=\"descripton\"\u003e\u003c/a\u003e\n## Description\n\nThis course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. \n\nYou’ll learn how to:\n- Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs);\n- Use Numba to create and launch custom CUDA kernels;\n- Apply key GPU memory management techniques.\n- Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.\n\n\u003ca name=\"information\"\u003e\u003c/a\u003e\n## Information\nAt the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:\n\n\u003e - GPU-accelerate NumPy ufuncs with a few lines of code.\n\u003e - Configure code parallelization using the CUDA thread hierarchy.\n\u003e - Write custom CUDA device kernels for maximum performance and flexibility.\n\u003e - Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.\n\nMore detailed information and links for the course can be found on the [course website](https://www.nvidia.com/en-in/training/instructor-led-workshops/fundamentals-of-accelerated-computing-with-cuda-python/).\n\n\u003ca name=\"certificate\"\u003e\u003c/a\u003e\n## Certificate\n\nThe certificate for the course can be found below:\n\n- [\"Fundamentals of Accelerated Computing with CUDA Python\" - NVIDIA Deep Learning Institute](https://learn.nvidia.com/certificates?id=lixK_Nx1Siq8PuLGeQTH1w) (Issued On: January 2025)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhrolive%2Ffundamentals-of-accelerated-computing-with-cuda-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhrolive%2Ffundamentals-of-accelerated-computing-with-cuda-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhrolive%2Ffundamentals-of-accelerated-computing-with-cuda-python/lists"}