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Source channel @githubtrending · Post #14985 · Jul 22

#c_lang#cuda#cuda_driver_api#cuda_kernels#cuda_opengl You can use the CUDA Samples from NVIDIA to learn and test CUDA Toolkit 12.9 features by downloading them from GitHub or as a ZIP file. These samples show how to use CUDA for GPU programming, including utilities, concepts, libraries, and performance optimization. You build them with CMake on Linux, Windows, or Tegra devices, and can run tests automatically with a provided Python script. This helps you understand CUDA programming, debug GPU code, and optimize your applications for better performance on NVIDIA GPUs. It’s a practical way to develop and improve GPU-accelerated software efficiently. https://github.com/NVIDIA/cuda-samples

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djangoproject

@djangoproject · Post #170 · 09/22/2016, 02:27 PM

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https://pypi.python.org/pypi/uvloop #uvloop is a fast, drop-in replacement of the built-in #asyncio event loop. uvloop is released under the MIT license. uvloop and asyncio, combined with the power of async/await in Python 3.5, makes it easier than ever to write high-performance #networking code in Python. uvloop makes asyncio fast. In fact, it is at least 2x faster than #nodejs, #gevent, as well as any other Python #asynchronous framework. The performance of uvloop-based asyncio is close to that of Go programs.

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#uvloop #asyncio #networking #nodejs #gevent #asynchronous