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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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@githubtrending · Post #15341 · 12/18/2025, 11:30 AM

#c_lang#driver#flash#jedec#jedec_sfdp#qspi#sfdp#sfdp_flash#spi_flash#universal_driver **SFUD** is an open-source library that drives many SPI/QSPI Flash chips from brands like Winbond and Macronix. It auto-detects chip specs via the **SFDP** standard or a built-in table, letting you read, write, erase, and init with simple APIs after easy config. This helps you avoid risks from Flash shortages or upgrades, boosts software reuse across projects, cuts dev time, and enables tools like programmers—saving effort on varied hardware. https://github.com/armink/SFUD