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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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Host Testing and evaluation

@HostEvaluate · Post #718 · 02/05/2021, 09:51 AM

HostProvider: Hostsolutions (NVMe) Specification: 2Core E5-2680v4 | 2G DDR4 | 50G NVMe Network: 15T@1Gbps Test IP: 45.14.149.1 Price: €4.05 / Quarterly #RO#Hostsolutions#M247 https://paste.ubuntu.com/p/pYmjPncfbS/ 仅联通绕美,性能相比其他产品确实好得多。性价比非常高。