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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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@eco_cn · Post #29975 · 03/17/2026, 03:42 AM
摩根大通：在CPO方面，市场叙事已经历从熊到中性再到看多的完整轮转。早期市场担忧CPO的普及会压缩收发器收入，但这一逻辑已被事实证伪——CPO带来的增量营收机会对相关公司的增长预期形成叠加而非替代。 行业分析机构预测，#CPO 市场规模将从2027年的不足5亿美元，增长至2030年的约100亿美元，其中规模扩展（scale-up）应用场景而非规模延伸（scale-out）是主要增长引擎。 投资者的关注点也随之转移：在确认光学公司整体受益于CPO之后，下一阶段的核心问题在于CPO赢家与输家的分化。摩根大通认为，Coherent #COHR 和Lumentum #LITE 凭借既有订单及英伟达的供应链定向投资，已稳固确立了CPO赢家地位。