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Source channel @githubtrending · Post #15077 · Aug 20

#c_lang#infiniband#iwarp#kernel_rdma_drivers#linux_kernel#rdma#roce#userspace_libraries You can use RDMA Core, a set of Linux userspace libraries and daemons, to work with RDMA devices for high-speed network communication. It supports many kernel drivers and provides tools and libraries like libibverbs and librdmacm to manage RDMA devices and connections. You can build it easily with cmake and install required packages depending on your Linux distribution. Using RDMA Core lets you set up software RDMA interfaces and verify them with commands like `ibv_devices` or `rdma link`. This helps you achieve faster, low-latency data transfer, which is useful for high-performance computing and networking tasks. https://github.com/linux-rdma/rdma-core

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@githubtrending · Post #15271 · 11/05/2025, 12:30 PM

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#cplusplus#arm#baidu#deep_learning#embedded#fpga#mali#mdl#mobile#mobile_deep_learning#neural_network Paddle Lite is a lightweight, high-performance deep learning inference framework designed to run AI models efficiently on mobile, embedded, and edge devices. It supports multiple platforms like Android, iOS, Linux, Windows, and macOS, and languages including C++, Java, and Python. You can easily convert models from other frameworks to PaddlePaddle format, optimize them for faster and smaller deployment, and run them with ready-made examples. This helps you deploy AI applications quickly on various devices with low memory use and fast speed, making it ideal for real-time, resource-limited environments. It also supports many hardware accelerators for better performance. https://github.com/PaddlePaddle/Paddle-Lite

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