The article discusses the importance of AI Networks, focusing on the basic concepts of AI networking. It explains how GPUs are essential for AI tasks due to their ability to process multiple tasks simultaneously, speeding up complex calculations required for training machine learning models. The article also delves into the significance of RDMA in facilitating low-latency, high-throughput data transfers between nodes, optimizing data movement for AI processes.
Furthermore, the article explores InfiniBand technology, designed for high-speed networking in data centers and HPC environments, enabling low-latency, high-bandwidth connections between servers and storage systems. It highlights the development of RoCEv2 (RDMA over converged Ethernet) to carry Infiniband payload with Ethernet, allowing for routing of infiniband workloads between different subnets.
Moreover, the article emphasizes the need for a lossless connection in AI networking, which is achieved through Quality of Service mechanisms like PFC and ECN. PFC helps control congestion by prioritizing traffic, ensuring important data like AI workloads are given precedence. ECN notifies endpoints about congestion before packet loss occurs, preventing congestion collapse and improving network efficiency.
Overall, the article discusses how with the implementation of PFC and ECN, a lossless network can be created, allowing for the smooth transmission of AI training and inferencing traffic via Ethernet. It also hints at further discussions on RoCEv2 considerations in a future post.
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Source link: https://medium.com/@pdkrzz/ai-networking-basics-6f4e60c8516b?source=rss——artificial_intelligence-5