How AI Data Center Promotes the Development of 400G/800G Optical Transceiver Modules?
With the continuous development of AI technology and related applications, the importance of large models, big data and AI computing capabilities in the development of AI has become increasingly prominent. Large models and data sets form the software foundation for AI research, and AI computing power is the key infrastructure. In this article, we explore the impact of AI developments on data center network architecture.
Fat-Tree data center network architecture
With the widespread application of AI large model training in various industries, traditional networks cannot meet the bandwidth and latency requirements of large model cluster training. Distributed training of large models requires communication between GPUs, and its traffic pattern is different from traditional cloud computing, which increases the east-west traffic of AI/ML data centers. Short-term and high-volume AI data lead to network latency and reduced training performance in traditional network architectures. Therefore, in order to meet the needs of short-term and high-volume data processing, the emergence of Fat-Tree network is inevitable.
In a traditional tree network topology, bandwidth is aggregated layer by layer, and the network bandwidth at the bottom of the tree is much smaller than the total bandwidth of all leaf nodes. In comparison, a Fat-Tree looks like a real tree, with thicker branches near the root. Therefore, network bandwidth gradually increases from leaves to roots, improving network efficiency and accelerating the training process. This is the basic premise of the Fat-Tree architecture, which enables non-blocking networks.
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