Parallelization Strategies in Neural Networks
From a network engineer’s perspective, it is not mandatory to understand the full functionality of every application running in a datacenter. However, understanding the communication patterns of the most critical applications—such as their packet and flow sizes, entropy, transport frequency, and link utilization—is essential. Additionally, knowing the required transport services, including reliability, in-order packet delivery, and lossless transmission, is important.
In AI fabrics, a neural network, including both its training and inference phases, can be considered an application. For this reason, this section first briefly explains the basic operation of the simplest neural network: the Feed Forward Neural Network (FNN). It then discusses the operation of a single neuron. Although a deep understanding of the application itself is not required, this section equips the reader with knowledge of what pieces of information are exchanged between GPUs during each phase and why these data exchanges are important.
Feedforward Neural Network: Forward Pass
Figure 1-7 illustrates a simple four-layer Feed Forward Neural Network (FNN) distributed across four GPUs. The two leftmost GPUs reside in Node-1, and the other two GPUs reside in Node-2. The training data is fed into the first layer. In real neural networks, this first layer is the input Continue reading
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