DDC Technology: A Revolutionary Solution by AIGC Network

Three Methods of GPU Load Sharing

The extraordinary power of the aforementioned AIGC large models stems not only from their vast amount of data, but also from the continuous evolution and advancement of algorithms. When training these large models, multiple GPUs are often required to distribute the workload. Three methods are employed to achieve efficient load sharing: data parallelism, tensor parallelism, and pipeline parallelism.

Data Parallelism

Data parallelism involves splitting the model's input data into multiple mini-batches and parallelly processing these mini-batches across different GPUs. Each GPU possesses a complete copy of the model and independently handles its own mini-batches. Through inter-GPU communication and synchronization, the model parameters are updated and integrated. Data parallelism is suitable for models with large-scale training datasets, accelerating the training process and improving the convergence speed of the model.

Tensor Parallelism

Tensor parallelism is typically employed when the model is too large to fit in a single processor's memory. In this method, different parts of the model are allocated to different GPUs, with each GPU responsible for processing a portion of the input data and executing the corresponding computations. Through frequent inter-GPU communication and synchronization, the outputs are collected and integrated, which may lead to high communication overhead. High-speed connections between processors are necessary for tensor parallelism to minimize exchange delays.

Pipeline Parallelism

Pipeline parallelism breaks down the model's computational flow into multiple stages, parallelizing the execution of these stages across different GPUs in a pipelined manner. Each GPU handles a specific portion of the overall model computation, passing the processed results to the next GPU for further calculations. This approach reduces the overall training time and is especially beneficial for models with complex computational flows and multiple consecutive stages, but it requires careful management of the pipeline to avoid creating too large stalls, where some processors may pause their work while waiting for dependent computation results. Sometimes, these three parallel tactics are coupled to improve the training process.

Three Traditional Solutions to Support the Operation of AIGC

In traditional solutions, there are three common solutions to support the operation of AIGC: InfiniBand, RDMA, and frame switches.

InfiniBand Networking

InfiniBand networking is a high-speed interconnect technology used in high-performance computing and data centers. Its advantages include high bandwidth, low latency, and no congestion. However, it can be expensive and costs several times more than traditional Ethernet networking.