Research Report on the Development of Artificial Intelligence Large Language Model Technology (2024)

Report Producer: China Software Evaluation Center

With the continuous expansion of the scale of large models, computing power management and scheduling have become particularly important. Effective computing power management and scheduling strategies can ensure the full utilization of computing resources, avoid resource waste, and improve training efficiency.

This includes proper task allocation, load balancing, resource monitoring, and dynamic tuning. Third, high-speed memory and storage effectively improve training efficiency. Large models need to read and write large amounts of data quickly during the training process, so they require high-speed memory and storage devices. For example, the use of high-speed storage devices such as DDR4 memory and NVMeSSDs can significantly improve training efficiency.

Fourth, network connection and communication affect the training speed. In distributed training, high-speed network connections are required between individual compute nodes to transmit data and synchronize gradient information. Therefore, the speed and stability of network connection and communication have an important impact on the training efficiency of large models. At present, the industry has carried out effective work in the coordination of computing, storage, and network.

In distributed training, the GPU continuously communicates between and within machines,5 and uses high-performance networks such as IB and RoCE to provide high-throughput and low-latency services for inter-machine communication, and at the same time, the internal network connection of the server and the communication topology in the cluster network need to be specially designed to meet the communication requirements of large model training.

NVIDIA GPUs can transfer up to 600GB/s of data between each other, and 8 or 16 GPUs can form a server host, which can better achieve high-speed data transmission to support large-scale model training. Baidu Intelligent Cloud and NVIDIA have jointly built a large-scale high-performance GPU/IB cluster, which has been specially designed and optimized to give full play to the overall computing power of the cluster.

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The report is 49 pages long

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