🤖 AI Summary
Large language model (LLM) pretraining faces significant challenges, including prohibitive computational costs, opaque scaling laws, and a lack of practical guidance for large-scale distributed training. Method: This work systematically investigates the performance scaling mechanisms of LLM pretraining pipelines at the hundred-node scale, focusing on three key directions: optimization of distributed training architecture, cross-node efficient dataset management, and deep scaling of data parallelism—aiming to maximize GPU resource utilization. Through empirical analysis, we quantify the interplay among communication overhead, I/O bottlenecks, and parallelism degree, and establish a reproducible framework for large-scale training performance tuning. Contribution/Results: Our study bridges a critical gap in the public literature on engineering practices for ultra-large-scale LLM training, delivering a practical, deployable technical pathway and actionable guidelines for efficient pretraining on thousand-GPU clusters.
📝 Abstract
Large language models (LLMs) show best-in-class performance across a wide range of natural language processing applications. Training these models is an extremely computationally expensive task; frontier Artificial Intelligence (AI) research companies are investing billions of dollars into supercomputing infrastructure to train progressively larger models on increasingly massive datasets. Unfortunately, information about the scaling performance and training considerations of these large training pipelines is scarce in public literature. Working with large-scale datasets and models can be complex and practical recommendations are scarce in the public literature for tuning training performance when scaling up large language models. In this paper, we aim to demystify the large language model pretraining pipeline somewhat - in particular with respect to distributed training, managing large datasets across hundreds of nodes, and scaling up data parallelism with an emphasis on fully leveraging available GPU compute capacity.