3D Parallelism

3D parallelism aims to accelerate computationally intensive tasks, particularly in large language model training and other deep learning applications, by distributing workloads across multiple dimensions (data, pipeline stages, and tensor operations) of a parallel computing system. Current research focuses on automating the complex process of configuring and optimizing these parallel strategies, employing techniques like graph neural networks, mixed integer quadratic programming, and large language models to generate efficient parallel code and automatically determine optimal configurations for diverse hardware architectures. These advancements promise significant improvements in training speed and scalability for large models, impacting both the efficiency of scientific computing and the development of more powerful AI systems.