Strong Scaling

Strong scaling in machine learning focuses on improving the efficiency of training and inference as the number of processing units (e.g., GPUs) increases, maintaining performance even with a larger number of agents or model parameters. Current research emphasizes efficient algorithms and architectures like mixture-of-experts models, attention mechanisms, and novel training strategies such as burst parallelism and dynamic layer operations to overcome limitations in scaling existing models. These advancements are crucial for training increasingly large models, such as large language models and graph neural networks, enabling progress in areas like materials science, natural language processing, and computer vision while mitigating the high computational costs associated with scaling.