Collective Communication

Collective communication, the coordinated exchange of data among multiple computing units, is crucial for scaling machine learning and other distributed applications. Current research focuses on optimizing collective communication algorithms for diverse hardware architectures (e.g., GPUs, FPGAs) and network topologies, addressing challenges like noisy channels, data heterogeneity, and hardware failures through techniques such as adaptive clustering, efficient scheduling, and communication-volume reduction. These advancements are vital for improving the efficiency and scalability of large-scale model training, particularly for large language models and other computationally intensive tasks, impacting both the speed and feasibility of scientific discovery and technological innovation.