Data Parallelism

Data parallelism accelerates computation by distributing data across multiple processors, enabling faster training of large models in machine learning and scientific computing. Current research focuses on optimizing data partitioning strategies for various architectures (e.g., K-means clustering, deep neural networks, large language models), including hybrid approaches that combine synchronous and asynchronous methods to improve efficiency and scalability. These advancements are crucial for tackling increasingly complex problems in fields like AI and scientific simulation, where the size and complexity of datasets demand efficient parallel processing techniques.