Parallel Optimization

Parallel optimization aims to accelerate the training and deployment of complex models by distributing computational tasks across multiple processors or machines. Current research focuses on improving the efficiency and stability of parallel algorithms like minibatch SGD and local SGD, as well as developing novel approaches such as concurrent optimization for multiple objectives and the use of advanced attention mechanisms in large language models. These advancements are crucial for tackling increasingly large datasets and complex problems in machine learning, impacting fields ranging from autonomous driving and robotics to natural language processing and scientific computing.