Asynchronous Parallel

Asynchronous parallel computing aims to accelerate computations by allowing multiple processing units to work concurrently without strict synchronization, thereby improving throughput and resource utilization. Current research focuses on optimizing asynchronous approaches for diverse applications, including Bayesian optimization in materials science, distributed machine learning (employing methods like selective synchronization and incremental block-coordinate descent), and evolutionary algorithms. These advancements are significant because they address the limitations of traditional synchronous methods, leading to faster training times for machine learning models and more efficient exploration of complex search spaces in various scientific and engineering domains.