Parallelization Strategy

Parallelization strategies aim to optimize the speed and efficiency of computationally intensive tasks by distributing workloads across multiple processing units. Current research focuses on enhancing the performance of large language models (LLMs), deep neural networks (DNNs), and other machine learning algorithms through techniques like data parallelism, model parallelism, and hybrid approaches, often incorporating advanced scheduling and memory management. These advancements are crucial for accelerating scientific discovery and enabling real-time applications in areas such as AI inference, big data analysis, and computational fluid dynamics, where processing demands are rapidly increasing. The development of automated parallelization tools and the exploration of optimal partitioning strategies are also key areas of investigation.