Parallel Processing

Parallel processing aims to accelerate computation by distributing tasks across multiple processing units, achieving significant speedups for computationally intensive problems. Current research focuses on optimizing parallel algorithms for diverse applications, including machine learning (e.g., training deep neural networks, accelerating value iteration), image processing (e.g., point cloud segmentation, 3D reconstruction), and robotic systems. These advancements are crucial for handling the ever-increasing data volumes and computational demands in various scientific fields and practical applications, ranging from autonomous vehicles to climate modeling.