Parallel Simulation

Parallel simulation accelerates computationally intensive tasks by distributing workloads across multiple processors, enabling faster and larger-scale simulations than previously possible. Current research focuses on optimizing parallel algorithms for diverse applications, including reinforcement learning, robotics, materials science, and neuromorphic computing, employing techniques like flow matching, spiking neural networks, and GPU acceleration to achieve significant speedups. These advancements enable more realistic and comprehensive simulations, leading to improved model accuracy, faster development cycles, and the exploration of larger design spaces in various scientific and engineering domains.