2025-07-23 –, Main Room 4
This project democratizes GPU hardware by developing a GPU-accelerated simulation engine for modeling physical systems on manifolds. Built with CUDA and Julia, it introduces innovative methods for high-performance simulations, enabling efficient modeling of complex phenomena. By emphasizing user-friendliness, scalability, and efficiency, the project lowers barriers for researchers to leverage GPU power, promoting the adoption of advanced computational tools through the Julia framework.
As a research group interested in performing physics simulations, we encountered two major challenges: understanding the complexity of the physical systems involved and dealing with the limitations of computational resources. Simulating intricate systems often required significant time and effort to grasp the underlying physics, while the simulations themselves were slow and computationally demanding. This led us to explore GPU acceleration as a solution. However, diving into GPU computing revealed the additional complexity of managing parallelism and optimizing performance for what we were trying to achieve. From this point, the research began—focusing on improving simulation performance, expanding the range of physical systems modeled, and enabling more efficient and accessible tools. Our aim is to develop a comprehensive framework for physics simulations, but currently, we have only achieved generalizations for certain systems, with the goal of advancing further in the future.
The framework is designed to model complex physical phenomena on manifolds with a focus on precision, scalability, and usability. Examples of simulations developed include fluid dynamics on manifolds, such as spheres and tori, to explore the behavior of flows influenced by geometry. Transport phenomena in manifold geometries, including diffusion and flow processes, highlight the framework's ability to handle intricate physical systems. Particle interactions in granular systems are modeled to study collisions, clustering, and emergent behaviors under varying forces like gravity and friction. Geodesic motion simulations analyze particle dynamics on non-Euclidean spaces, offering insights into trajectories on curved geometries. Coupled processes in multiphysics environments, such as active matter systems where particles exhibit collective dynamics, and electromagnetic simulations for field interactions, highlight the framework’s ability to address diverse and complex problems. These examples underscore the framework's versatility in handling both theoretical and applied physics challenges.
In addition to enhancing computational performance, this project contributes to the growth of the Julia community in GPU computing. By providing accessible tools and showcasing the capabilities of Julia in high-performance parallel computing, it encourages new users to engage with both GPU programming and the Julia ecosystem. The engine lowers technical barriers, fostering the adoption of GPUs while supporting Julia's expansion into diverse areas of computational physics.
