RTSRT: Accelerating Monte Carlo Particle Transport with Ray Tracing Shared Cache Architecture
Cunhao Cui, Tiejun Li, Jianmin Zhang, Hanqing Li, Changsong Jin, Ruixuan Ren · 2024
The Monte Carlo (MC) method is widely used for solving particle transport problems by tracking a large number of particles through a model to simulate their interactions. Recently, GPU Ray Tracing (RT) accelerators have been explored to enhance MC simulation performance, since the geometric operations involved in particle transport simulations can be formulated as a ray tracing problem. However, these simulations are always accompanied by cache contention between the RT accelerator and the rest of the GPU stream multiprocessor (SM) pipeline, due to the memory-intensive nature of the geometric operations and cross-section data calculations in MC particle transport. To address this, we organize the RT caches dedicated to RT accelerators into a Shared architecture through a Redirection Table (RTSRT). RTSRT improves the parallelism between the RT accelerator and the rest of the SM pipeline, thereby enhancing the performance of MC particle transport simulations. The evaluation results show that when applied to complex models, RTSRT can improve the performance of the MC particle transport proxy application Quicksilver by 25% with minimal area overhead as compared with the baseline RT accelerator.