Optimizing Particle Systems through CUDA-Assisted Multithreading

Abstract

Particle systems present challenges that have warranted and attracted large amount of attention in both usage and optimization. The use of particle systems has driven complexity of simulation to greater needs of data size and accuracy. Optimization, thus, has become a moving target for researchers to reach. Studies show that multithreading has potential to make the simulation efficient while optimizing complex and data-intensive particle systems. The CUDA (Compute Unified Device Architecture) works with programming languages such as C/C++ and Python to make multithreaded parallel programming easier. This work serves to analyze particle systems using CUDA and provide an understanding about how various parameters such as the particle count and grid size influence the simulation performance. We improve the CUDA particles demo by Nvidia using our Python scripts and study the impact of particles and grids on execution time and throughput. Experimental results indicate that a required level of performance can be achieved by varying the number of particles, the size grids, and the orientation of grids as needed.

Enhancing Particle Swarm Optimization Performance Through CUDA and Tree Reduction Algorithm

“…The experimental results in their work demonstrate the achievement of desired performance levels by adjusting the number of particles, grid size, and grid orientation. It also presents hypotheses regarding the impact of changing these parameters on processing time and provides experimental results to support these hypotheses [23]. Furthermore, another work introduces a new approach to running standard particle swarm optimization (SPSO) by utilizing GPU's parallel computing capability and NVIDIA's CUDA software platform.…”