Overview
GPU-Accelerated Boids Flocking Simulation - A high-performance CUDA implementation of Craig Reynolds' boids algorithm, demonstrating parallel computing techniques for real-time particle simulation with OpenGL visualization.
Description
A real-time flocking simulation implementing Craig Reynolds' classic boids algorithm on the GPU using CUDA. The project explores three progressively optimized approaches to neighbor searching, demonstrating significant performance improvements through spatial data structures and memory access optimization.
The simulation renders thousands of autonomous agents (boids) that exhibit emergent flocking behavior based on three simple rules: cohesion (fly towards local center of mass), separation (avoid crowding neighbors), and alignment (match velocity with nearby boids).
- Implemented three simulation methods: Naive O(N²) brute-force, Scattered Uniform Grid, and Coherent Uniform Grid with memory coalescing
- Utilized CUDA parallel primitives and Thrust library for efficient GPU-based sorting and spatial hashing
- Achieved significant performance gains through uniform grid spatial partitioning, reducing neighbor search complexity from O(N²) to O(N)
- Optimized GPU memory access patterns using coherent data layout for improved cache utilization
- Integrated real-time OpenGL visualization with CUDA-OpenGL interoperability
- Implemented ping-pong buffering strategy for concurrent read/write operations in parallel velocity updates