Source: Stratio Blog
Project by Haoyang Cai & Lucy Li
Source: Stratio Blog
We are going to implement a parallel solution of particle swarm optimization using CUDA.
Particle Swarm Optimization (PSO) is a robust stochastic optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to solve complex computational problems efficiently. The algorithm iteratively tries to improve a candidate solution with regard to a given measure of quality. It adapts the stochastic and deterministic aspects by having particles in the swarm follow optimal paths while being randomly influenced by the best solutions found so far.
In this project, we plan to implement a parallel version of the Particle Swarm Optimization algorithm using CUDA to leverage the computational power of GPUs. The parallelism inherent in the PSO algorithm stems from the fact that each particle's position update can be computed independently given the global and personal best positions. However, the computation of these best positions themselves requires some form of synchronization or reduction across all particles.
A key aspect of PSO that could benefit from parallelism is the evaluation of the fitness function for each particle, which is typically the most compute-intensive part of the algorithm, especially for complex problem spaces. By distributing this computation across multiple GPU cores, we can significantly reduce the execution time of the PSO algorithm.
The basic idea can be illustrated with the following pseudocode, outlining the parallel computation within a single iteration of PSO:
for each particle i in parallel:
update velocity v[i] based on personal best p[i] and global best g
update position x[i] based on velocity v[i]
evaluate fitness f[i] of new position x[i]
reduce all f[i] to find new global best g
Parallelizing the PSO algorithm poses several challenges, primarily related to data dependencies and memory access patterns:
We will utilize NVIDIA GPUs for implementing the CUDA-based PSO, given their widespread availability and robust development tools. The project will start with a basic sequential PSO implementation, which we will then parallelize. We plan to use the following resources:
At the poster session, we plan to showcase a side-by-side comparison of the sequential and parallel PSO implementations, highlighting the performance improvements and scalability of the CUDA-based approach. We will present speedup graphs, execution time comparisons, and possibly an interactive demo showing the algorithm's convergence in real-time on a chosen problem set.
Through this project, we aim to learn effective strategies for parallelizing inherently parallel algorithms like PSO on modern GPU architectures, understand how to mitigate common parallelization challenges (e.g., synchronization, memory access patterns), and gain insights into CUDA-specific optimizations that can maximize performance.
In our project, we choose to use a variety of platforms to evaluate the performance and the scalability of our parallel solution to PSO using CUDA. We will be using GHC machines with NVIDIA RTX 2080 GPUs, our PC with a RTX 4090 GPU, and PSC machines with 64 CPU cores. Testing on different GPUs will show us whether our solution scales to newer generations of GPUs. By comparing our GPU code to existing CPU implementations running on the 64-core PSC machines, we can see if using GPUs will provide a substantial advantage for the PSO problem.