high performance computing on graphics processing units: hgpu.org

Differential Evolution (DE) algorithms can be used in various fields for problem solving where we need to find an optimal (or close to optimal) solution but we don’t have a clear, straightforward method to compute it. Unfortunately it can take a very long time to produce such a solution when implemented serially or even parallel on a Central Processing Unit (CPU). To reduce computation time we can utilise the power of Graphics Processing Units (GPU) using Compute Unified Device Architecture (CUDA) technology. Since GPUs are designed purely for computational tasks as opposed to CPUs which are more general-work oriented they can provide a great architecture for parallelising our DE method.