Enhancing nonlinear optimization through GPU computing
Abstract. This presentation delves into the realm of GPU programming, with a specific focus on the utilization of two Julia packages: CUDA.jl and CUDSS.jl, designed to facilitate GPU computing within the Julia programming language ecosystem.
The objective is to showcase the capabilities of these packages and demonstrate their significance in accelerating computational tasks through parallel processing on GPUs.
To illustrate the effectiveness of CUDA.jl and CUDSS.jl, we turn to MadNLP.jl, an advanced nonlinear optimization solver built in Julia optimized for GPU processing.
Through a detailed comparative analysis with Ipopt, a widely recognized CPU-based optimization solver, we aim to highlight the substantial performance enhancements achieved through the utilization of GPU resources.
See also. Parallel computing and GPU programming with Julia
