An Efficient Parallel Implementation of an Optimized Simplex Method in GPU-CUDA

The general-purpose graphics processing unit programming has been widely used and has achieved satisfactory results across many knowledge areas and it keeps evolving since its earlier stages. This paper presents an implementation of a modified Simplex method using the computational power brought from graphics processing unit (GPU) computing using the Nvidia framework for GPU programming, compute unified device architecture (CUDA). The results achieved shown that the parallel GPU implementation reached 15x and 22x maximum speedup measuring the overall time (data transfer plus kernel work time) and the kernel computation time respectively, in comparison with the standard sequential implementation using central processing unit (CPU).