We provide a preliminary study on utilizing GPU (Graphics Processing Unit) to accelerate computation for three simulation optimization tasks with either first-order or second-order algorithms. Compared to the implementation using only CPU (Central Processing Unit), the GPU implementation benefits from computational advantages of parallel processing for large-scale matrices and vectors operations. Numerical experiments demonstrate computational advantages of utilizing GPU implementation in simulation optimization problems, and show that such advantage comparatively further increase as the problem scale increases.

翻译：本文初步研究了利用图形处理器加速三种仿真优化任务的计算，这些任务分别采用一阶或二阶算法。与仅使用中央处理器（CPU）的实现相比，GPU实现得益于大规模矩阵和向量运算的并行处理优势。数值实验证明了在仿真优化问题中应用GPU实现的计算优势，并且表明随着问题规模的增大，这种优势会相对进一步扩大。