With the maturity of deep learning, its use is emerging in every field. Also, as different types of GPUs are becoming more available in the markets, it creates a difficult decision for users. How can users select GPUs to achieve optimal performance for a specific task? Analysis of GPU architecture is well studied, but existing works that benchmark GPUs do not study tasks for networks with significantly larger input. In this work, we tried to differentiate the performance of different GPUs on neural network models that operate on bigger input images (256x256).

翻译：随着深度学习的成熟，其应用正渗透到各个领域。同时，随着市场上不同类型GPU的日益普及，用户面临着艰难的选择：如何挑选GPU以在特定任务中实现最优性能？尽管GPU架构分析已得到充分研究，但现有GPU基准测试工作并未针对输入规模显著增大的网络任务进行探究。本研究尝试区分不同GPU在处理大尺寸输入图像（256x256）的神经网络模型上的性能表现。