Benchmarking GPUs on SVBRDF Extractor Model
Narayan Kandel Melanie Lambert
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).