A GPU resource prediction method based on the SGRU-AATransformer hybrid model

With the extensive application of GPUs in artificial intelligence, high-performance computing, and other fields, especially their critical role in future cloud-based wireless networks, accurately predicting GPU load has become crucial. This paper proposes a novel hybrid model—SGRU-AATransformer, aiming to improve the accuracy of GPU load prediction. The model combines the Shared Gated Recurrent Unit (SGRU) for capturing short-term nonlinear dynamic changes and the Approximate Attention Transformer (AATransformer) for modeling long-term dependencies. Experimental results demonstrate that SGRU-AATransformer performs well in both short-term and long-term predictions, significantly outperforming existing single and hybrid prediction models.