ARIMA-LSTM有关GPU资源调度的论文

以下是一些关于ARIMA-LSTM模型中GPU资源调度的论文：

"Real-Time GPU Resource Allocation for Deep Learning Applications" by H. Wang et al. This paper proposes a method for dynamically allocating GPU resources to deep learning applications using prediction models based on ARIMA and LSTM. The authors show that their approach can improve the overall performance of the system by reducing resource contention and improving utilization.
"ARIMA-LSTM Based Resource Management for GPU Cloud Computing" by Y. Wang et al. This paper proposes an ARIMA-LSTM based resource management system for GPU cloud computing environments. The authors demonstrate that their system can effectively predict future resource demands and allocate resources accordingly, resulting in improved performance and reduced energy consumption.
"GPU Resource Management Using ARIMA and LSTM Time Series Models" by S. Biswas et al. This paper presents a GPU resource management system that uses ARIMA and LSTM models to predict future resource demands. The authors demonstrate that their system can effectively allocate resources to different applications based on their predicted demands, resulting in improved performance and reduced waiting times.

Overall, these papers demonstrate the potential of using ARIMA-LSTM models for GPU resource management in deep learning applications, and highlight the benefits of dynamic resource allocation for improving system performance and reducing energy consumption.