Journal Article

HPC Cluster Task Prediction Based on Multimodal Temporal Networks with Hierarchical Attention Mechanism

Zhou, Jingbo, Bai, Xuemei, Wang, Zhijun

2025

Overview

In recent years, the increasing adoption of High-Performance Computing (HPC) clusters in scientific research and engineering has exposed challenges such as resource imbalance, node idleness, and overload, which hinder scheduling efficiency. Accurate multidimensional task prediction remains a key bottleneck. To address this, we propose a hybrid prediction model that integrates Informer, Long Short-Term Memory (LSTM), and Graph Neural Networks (GNN), enhanced by a hierarchical attention mechanism combining multi-head self-attention and cross-attention. The model captures both long- and short-term temporal dependencies and deep semantic relationships across features. Built on a multitask learning framework, it predicts task execution time, CPU usage, memory, and storage demands with high accuracy. Experiments show prediction accuracies of 89.9%, 87.9%, 86.3%, and 84.3% on these metrics, surpassing baselines like Transformer-XL. The results demonstrate that our approach effectively models complex HPC workload dynamics, offering robust support for intelligent cluster scheduling and holding strong theoretical and practical significance.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Clustering (Computers) / Clusters / Computational linguistics / Computer memory / Efficiency / Forecasting / Graph neural networks / hierarchical attention mechanism / High performance computing / high-performance computing scheduling / Informer–LSTM–GNN hybrid model / Language processing / Methods / multimodal temporal prediction / Natural language interfaces / Neural networks / Prediction models / Scheduling / Workloads