A Comparative Study of Learning Techniques with Convolutional Neural Network Based on HPC-Workload Dataset

Abstract

High-Performance Computing or HPC is a computer system that has high computing power. The HPC supports various computational domains. A huge amount of jobs from a large group of users prefer to complete their jobs in this kind of system. Therefore, managing the jobs or job scheduling is very important since it involves the overall system efficiency. The analysis of an HPC-workload log file is a solution to improve system efficiency. Because some information may appear in the log file, this information can help the system scheduler to make an appropriate decision for job scheduling in the HPC system. This research proposed predictive models for predicting the job status at the finishing state in the HPC system. The model can be used as a tool for monitoring the jobs in the HPC system. We develop and build the three models including HPC-CNN, HPC-AlexNet, and HPC-VGG16 based on the two different learning techniques, which comprise Initial and Transfer Learning of Convolutional Neural Network based on the HPC-work load dataset. Moreover, the three state-of-the-art Machine Learning methods: Classification and Regression Tree (CART), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are used as the baseline models for performance comparison. The results show that the model that performs the best predictive performance is the proposed HPC-CNN model. It archives 76.48% accuracy of the prediction followed with the CART model (75.60%), while the SVM model performs lowest the accuracy at 66.80%.