Applying deep neural networks to HEP job classification. Issue 5 (December 2015)

• Record Type: Journal Article
• Authors: Wang, L; Shi, J; Yan, X
• Is Part Of: Journal of physics. Volume 664:Issue 5(2015)
• Publication Date: 2015-12
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/664/5/052042
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision.