CoreWatch AI-Driven CPU/GPU Performance Analyzer
Article Sidebar
Main Article Content
CoreWatch is a lightweight, cross-platform monitoring system designed to provide real-time insights into CPU, GPU, memory, disk, and network performance. It uses Flask with Socket IO for low-latency metric streaming and integrates psutil and NVIDIA-SMI for accurate data collection. The dashboard employs Chart.js for smooth, interactive visualizations. An LSTM-based prediction module enhances monitoring by forecasting short-term CPU and GPU trends. CoreWatch maintains under 3% system overhead, ensuring efficient performance without additional load. Testing confirms stable responsiveness across Windows, Linux, and macOS. The system focuses on accessibility, minimal setup, and clear visual analysis. Future extensions include historical logging, improved AI models, and remote monitoring capabilities.
Downloads
References
C. L. Aldea, R. Bocu, and R. N. Solca, “Real-Time Monitoring and Management of Hardware and Software Resources in Heterogeneous Computer Networks through an Integrated System Architecture,” Symmetry, vol. 15, no. 6, article 1134, 2023.
E. Yildirim, “Predicting Runtime and Resource Utilization of Jobs on Integrated Cloud and HPC Systems,” Future Generation Computer Systems, 2025.
T. P. da Costa, S. A. de Oliveira, et al., “A Systematic Review of Real-Time Data Monitoring and Its Applications,” Expert Systems with Applications, vol. 242, article 123880, 2024.
Y. Wang and S. Xing, “AI-Driven CPU Resource Management in Cloud Operating Systems,” Journal of Computer and Communications, vol. 13, pp. 135–149, 2025.
S. Woo, “Exploring GPU Sharing Techniques for Edge AI Smart City Applications,” ETRI Journal, Wiley Online Library, vol. 47, no. 2, pp. 112–123, 2025.
L. Nashold and R. Krishnan, “Using LSTM and SARIMA Models to Forecast Cluster CPU Usage,” arXiv preprint arXiv:2007.08092, 2020.
Y. S. Patel and J. Bedi, “MAG-D: A Multivariate Attention and GRU Based Deep Learning Approach for Cloud Workload Forecasting,” Future Generation Computer Systems, vol. 142, pp. 376–392, 2023.
L. M. Weakley, A. A. Sim, and S. Canon, “Monitoring and Characterizing GPU Usage,” in Proc. Cray User Group (CUG 2023), Helsinki, Finland, 2023.
F. Shilpika, T. Fujiwara, N. Sakamoto, and H. Takemura, “A Visual Analytics Approach for Hardware System Monitoring with Streaming Functional Data Analysis,” arXiv preprint arXiv:2009.03488, 2020.
J. Huang, S. Zhou, G. Li, et al., “Real-Time Monitoring and Optimization Methods for User-Side Energy Management Based on Edge Computing,” Scientific Reports, vol. 15, article 24890, Nature Publishing Group, 2025.
M Divyaprabha Kabbal Narayana and Sudarshan Tekal Subramanyam Babu, “Optimal task partitioning to minimize failure in heterogeneous computational platform,” International Journal of Electrical and Computer Engineering, vol. 15, no. 1, pp. 1079–1088, Feb. 2025, doi: 10.11591/ijece.v15i1.pp1079-1088.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.