Methodology for the Analysis of the Operation of High-Performance Computing Equipment

Abstract:

High-performance computing equipment uses graphic processors, known as GPUs, as a mechanism for high computational capacity. To increase the computational capacity, additional graphics processors are added in the configurations. This strategy increases the computational capacity as well as considerably increases the temperature generated by the entire system, normally the GPUs have cooling systems, which help to cool as much as they are required, the operating systems have automatic mechanisms to access the GPUs, these functions are activated when it is required to use them, in the event of a need for high computing capacity requirement. In this work we demonstrate a method to analyze the operation of computing equipment when working on high-performance functions, such as video games, which require high computational capacity to be able to play in real time and perform the tasks. Necessary renderings, the method can be applied to other applications that are necessary to ensure the continued operation of the computing equipment.

• Periodical: Advances in Science and Technology (Volume 151)
• Pages: 85-92
• DOI: https://doi.org/10.4028/p-Nd9TOT
• Online since: June 2024
• Authors: Wilver Auccahuasi*, Kitty Urbano, Edwin Flores, Oscar Linares, Christian Ovalle, Medalith Campos -Sobrino, Humberto Quispe-Peña, Julia Sobrino-Mesias, Nicanor Benites, Juan Marin-Huapaya, Rosalynn Ornella Flores-Castañeda, Sandro Olaya-Cotera
• Keywords: Computer Equipment, High Performance, Operation, Temperature, Thermal Image

References

[1] Scott, S.; Chen, W.-Y.; Heifetz, A. Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals. Sensors 2023, 23, 8462

DOI: 10.3390/s23208462

[2] Zhu, D.; Li, J.; Wang, F.; Gong, X.; Cong, W.; Wang, P.; Liu, Y. A Method for Extracting Contours of Building Facade Hollowing Defects Using Polarization Thermal Images Based on Improved Canny Algorithm. Buildings 2023, 13, 2563

DOI: 10.3390/buildings13102563

[3] Wang, F.; Wang, Z.; Chen, Z.; Zhu, D.; Gong, X.; Cong, W. An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm. Appl. Sci. 2023, 13, 11031

DOI: 10.3390/app131911031

[4] Biswanath, M.K.; Hoegner, L.; Stilla, U. Thermal Mapping from Point Clouds to 3D Building Model Facades. Remote Sens. 2023, 15, 4830

DOI: 10.3390/rs15194830

[5] Korolev, S.; Urmanov, I.; Sorokin, A.; Girina, O. Detecting Volcano Thermal Activity in Night Images Using Machine Learning and Computer Vision. Remote Sens. 2023, 15, 4815

DOI: 10.3390/rs15194815

[6] Liu, J.; Zhou, X.; Wan, Z.; Yang, X.; He, W.; He, R.; Lin, Y. Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE. Sensors 2023, 23, 8101

DOI: 10.3390/s23198101

[7] Li, S.; Wang, G.; Zhang, H.; Zou, Y. SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion. Remote Sens. 2023, 15, 4467

DOI: 10.3390/rs15184467

[8] Maśko, M.; Borowska, M.; Sikorska, U.; Ciesielska, A.; Zdrojkowski, Ł.; Domino, M. Quantification of the Area of the Highest Temperature in Equine Infrared Images. Appl. Sci. 2023, 13, 11006

DOI: 10.3390/app131911006

[9] Zhao, K.; Duan, Y.; Chen, J.; Li, Q.; Hong, X.; Zhang, R.; Wang, M. Detection of Respiratory Rate of Dairy Cows Based on Infrared Thermography and Deep Learning. Agriculture 2023, 13, 1939

DOI: 10.3390/agriculture13101939

[10] A Silva, W.C.d.; Silva, J.A.R.d.; Silva, É.B.R.d.; Barbosa, A.V.C.; Sousa, C.E.L.; Carvalho, K.C.d.; Santos, M.R.P.d.; Neves, K.A.L.; Martorano, L.G.; Camargo Júnior, R.N.C.; et al. Characterization of Thermal Patterns Using Infrared Thermography and Thermolytic Responses of Cattle Reared in Three Different Systems during the Transition Period in the Eastern Amazon, Brazil. Animals 2023, 13, 2735

DOI: 10.3390/ani13172735

[11] De Meneck, F.; Santana, V.; Brioschi, G.C.; Haddad, D.S.; Neves, E.B.; Franco, M.d.C.; Brioschi, M.L. Infrared Imaging of the Brain-Eyelid Thermal Tunnel: A Promising Method for Measuring Body Temperature in Afebrile Children. Int. J. Environ. Res. Public Health 2023, 20, 6867

DOI: 10.3390/ijerph20196867

[12] Angrisani, L.; De Benedetto, E.; Duraccio, L.; Lo Regio, F.; Ruggiero, R.; Tedesco, A. Infrared Thermography for Real-Time Assessment of the Effectiveness of Scoliosis Braces. Sensors 2023, 23, 8037

DOI: 10.3390/s23198037

[13] Li, L.; Tang, W.; Yang, H.; Xue, C. Classification of User Emotional Experiences on B2C Websites Utilizing Infrared Thermal Imaging. Sensors 2023, 23, 7991

DOI: 10.3390/s23187991

[14] Drahanský, M.; Charvát, M.; Macek, I.; Mohelníková, J. Thermal Imaging Detection System: A Case Study for Indoor Environments. Sensors 2023, 23, 7822

DOI: 10.3390/s23187822

[15] Ovalle, C., Auccahuasi, W., Meza, S., Rojas, K., Cosme, M., Inciso-Rojas, M., ... & Auccahuasi, A. (2023). Muscle temperature analysis, using thermal imaging, applied to the treatment of muscle recovery. Procedia Computer Science, 218, 1247-1256.

DOI: 10.1016/j.procs.2023.01.103