Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing
Anabi Hilary Kelechi,
Mohammed H. Alsharif,
Okpe Jonah Bameyi,
Paul Joan Ezra,
Iorshase Kator Joseph,
Aaron-Anthony Atayero,
Zong Woo Geem,
Junhee Hong
Affiliations
Anabi Hilary Kelechi
Department of Electrical Engineering and Information Engineering, College of Engineering, Covenant University, Canaanland, Ota P.M.B 1023, Ogun State 110125, Nigeria
Mohammed H. Alsharif
Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
Okpe Jonah Bameyi
Department of Electrical Engineering and Information Engineering, College of Engineering, Covenant University, Canaanland, Ota P.M.B 1023, Ogun State 110125, Nigeria
Paul Joan Ezra
Department of Electrical Engineering and Information Engineering, College of Engineering, Covenant University, Canaanland, Ota P.M.B 1023, Ogun State 110125, Nigeria
Iorshase Kator Joseph
Department of Electrical Engineering and Information Engineering, College of Engineering, Covenant University, Canaanland, Ota P.M.B 1023, Ogun State 110125, Nigeria
Aaron-Anthony Atayero
Department of Electrical Engineering and Information Engineering, College of Engineering, Covenant University, Canaanland, Ota P.M.B 1023, Ogun State 110125, Nigeria
Zong Woo Geem
Department of Energy IT, Gachon University, Seongnam 13120, Korea
Junhee Hong
Department of Energy IT, Gachon University, Seongnam 13120, Korea
Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.
