TY - JOUR
T1 - Artificial intelligence
T2 - An energy efficiency tool for enhanced high performance computing
AU - Kelechi, Anabi Hilary
AU - Alsharif, Mohammed H.
AU - Bameyi, Okpe Jonah
AU - Ezra, Paul Joan
AU - Joseph, Iorshase Kator
AU - Atayero, Aaron Anthony
AU - Geem, Zong Woo
AU - Hong, Junhee
N1 - Funding Information:
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20172010000190).
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - 5G
KW - Artificial intelligence (AI)
KW - Big data
KW - Energy efficiency (EE)
KW - High performance computing (HPC)
KW - Internet of things (IoT)
KW - Machine learning (ML)
UR - http://www.scopus.com/inward/record.url?scp=85087510582&partnerID=8YFLogxK
U2 - 10.3390/SYM12061029
DO - 10.3390/SYM12061029
M3 - Review article
AN - SCOPUS:85087510582
SN - 2073-8994
VL - 12
JO - Symmetry
JF - Symmetry
IS - 6
M1 - 1029
ER -
