DocumentCode :
701750
Title :
Energy efficient rescheduling algorithm for High Performance Computing
Author :
Chauhan, Manisha ; Parveen, Nazia ; Saurav, Sumit Kumar ; Ganga Prasad, G.L.
Author_Institution :
Opp.HAL Aeroengine Div., Centre for Dev. of Adv. Comput., Bangalore, India
fYear :
2015
fDate :
19-20 Feb. 2015
Firstpage :
1
Lastpage :
7
Abstract :
With the augmentation of High Performance Computing (HPC) system and power consumption pattern, energy optimization has become an important concern. Several surveys indicate that the energy utilized in computation and communication within a HPC system contributes considerably to their operational costs. The proposed energy efficient rescheduling algorithm aims to reduce energy consumption in HPC. This algorithm is based on energy efficient rescheduling of a job which considers the optimal operating point (OOP) i.e. voltage and frequency along with resource matching constraint to achieve the target performance. Performance -Energy-Time (PET) matrix for every application is built and stored in knowledge base (KB) in order to devise its OOP. The devised OOP gives minimal energy consumption at target execution environment. The algorithm is useful for energy optimization for cluster environment. Earlier works exploited process migration technique to address load balancing aspect. The proposed algorithm exploits process migration and reschedules the job dynamically with its OOP in context of energy reduction.
Keywords :
knowledge based systems; parallel processing; power aware computing; resource allocation; HPC system; KB; OOP; PET matrix; cluster environment; dynamic job rescheduling; energy consumption reduction; energy efficient job rescheduling algorithm; energy optimization; high-performance computing; knowledge base; load balancing; minimal energy consumption; operational costs; optimal operating point; performance-energy-time matrix; power consumption pattern; process migration technique; resource matching constraint; target execution environment; target performance; Algorithm design and analysis; Clustering algorithms; Energy consumption; Energy efficiency; Optimization; Positron emission tomography; Power demand; Energy Efficient Rescheduling Algorithm; Energy Optimization; High Performance Computing; Knowledge Base; Process Migration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel Computing Technologies (PARCOMPTECH), 2015 National Conference on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4799-6916-6
Type :
conf
DOI :
10.1109/PARCOMPTECH.2015.7084521
Filename :
7084521
Link To Document :
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