Invited paper: Secure swarm intelligence: A new approach to many-core power management

2017 ◽  
Author(s):  
Augusto Vega ◽  
Alper Buyuktosunoglu ◽  
Pradip Bose
Keyword(s):  
Swarm Intelligence ◽  
Power Management ◽  
New Approach ◽  
Many Core

Management Application - a New Approach to Control Many-Core Systems

2021 ◽  
Author(s):  
Angelo Elias Dalzotto ◽  
Marcelo Ruaro ◽  
Leonardo Vian Erthal ◽  
Fernando Gehm Moraes
Keyword(s):  
New Approach ◽  
Management Application ◽  
Many Core

3D Many-Core Microprocessor Power Management by Space-Time Multiplexing Based Demand-Supply Matching

2015 ◽  
Vol 64 (11) ◽  
pp. 3022-3036 ◽  
Author(s):  
Sai Manoj P. D. ◽  
Hao Yu ◽  
Kanwen Wang
Keyword(s):  
Power Management ◽  
Space Time ◽  
Many Core

Reliability-Aware Runtime Power Management for Many-Core Systems in the Dark Silicon Era

2017 ◽  
Vol 25 (2) ◽  
pp. 427-440 ◽  
Author(s):  
Amir M. Rahmani ◽  
Mohammad-Hashem Haghbayan ◽  
Antonio Miele ◽  
Pasi Liljeberg ◽  
Axel Jantsch ◽  
...  
Keyword(s):  
Power Management ◽  
Dark Silicon ◽  
Many Core

PoweRock: Power Modeling and Flexible Dynamic Power Management for Many-Core Architectures

2017 ◽  
Vol 11 (2) ◽  
pp. 600-612 ◽  
Author(s):  
Zhiquan Lai ◽  
King Tin Lam ◽  
Cho-Li Wang ◽  
Jinshu Su
Keyword(s):  
Power Management ◽  
Dynamic Power Management ◽  
Power Modeling ◽  
Dynamic Power ◽  
Many Core

scholarly journals Software-defined PMC for runtime power management of a many-core neuromorphic platform

2017 ◽  
Author(s):  
Indar Sugiarto ◽  
Delong Shang ◽  
Amit Kumar Singh ◽  
Bassem Ouni ◽  
Geoff Merrett ◽  
...  
Keyword(s):  
Power Management ◽  
Many Core

Parallelization and sustainability of distributed genetic algorithms on many-core processors

2014 ◽  
Vol 7 (1) ◽  
pp. 2-23 ◽  
Author(s):  
Yuji Sato ◽  
Mikiko Sato
Keyword(s):  
Graphics Processing Units ◽  
Fault Tolerant ◽  
Optimal Solution ◽  
New Approach ◽  
Content Type ◽  
Distributed Genetic Algorithms ◽  
Distributed Genetic Algorithm ◽  
Many Core ◽  
Graphics Processing ◽  
Application Programs

Purpose – The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units (GPUs) and multi-core processors (MCPs). Design/methodology/approach – For distributed genetic algorithm (GA) models, the paper proposes a method where an island's ID number is added to the header of data transferred by this island for use in fault detection. Findings – The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault, and that increasing the number of parallel threads makes the system less susceptible to faults. Originality/value – The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.

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