+The need and demand for more computing power have been increasing since the
+birth of the first computing unit and it is not expected to slow down in the
+coming years. To meet these demands, at first the frequency of the CPU was regularly increased until reaching the thermal limit. Also, researchers and supercomputers
+constructors have been regularly increasing the number of computing cores and
+processors in supercomputers. Many parallel and distributed architectures, such as multi-core, clusters and grids, were implemented in order to obtain more computing power. This approach consists in using at the same time many computing nodes to solve a big problem that cannot be solved on a single node.
+Therefore, these two common approaches are the most common up to now to get more computing power, but they are increasing the energy consumption of the resulting computing architecture.
+Indeed, the power consumed by a processor exponentially increases when its frequency increases and a platform consisting of $N$ computing nodes consumes as much as the sum of the power consumed by each computing node.
+As an example, the Chinese supercomputer
+Tianhe-2 had the highest FLOPS in November 2015 according to the Top500 list
+\cite{ref101}. However, it was also the most power hungry
+platform with its over 3 million cores consuming around 17.8 megawatts.
+Moreover, according to the U.S. annual energy outlook 2015
+\cite{ref102}, the price of energy for 1 megawatt per hour
+was approximately equal to \$70. Therefore, the price of the energy consumed by
+the Tianhe-2 platform is approximately more than \$10 million each year.
+Moreover, the heat generated by the platform and therefore a cooling infrastructure \cite{ref103} which also consumes a lot of energy, must be implemented to keep the platform from overheating. High CPU's temperatures can also drastically increase its energy consumption, see \cite{ref104} for more details.
+The computing platforms must be more energy efficient and offer the highest number
+of FLOPS per watt possible, such as the Shoubu-ExaScaler from RIKEN
+which became the top of the Green500 list in November 2015 \cite{ref105}.
+This heterogeneous platform executes more than 7 GFlops per watt while consuming
+50.32 kilowatts.
+
+For all these reasons energy reduction has become an important topic in the high performance
+computing (HPC) field. To tackle this problem, many researchers use DVFS (Dynamic
+Voltage and Frequency Scaling) operations which reduce dynamically the frequency and
+voltage of cores and thus their energy consumption \cite{ref49}.
+Indeed, modern CPUs offer a set of acceptable frequencies which are usually called gears, and the user or
+the operating system can modify the frequency of the processor according to its
+needs. However, DVFS reduces the number of FLOPS executed by the processor which may increase the execution
+time of the application running over that processor.
+Therefore researchers try to reduce the frequency to the minimum when processors are idle
+(waiting for data from other processors or communicating with other processors).
+Moreover, depending on their objectives, they use heuristics to find the best
+frequency scaling factor during the computation. If they aim for performance they choose
+the best frequency scaling factor that reduces the consumed energy while affecting as
+little as possible the performance. On the other hand, if they aim for energy
+reduction, the chosen frequency scaling factor must produce the most energy efficient
+execution without considering the degradation of the performance. Whereas, it is
+important to notice that lowering the frequency to the minimum value does not always
+give the most energy efficient execution due to energy leakage that increases the total energy consumption of the CPU when the execution time increases. However, the more important question is how to select the best frequency gears that minimizes the total energy consumption and the maximizes the performance of parallel application running over a parallel platform at the same time?
+
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