+DVFS is a technique used in modern processors to scale down both the voltage and
+the frequency of the CPU while computing, in order to reduce the energy
+consumption of the processor. DVFS is also allowed in GPUs to achieve the
+same goal. Reducing the frequency of a processor lowers its number of FLOPS and
+might degrade the performance of the application running on that processor,
+especially if it is compute bound. Therefore selecting the appropriate frequency
+for a processor to satisfy some objectives while taking into account all the
+constraints, is not a trivial operation. Many researchers used different
+strategies to tackle this problem. Some of them developed online methods that
+compute the new frequency while executing the application, such as
+~\cite{Hao_Learning.based.DVFS,Spiliopoulos_Green.governors.Adaptive.DVFS}. Others
+used offline methods that might need to run the application and profile it
+before selecting the new frequency, such as
+~\cite{Rountree_Bounding.energy.consumption.in.MPI,Cochran_Pack_and_Cap_Adaptive_DVFS}. The
+methods could be heuristics, exact or brute force methods that satisfy varied
+objectives such as energy reduction or performance. They also could be adapted
+to the execution's environment and the type of the application such as
+sequential, parallel or distributed architecture, homogeneous or heterogeneous
+platform, synchronous or asynchronous application, ...
+
+In this paper, we are interested in reducing energy for message passing iterative synchronous applications running over heterogeneous platforms.
+Some works have already been done for such platforms and they can be classified into two types of heterogeneous platforms:
+\begin{itemize}
+
+\item the platform is composed of homogeneous GPUs and homogeneous CPUs.
+\item the platform is only composed of heterogeneous CPUs.
+
+\end{itemize}
+
+For the first type of platform, the computing intensive parallel tasks are executed on the GPUs and the rest are executed
+on the CPUs. Luley et al.
+~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}, proposed a heterogeneous
+cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal was to maximize the
+energy efficiency of the platform during computation by maximizing the number of FLOPS per watt generated.
+In~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, Kai Ma et al. developed a scheduling
+algorithm that distributes workloads proportional to the computing power of the nodes which could be a GPU or a CPU. All the tasks must be completed at the same time.
+In~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Rong et al. showed that
+a heterogeneous (GPUs and CPUs) cluster that enables DVFS gave better energy and performance
+efficiency than other clusters only composed of CPUs.
+
+The work presented in this paper concerns the second type of platform, with heterogeneous CPUs.
+Many methods were conceived to reduce the energy consumption of this type of platform. Naveen et al.~\cite{Naveen_Power.Efficient.Resource.Scaling}
+developed a method that minimizes the value of $energy\cdot delay^2$ (the delay is the sum of slack times that happen during synchronous communications) by dynamically assigning new frequencies to the CPUs of the heterogeneous cluster. Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} proposed
+an algorithm that divides the executed tasks into two types: the critical and
+non critical tasks. The algorithm scales down the frequency of non critical tasks proportionally to their slack and communication times while limiting the performance degradation percentage to less than 10\%. In~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS}, they developed
+ a heterogeneous cluster composed of two types
+of Intel and AMD processors. They use a gradient method to predict the impact of DVFS operations on performance.
+In~\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks},
+ the best frequencies for a specified heterogeneous cluster are selected offline using some
+heuristic. Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements} used a greedy dynamic programming approach to
+minimize the power consumption of heterogeneous servers while respecting given time constraints. This approach
+had considerable overhead.
+In contrast to the above described papers, this paper presents the following contributions :
+\begin{enumerate}
+\item two new energy and performance models for message passing iterative synchronous applications running over
+ a heterogeneous platform. Both models take into account communication and slack times. The models can predict the required energy and the execution time of the application.
+
+\item a new online frequency selecting algorithm for heterogeneous platforms. The algorithm has a very small
+ overhead and does not need any training or profiling. It uses a new optimization function which simultaneously maximizes the performance and minimizes the energy consumption of a message passing iterative synchronous application.
+
+\end{enumerate}
