Section~\ref{sec.expe} presents the results of applying the algorithm on the NAS parallel benchmarks and executing them
on a heterogeneous platform. It also shows the results of running three
different power scenarios and comparing them.
- Finally, we conclude in Section~\ref{sec.concl} with a summary and some future works.
+ Finally, in Section~\ref{sec.concl} the paper is ended with a summary and some future works.
\section{Related works}
\label{sec.relwork}
- Energy reduction process for high performance clusters recently performed using
- dynamic voltage and frequency scaling (DVFS) technique. DVFS is a technique enabled
- in modern processors to scaled down both of the voltage and the frequency of
- the CPU while it is in the computing mode to reduce the energy consumption. DVFS is
- also allowed in the graphical processors GPUs, to achieved the same goal. Applying
- DVFS has a dramatical side effect if it is applied to minimum levels to gain more
- energy reduction, producing a high percentage of performance degradations for the
- parallel applications. Many researchers used different strategies to solve this
- nonlinear problem for example in
- ~\cite{Hao_Learning.based.DVFS,Dhiman_Online.Learning.Power.Management}, their methods
- add big overheads to the algorithm to select the suitable frequency.
- This paper presents a method
- to find the optimal set of frequencies for heterogeneous cluster to
- simultaneously optimize both the energy and the execution time without adding big
- overhead. This work is developed from our previous work of homogeneous cluster~\cite{Our_first_paper}.
- Therefore we are interested to present some works that concerned the heterogeneous clusters
- enabled DVFS. In general, the heterogeneous cluster works fall into two categorizes:
- GPUs-CPUs heterogeneous clusters and CPUs-CPUs heterogeneous clusters. In GPUs-CPUs
- heterogeneous clusters some parallel tasks executed on GPUs and the others executed
- on CPUs. As an example of these works, Luley et al.
+ DVFS is a technique enabled
+ 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 the 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 and while taking into account all the constraints, is not a trivial operation. Many researchers used different strategies to tackle this problem. Some of them used online methods that compute the new frequency while executing the application \textbf{add a reference for an online method here}. Others used offline methods that might need to run the application and profile it before selecting the new frequency \textbf{add a reference for an offline method}. 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 it 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 compute 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 is to determined the
- energy efficiency as a function of performance per watt, the best tradeoff is done when the
- performance per watt function is maximized. In the work of Kia Ma et al.
- ~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, they developed a scheduling
- algorithm to distributed different workloads proportional to the computing power of the node
- to be executed on CPU or GPU, emphasize all tasks must be finished in the same time.
- Recently, Rong et al.~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Their study explain that
- a heterogeneous clusters enabled DVFS using GPUs and CPUs gave better energy and performance
- efficiency than other clusters composed of only CPUs.
- The CPUs-CPUs heterogeneous clusters consist of number of computing nodes all of the type CPU.
- Our work in this paper can be classified to this type of the clusters.
- As an example of these works see Naveen et al.~\cite{Naveen_Power.Efficient.Resource.Scaling} work,
- They developed a policy to dynamically assigned the frequency to a heterogeneous cluster.
- The goal is to minimizing a fixed metric of $energy*delay^2$. Where our proposed method is automatically
- optimized the relation between the energy and the delay of the iterative applications.
- Other works such as Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling},
- their algorithm divided the executed tasks into two types: the critical and
- non critical tasks. The algorithm scaled down the frequency of the non critical tasks
- as function to the amount of the slack and communication times that
- have with maximum of performance degradation percentage less than 10\%. In our method there is no
- fixed bounds for performance degradation percentage and the bound is dynamically computed
- according to the energy and the performance tradeoff relation of the executed application.
- There are some approaches used a heterogeneous cluster composed from two different types
- of Intel and AMD processors such as~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS}
- and \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}, they predicated both the energy
- and the performance for each frequency gear, then the algorithm selected the best gear that gave
- the best tradeoff. In contrast our algorithm works over a heterogeneous platform composed of
- four different types of processors. Others approaches such as
- \cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks},
- they are selected the best frequencies for a specified heterogeneous clusters offline using some
- heuristic methods. While our proposed algorithm works online during the execution time of
- iterative application. Greedy dynamic approach used by Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements},
- minimized the power consumption of a heterogeneous severs with time/space complexity, this approach
- had considerable overhead. In our proposed frequency selecting algorithm has very small overhead and
- it is works without any previous analysis for the application time complexity. The primary
- contributions of our paper are :
+ 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 minimize the value of $energy*delay^2$ by dynamically assigning new frequencies to the CPUs of the heterogeneous cluster. \textbf{should define the delay} Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} propose
+ 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}
+ and \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}, a heterogeneous cluster composed of two types
+ of Intel and AMD processors. The consumed energy
+ and the performance for each frequency gear were predicted, then the algorithm selected the best gear that gave
+ the best tradeoff. \textbf{what energy model they used? what method they used? }
+ 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 approach to
+ minimize the power consumption of heterogeneous severs with time/space complexity \textbf{what does it mean}. This approach
+ had considerable overhead.
+ In contrast to the above described papers, this paper presents the following contributions :
\begin{enumerate}
- \item It is presents a new online frequency selecting algorithm which has very small
- overhead and not need for any training and profiling.
- \item It is develops a new energy model for iterative distributed applications running over
- a heterogeneous clusters, taking into account the communication and slack times.
- \item The proposed frequency selecting algorithm predicts both the energy and the execution time
- of the iterative application running over heterogeneous platform.
- \item It demonstrates a new optimization function which maximize the performance and
- minimize the energy consumption simultaneously.
+ \item two new energy and performance models for message passing iterative synchronous applications running over
+ a heterogeneous platform. Both models takes into account the 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 for 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}
In this paper, we are interested in reducing the energy consumption of message
passing distributed iterative synchronous applications running over
- heterogeneous platforms. We define a heterogeneous platform as a collection of
+ heterogeneous platforms. A heterogeneous platform is defined as a collection of
heterogeneous computing nodes interconnected via a high speed homogeneous
network. Therefore, each node has different characteristics such as computing
power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all
iteration and $MinTcm$ is the communication time of the slowest processor from
the first iteration. The model computes the maximum computation time
with scaling factor from each node added to the communication time of the \subsection{The verifications of the proposed method}
- \label{sec.verif}
+ \label{sec.verif.method}
The precision of the proposed algorithm mainly depends on the execution time prediction model defined in
EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}).
The energy model is also significantly dependent on the execution time model because the static energy is
of iterations and $N$ is the number of computing nodes. The algorithm needs from 12 to 20 iterations to select the best
vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}).
slowest node, it means only the communication time without any slack time.
- Therefore, we can consider the execution time of the iterative application is
+ Therefore, the execution time of the iterative application is
equal to the execution time of one iteration as in EQ(\ref{eq:perf}) multiplied
by the number of iterations of that application.
- This prediction model is developed from our model for predicting the execution time of
+ This prediction model is developed from the model for predicting the execution time of
message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}.
- The execution time prediction model is used in our method for optimizing both
+ The execution time prediction model is used in the method for optimizing both
energy consumption and performance of iterative methods, which is presented in the
following sections.
process of the frequency can be expressed by the scaling factor $S$ which is the
ratio between the maximum and the new frequency as in EQ(\ref{eq:s}).
The CPU governors are power schemes supplied by the operating
- system's kernel to lower a core's frequency. we can calculate the new frequency
- $F_{new}$ from EQ(\ref{eq:s}) as follow:
+ system's kernel to lower a core's frequency. The new frequency
+ $F_{new}$ from EQ(\ref{eq:s}) can be calculated as follows:
\begin{equation}
\label{eq:fnew}
F_\textit{new} = S^{-1} \cdot F_\textit{max}
\end{equation}
The static power is related to the power leakage of the CPU and is consumed during computation
and even when idle. As in~\cite{Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling},
- we assume that the static power of a processor is constant
+ the static power of a processor is considered as constant
during idle and computation periods, and for all its available frequencies.
The static energy is the static power multiplied by the execution time of the program.
According to the execution time model in EQ(\ref{eq:perf}), the execution time of the program
Reducing the frequencies of the processors according to the vector of
scaling factors $(S_1, S_2,\dots, S_N)$ may degrade the performance of the
application and thus, increase the static energy because the execution time is
- increased~\cite{Kim_Leakage.Current.Moore.Law}. We can measure the overall energy consumption for the iterative
- application by measuring the energy consumption for one iteration as in EQ(\ref{eq:energy})
+ increased~\cite{Kim_Leakage.Current.Moore.Law}. The overall energy consumption for the iterative
+ application can be measured by measuring the energy consumption for one iteration as in EQ(\ref{eq:energy})
multiplied by the number of iterations of that application.
complex and nonlinear, Thus, unlike the relation between the execution time
and the scaling factor, the relation of the energy with the frequency scaling
factors is nonlinear, for more details refer to~\cite{Freeh_Exploring.the.Energy.Time.Tradeoff}.
- Moreover, they are not measured using the same metric. To solve this problem, we normalize the
- execution time by computing the ratio between the new execution time (after
+ Moreover, they are not measured using the same metric. To solve this problem, the
+ execution time is normalized by computing the ratio between the new execution time (after
scaling down the frequencies of some processors) and the initial one (with maximum
frequency for all nodes,) as follows:
\begin{multline}
\end{multline}
- In the same way, we normalize the energy by computing the ratio between the consumed energy
+ In the same way, the energy is normalized by computing the ratio between the consumed energy
while scaling down the frequency and the consumed energy with maximum frequency for all nodes:
\begin{multline}
\label{eq:enorm}
- Our solution for this problem is to make the optimization process for energy and
- execution time follow the same direction. Therefore, we inverse the equation of the
- normalized execution time which gives the normalized performance equation, as follows:
+ This problem can be solved by making the optimization process for energy and
+ execution time follow the same direction. Therefore, the equation of the
+ normalized execution time is inverted which gives the normalized performance equation, as follows:
\begin{multline}
\label{eq:pnorm_inv}
P_\textit{Norm} = \frac{T_\textit{Old}}{T_\textit{New}}\\
\caption{The energy and performance relation}
\end{figure}
- Then, we can model our objective function as finding the maximum distance
+ Then, the objective function can be modeled as finding the maximum distance
between the energy curve EQ~(\ref{eq:enorm}) and the performance
curve EQ~(\ref{eq:pnorm_inv}) over all available sets of scaling factors. This
represents the minimum energy consumption with minimum execution time (maximum
- performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then our objective
+ performance) at the same time, see figure~(\ref{fig:r1}) or figure~(\ref{fig:r2}). Then the objective
function has the following form:
\begin{equation}
\label{eq:max}
\overbrace{E_\textit{Norm}(S_{ij})}^{\text{Minimize}} )
\end{equation}
where $N$ is the number of nodes and $F$ is the number of available frequencies for each nodes.
- Then we can select the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}).
- Our objective function can work with any energy model or any power values for each node
+ Then, the optimal set of scaling factors that satisfies EQ~(\ref{eq:max}) can be selected.
+ The objective function can work with any energy model or any power values for each node
(static and dynamic powers). However, the most energy reduction gain can be achieved when
the energy curve has a convex form as shown in~\cite{Zhuo_Energy.efficient.Dynamic.Task.Scheduling,Rauber_Analytical.Modeling.for.Energy,Hao_Learning.based.DVFS}.
\label{sec.optim}
\subsection{The algorithm details}
- In this section we propose algorithm~(\ref{HSA}) which selects the frequency scaling factors
+ In this section algorithm~(\ref{HSA}) is presented. It selects the frequency scaling factors
vector that gives the best trade-off between minimizing the energy consumption and maximizing
the performance of a message passing synchronous iterative application executed on a heterogeneous
platform. It works online during the execution time of the iterative message passing program.
passing iterative synchronous applications, fast nodes have to wait for the slower ones to finish their
computations before being able to synchronously communicate with them as in figure (\ref{fig:heter}).
These periods are called idle or slack times.
- Our algorithm takes into account this problem and tries to reduce these slack times when selecting the
+ The algorithm takes into account this problem and tries to reduce these slack times when selecting the
frequency scaling factors vector. At first, it selects initial frequency scaling factors that increase
the execution times of fast nodes and minimize the differences between the computation times of
fast and slow nodes. The value of the initial frequency scaling factor for each node is inversely
\EndWhile
\State Return $Sopt_1,Sopt_2,\dots,Sopt_N$
\end{algorithmic}
- \caption{Heterogeneous scaling algorithm}
+ \caption{frequency scaling factors selection algorithm}
\label{HSA}
\end{algorithm}
\end{algorithm}
\subsection{The verifications of the proposed algorithm}
- \label{sec.verif}
+ \label{sec.verif.algo}
The precision of the proposed algorithm mainly depends on the execution time prediction model defined in
EQ(\ref{eq:perf}) and the energy model computed by EQ(\ref{eq:energy}).
The energy model is also significantly dependent on the execution time model because the static energy is
\section{Conclusion}
\label{sec.concl}
- In this paper, we have presented a new online selecting frequency scaling factors algorithm
- that selects the best possible vector of frequency scaling factors for a heterogeneous platform.
- This vector gives the maximum distance (optimal tradeoff) between the predicted energy and
- the predicted performance curves. In addition, we developed a new energy model for measuring
+ In this paper, a new online frequency selecting algorithm have been presented. It selects the best possible vector of frequency scaling factors that gives the maximum distance (optimal tradeoff) between the predicted energy and
+ the predicted performance curves for a heterogeneous platform. This algorithm uses a new energy model for measuring
and predicting the energy of distributed iterative applications running over heterogeneous
- cluster. The proposed method evaluated on Simgrid/SMPI simulator to built a heterogeneous
- platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
- the proposed algorithm to changes its behaviour to selects different scaling factors when
- the number of computing nodes and both of the static and the dynamic powers are changed.
-
- In the future, we plan to improve this method to apply on asynchronous iterative applications
- where each task does not wait the others tasks to finish there works. This leads us to develop a new
- energy model to an asynchronous iterative applications, where the number of iterations is not
+ platform. To evaluate the proposed method, it was applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also selects different scaling factors according to the percentage of the computing and communication times, and according to the values of the static and dynamic powers of the CPUs.
+
+ In the near future, this method will be applied to real heterogeneous platforms to evaluate its performance in a real study case. It would also be interesting to evaluate its scalability over large scale heterogeneous platform and measure the energy consumption reduction it can produce. Afterward, We would like to develop a similar method that is adapted to asynchronous iterative applications
+ where each task does not wait for others tasks to finish there works. The development of such method might require a new
+ energy model because the number of iterations is not
known in advance and depends on the global convergence of the iterative system.
\section*{Acknowledgment}
