adding the some corrections

[mpi-energy2.git] / Heter_paper.tex

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 3ba32046ce6d91f8eefba744bb466a3c787c05e9..2fc854959ff4a138331b9011b0833c60010fe89b 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -81,14 +81,14 @@
 
 \section{Introduction}
 \label{sec.intro}
 
 \section{Introduction}
 \label{sec.intro}

-Modern processors continue increasing in a performance. 
-The CPUs constructors are competing to achieve maximum number 
+Modern processors continue increasing in performance, 
+the CPUs constructors are competing to achieve maximum number 

 of floating point operations per second (FLOPS). 
 Thus, the energy consumption and the heat dissipation are increased 
 drastically according to this increase. Because the number of FLOPS 
 of floating point operations per second (FLOPS). 
 Thus, the energy consumption and the heat dissipation are increased 
 drastically according to this increase. Because the number of FLOPS 

-is linearly related to the power consumption of a CPU
+is more related to the power consumption of a CPU

 ~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}.  
 ~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}.  

-As an example of the more power hungry cluster, Tianhe-2 became in 
+As an example of the most power hungry cluster, Tianhe-2 became in 

 the top of the Top500 list in June 2014 \cite{TOP500_Supercomputers_Sites}. 
 It has more than 3 millions of cores and consumed more than 17.8 megawatts. 
 Moreover, according to the U.S. annual energy outlook 2014 
 the top of the Top500 list in June 2014 \cite{TOP500_Supercomputers_Sites}. 
 It has more than 3 millions of cores and consumed more than 17.8 megawatts. 
 Moreover, according to the U.S. annual energy outlook 2014 


@@ -101,11 +101,11 @@ energy efficiency due to the increase in the energy cost and the environment
 influences. Therefore, a green computing clusters with maximum number of 
 FLOPS per watt are required nowadays. For example, the GSIC center of Tokyo, 
 became the top of the Green500 list in June 2014 \cite{Green500_List}. 
 influences. Therefore, a green computing clusters with maximum number of 
 FLOPS per watt are required nowadays. For example, the GSIC center of Tokyo, 
 became the top of the Green500 list in June 2014 \cite{Green500_List}. 

-This platform has more than four thousand of  MFLOPS per watt. Dynamic 
+This heterogeneous platform has more than four thousand of  MFLOPS per watt. Dynamic 

 voltage and frequency scaling (DVFS) is a process used widely to reduce the energy 
 voltage and frequency scaling (DVFS) is a process used widely to reduce the energy 

-consumption of the processor. In a heterogeneous clusters enabled DVFS, many researchers 
+consumption of the processor. In heterogeneous clusters enabled DVFS, many researchers 

 used DVFS  in a different ways. DVFS can be minimized the energy consumption 
 used DVFS  in a different ways. DVFS can be minimized the energy consumption 

-but it leads to a disadvantage due to increase in performance degradation. 
+but it leads to a disadvantage due to the increase in performance degradation. 

 Therefore,  researchers used different optimization strategies to overcame 
 this problem. The best tradeoff relation between the energy reduction and 
 performance degradation ratio is became a key challenges in a heterogeneous 
 Therefore,  researchers used different optimization strategies to overcame 
 this problem. The best tradeoff relation between the energy reduction and 
 performance degradation ratio is became a key challenges in a heterogeneous 


@@ -129,26 +129,26 @@ Finally, we conclude in Section~\ref{sec.concl} with a summary and some future w
 
 \section{Related works}
 \label{sec.relwork}
 
 \section{Related works}
 \label{sec.relwork}

-Energy reduction process for a high performance clusters recently performed using 
+Energy reduction process for high performance clusters recently performed using 

 dynamic voltage and frequency scaling (DVFS) technique. DVFS is a technique enabled 
 dynamic voltage and frequency scaling (DVFS) technique. DVFS is a technique enabled 

-in a modern processors to scaled down both of the  voltage and the frequency of 
+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 
 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 
+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.  
 In this paper we  present a method 
 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.  
 In this paper we  present a method 

-to find the optimal set of frequency scaling factors for a heterogeneous cluster to 
-simultaneously optimize both the energy and the execution time  without adding a big 
-overhead. This work is developed from our previous work of a homogeneous cluster~\cite{Our_first_paper}. 
+to find the optimal set of frequency scaling factors 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 
 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 a GPUs and the others executed 
-on a CPUs. As an example of this works, Luley et al.
+heterogeneous clusters some parallel tasks executed on  GPUs and the others executed 
+on  CPUs. As an example of this works, 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 
 ~\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 


@@ -161,7 +161,7 @@ a heterogeneous clusters enabled DVFS using GPUs and CPUs gave better energy and
 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. 
 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 this works see  Naveen et al.~\cite{Naveen_Power.Efficient.Resource.Scaling} work, 
+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. 
 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. 


@@ -169,7 +169,7 @@ Other works such as Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduli
 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 
 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 of 10\%. In our method there is no 
+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 
 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 


@@ -184,7 +184,19 @@ heuristic methods. While our proposed algorithm works online during the executio
 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 scaling algorithm has very small overhead and 
 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 scaling algorithm has very small overhead and 

-it is works without any previous analysis for the application time complexity. 
+it is works without any previous analysis for the application time complexity. The primary 
+contributions of our paper are :
+\begin{enumerate}
+\item It is presents  a new online heterogeneous scaling 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 scaling algorithm predicts both the energy and the execution time 
+      of the iterative application.
+\item It demonstrates a new optimization function which maximize the performance and 
+      minimize the energy consumption simultaneously.
+      
+\end{enumerate}

 
 \section{The performance and energy consumption measurements on heterogeneous architecture}
 \label{sec.exe}
 
 \section{The performance and energy consumption measurements on heterogeneous architecture}
 \label{sec.exe}


@@ -204,12 +216,12 @@ network. Therefore, each node has different characteristics such as computing
 power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all
 have the same network bandwidth and latency.
 
 power (FLOPS), energy consumption, CPU's frequency range, \dots{} but they all
 have the same network bandwidth and latency.
 

-The  overall execution time  of a distributed iterative synchronous application 
+The overall execution time  of a distributed iterative synchronous application 

 over a heterogeneous platform  consists of the sum of the computation time and 
 the communication time for every iteration on a node. However, due to the 
 heterogeneous computation power of the computing nodes, slack times might occur 
 when fast nodes have to  wait, during synchronous communications, for  the slower 
 over a heterogeneous platform  consists of the sum of the computation time and 
 the communication time for every iteration on a node. However, due to the 
 heterogeneous computation power of the computing nodes, slack times might occur 
 when fast nodes have to  wait, during synchronous communications, for  the slower 

-nodes to finish  their computations (see Figure~(\ref{fig:heter}). 
+nodes to finish  their computations (see Figure~(\ref{fig:heter})). 

 Therefore,  the overall execution time  of the program is the execution time of the slowest
 task which have the highest computation time and no slack time.
   
 Therefore,  the overall execution time  of the program is the execution time of the slowest
 task which have the highest computation time and no slack time.
   


@@ -267,7 +279,7 @@ Therefore, we can consider 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.
 
 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 based on our model for predicting the execution time of 
+This prediction model is developed from our 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 
 energy consumption and performance of iterative methods, which is presented in the 
 message passing distributed applications for homogeneous architectures~\cite{Our_first_paper}. 
 The execution time prediction model is used in our method for optimizing both 
 energy consumption and performance of iterative methods, which is presented in the 


@@ -280,17 +292,17 @@ Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Schedul
 Rizvandi_Some.Observations.on.Optimal.Frequency} divide the power consumed by a processor into
 two power metrics: the static and the dynamic power.  While the first one is
 consumed as long as the computing unit is turned on, the latter is only consumed during
 Rizvandi_Some.Observations.on.Optimal.Frequency} divide the power consumed by a processor into
 two power metrics: the static and the dynamic power.  While the first one is
 consumed as long as the computing unit is turned on, the latter is only consumed during

-computation times.  The dynamic power $P_{d}$ is related to the switching
+computation times.  The dynamic power $Pd$ is related to the switching

 activity $\alpha$, load capacitance $C_L$, the supply voltage $V$ and
 operational frequency $F$, as shown in EQ(\ref{eq:pd}).
 \begin{equation}
   \label{eq:pd}
 activity $\alpha$, load capacitance $C_L$, the supply voltage $V$ and
 operational frequency $F$, as shown in EQ(\ref{eq:pd}).
 \begin{equation}
   \label{eq:pd}

-  P_\textit{d} = \alpha \cdot C_L \cdot V^2 \cdot F
+  Pd = \alpha \cdot C_L \cdot V^2 \cdot F

 \end{equation}
 \end{equation}

-The static power $P_{s}$ captures the leakage power as follows:
+The static power $Ps$ captures the leakage power as follows:

 \begin{equation}
   \label{eq:ps}
 \begin{equation}
   \label{eq:ps}

-   P_\textit{s}  = V \cdot N_{trans} \cdot K_{design} \cdot I_{leak}
+   Ps  = V \cdot N_{trans} \cdot K_{design} \cdot I_{leak}

 \end{equation}
 where V is the supply voltage, $N_{trans}$ is the number of transistors,
 $K_{design}$ is a design dependent parameter and $I_{leak}$ is a
 \end{equation}
 where V is the supply voltage, $N_{trans}$ is the number of transistors,
 $K_{design}$ is a design dependent parameter and $I_{leak}$ is a


@@ -298,10 +310,10 @@ technology-dependent parameter.  The energy consumed by an individual processor
 to execute a given program can be computed as:
 \begin{equation}
   \label{eq:eind}
 to execute a given program can be computed as:
 \begin{equation}
   \label{eq:eind}

-   E_\textit{ind} =  P_\textit{d} \cdot Tcp + P_\textit{s} \cdot T
+   E_\textit{ind} =  Pd \cdot Tcp + Ps \cdot T

 \end{equation}
 \end{equation}

-where $T$ is the execution time of the program, $T_{cp}$ is the computation
-time and $T_{cp} \leq T$.  $T_{cp}$ may be equal to $T$ if there is no
+where $T$ is the execution time of the program, $Tcp$ is the computation
+time and $Tcp \leq T$.  $Tcp$ may be equal to $T$ if there is no

 communication and no slack time.
 
 The main objective of DVFS operation is to reduce the overall energy consumption~\cite{Le_DVFS.Laws.of.Diminishing.Returns}.  
 communication and no slack time.
 
 The main objective of DVFS operation is to reduce the overall energy consumption~\cite{Le_DVFS.Laws.of.Diminishing.Returns}.  


@@ -309,7 +321,7 @@ The operational frequency $F$ depends linearly on the supply voltage $V$, i.e.,
 constant $\beta$.  This equation is used to study the change of the dynamic
 voltage with respect to various frequency values in~\cite{Rauber_Analytical.Modeling.for.Energy}.  The reduction
 process of the frequency can be expressed by the scaling factor $S$ which is the
 constant $\beta$.  This equation is used to study the change of the dynamic
 voltage with respect to various frequency values in~\cite{Rauber_Analytical.Modeling.for.Energy}.  The reduction
 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}).
+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:
 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:


@@ -347,7 +359,7 @@ to the frequency scaling factor, while this scaling factor does not affect the c
 The static energy of a processor after scaling its frequency is computed as follows: 
 \begin{equation}
   \label{eq:Estatic}
 The static energy of a processor after scaling its frequency is computed as follows: 
 \begin{equation}
   \label{eq:Estatic}

- E_\textit{s} = P_\textit{s} \cdot (Tcp \cdot S  + Tcm)
+ E_\textit{s} = Ps \cdot (Tcp \cdot S  + Tcm)

 \end{equation}
 
 In the considered heterogeneous platform, each processor $i$ might have different dynamic and 
 \end{equation}
 
 In the considered heterogeneous platform, each processor $i$ might have different dynamic and 


@@ -631,7 +643,7 @@ of these different types of  nodes are inspired   from the specifications of rea
 The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, 
 for example if  a benchmark was executed on 8 nodes, 2 nodes from each type were used. Since the constructors 
 of CPUs do not specify the dynamic and the static power of their CPUs, for each type of node they were 
 The heterogeneous platform had up to 144 nodes and had nodes from the four types in equal proportions, 
 for example if  a benchmark was executed on 8 nodes, 2 nodes from each type were used. Since the constructors 
 of CPUs do not specify the dynamic and the static power of their CPUs, for each type of node they were 

-chosen proportionally to  its computing power (FLOPS).  In the initial heterogeneous platform,  while computing 
+chosen proportionally to its computing power (FLOPS).  In the initial heterogeneous platform,  while computing 

 with highest frequency, each node  consumed power proportional to its computing power which 80\% of it was 
 dynamic power and the rest was 20\% for the static power, the same assumption  was made in \cite{Our_first_paper,Rauber_Analytical.Modeling.for.Energy}. 
 Finally, These nodes were connected via an ethernet network with 1 Gbit/s bandwidth.
 with highest frequency, each node  consumed power proportional to its computing power which 80\% of it was 
 dynamic power and the rest was 20\% for the static power, the same assumption  was made in \cite{Our_first_paper,Rauber_Analytical.Modeling.for.Energy}. 
 Finally, These nodes were connected via an ethernet network with 1 Gbit/s bandwidth.


@@ -887,8 +899,8 @@ Plots (\ref{fig:energy} and \ref{fig:per_deg}) present the energy saving and per
 respectively for all the benchmarks according to the number of used nodes. As shown in the first plot, 
 the energy saving percentages of the benchmarks MG, LU, BT and FT are decreased linearly  when the the 
 number of nodes is increased. While for the  EP and SP benchmarks, the energy saving percentage is not 
 respectively for all the benchmarks according to the number of used nodes. As shown in the first plot, 
 the energy saving percentages of the benchmarks MG, LU, BT and FT are decreased linearly  when the the 
 number of nodes is increased. While for the  EP and SP benchmarks, the energy saving percentage is not 

-affected by the increase of the number of computing nodes, because in these benchmarks there are no 
-communications. Finally, the energy saving of the GC benchmark  is significantly decreased when the number 
+affected by the increase of the number of computing nodes, because in these benchmarks there are little or 
+no communications. Finally, the energy saving of the GC benchmark  is significantly decreased when the number 

 of nodes is increased because  this benchmark has more communications than the others. The second plot 
 shows that the performance degradation percentages of most of the benchmarks are decreased when they 
 run on a big number of nodes because they spend more time communicating than computing, thus, scaling 
 of nodes is increased because  this benchmark has more communications than the others. The second plot 
 shows that the performance degradation percentages of most of the benchmarks are decreased when they 
 run on a big number of nodes because they spend more time communicating than computing, thus, scaling