corrections

[mpi-energy2.git] / mpi-energy2-extension / Heter_paper.tex

diff --git a/mpi-energy2-extension/Heter_paper.tex b/mpi-energy2-extension/Heter_paper.tex
index 5ef27298690893c35cdb23d425ac60510eec3e40..85f68f4339cc0d41cd74dc25412f7936997d76d2 100644 (file)
--- a/mpi-energy2-extension/Heter_paper.tex
+++ b/mpi-energy2-extension/Heter_paper.tex
@@ -107,9 +107,9 @@
 
 
 
 
 
 

-\title{Optimizing Energy Consumption with DVFS for Message \\
-         Passing Applications \textcolor{blue}{with iterations} on \\
-                    Grid Architectures} 
+\title{Optimizing the Energy Consumption \\ 
+of Message Passing Applications with Iterations \\ 
+Executed over Grids} 

   
 
 
   
 
 


@@ -143,10 +143,10 @@ scaling (DVFS) is one of them. It can be used to reduce the power consumption of
   In this paper, a new online frequency selecting algorithm for grids, composed of heterogeneous clusters, is presented.  
   It selects the frequencies and tries to give the best
   trade-off between energy saving and performance degradation, for each node
   In this paper, a new online frequency selecting algorithm for grids, composed of heterogeneous clusters, is presented.  
   It selects the frequencies and tries to give the best
   trade-off between energy saving and performance degradation, for each node

-  computing the message passing  application \textcolor{blue}{with iterations}. 
+  computing the message passing  application with iterations. 

   The algorithm has a small
   overhead and works without training or profiling. It uses a new energy model
   The algorithm has a small
   overhead and works without training or profiling. It uses a new energy model

-  for message passing  applications \textcolor{blue}{with iterations} running on a  grid. 
+  for message passing  applications with iterations running on a  grid. 

   The proposed algorithm is evaluated on a real grid, the Grid'5000 platform, while
   running the NAS parallel benchmarks.  The experiments on 16 nodes, distributed on three clusters, show that it reduces  on average the
   energy consumption  by \np[\%]{30} while  the performance  is on average only degraded
   The proposed algorithm is evaluated on a real grid, the Grid'5000 platform, while
   running the NAS parallel benchmarks.  The experiments on 16 nodes, distributed on three clusters, show that it reduces  on average the
   energy consumption  by \np[\%]{30} while  the performance  is on average only degraded


@@ -192,7 +192,7 @@ This heterogeneous platform executes more than 7 GFlops per watt while consuming
 50.32 kilowatts.
 
 Besides platform improvements, there are many software and hardware techniques
 50.32 kilowatts.
 
 Besides platform improvements, there are many software and hardware techniques

-to lower the energy consumption of these platforms, such as DVFS, scheduling \textcolor{blue}{and other techniques}.
+to lower the energy consumption of these platforms, such as DVFS, scheduling and other techniques.

  DVFS is a widely used process to reduce the energy consumption of a
 processor by lowering its frequency
 \cite{Rizvandi_Some.Observations.on.Optimal.Frequency}. However, it also reduces
  DVFS is a widely used process to reduce the energy consumption of a
 processor by lowering its frequency
 \cite{Rizvandi_Some.Observations.on.Optimal.Frequency}. However, it also reduces


@@ -202,7 +202,7 @@ different optimization strategies to select the frequency that gives the best
 trade-off between the energy reduction and performance degradation ratio. In
 \cite{Our_first_paper} and \cite{pdsec2015}, a frequency selecting algorithm
 was proposed to reduce the energy consumption of message passing 
 trade-off between the energy reduction and performance degradation ratio. In
 \cite{Our_first_paper} and \cite{pdsec2015}, a frequency selecting algorithm
 was proposed to reduce the energy consumption of message passing 

-applications \textcolor{blue}{with iterations} running over homogeneous and heterogeneous clusters respectively.
+applications with iterations running over homogeneous and heterogeneous clusters respectively.

 The results of the experiments showed significant energy consumption
 reductions. All the experimental results were conducted over the SimGrid
 simulator \cite{SimGrid}, which offers easy tools to describe homogeneous and heterogeneous  platforms, and to simulate the execution of message passing parallel
 The results of the experiments showed significant energy consumption
 reductions. All the experimental results were conducted over the SimGrid
 simulator \cite{SimGrid}, which offers easy tools to describe homogeneous and heterogeneous  platforms, and to simulate the execution of message passing parallel


@@ -212,7 +212,7 @@ In this paper, a new frequency selecting algorithm, adapted to grid platforms
 composed of heterogeneous clusters, is presented. It is applied to the NAS
 parallel benchmarks and evaluated over a real testbed, the Grid'5000 platform
 \cite{grid5000}. It selects for a grid platform running a message passing
 composed of heterogeneous clusters, is presented. It is applied to the NAS
 parallel benchmarks and evaluated over a real testbed, the Grid'5000 platform
 \cite{grid5000}. It selects for a grid platform running a message passing

- application \textcolor{blue}{with iterations} the vector of frequencies that simultaneously tries to
+ application with iterations the vector of frequencies that simultaneously tries to

 offer the maximum energy reduction and minimum performance degradation
 ratios. The algorithm has a very small overhead, works online and does not need
 any training or profiling.
 offer the maximum energy reduction and minimum performance degradation
 ratios. The algorithm has a very small overhead, works online and does not need
 any training or profiling.


@@ -255,8 +255,8 @@ 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.
 
 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
- synchronous applications \textcolor{blue}{with iterations} running over heterogeneous grid platforms.  Some
+In this paper, we are interested in reducing the energy consumption of message passing
+ synchronous applications with iterations running over heterogeneous grid platforms.  Some

 works have already been done for such platforms and they can be classified into
 two types of heterogeneous platforms:
 \begin{itemize}
 works have already been done for such platforms and they can be classified into
 two types of heterogeneous platforms:
 \begin{itemize}


@@ -304,7 +304,7 @@ 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 
 following contributions :
 \begin{enumerate}
 \item two new energy and performance models for message passing 

-  synchronous applications \textcolor{blue}{with iterations} running over a heterogeneous grid platform. Both models
+  synchronous applications with iterations running over a heterogeneous grid platform. Both models

   take into account communication and slack times. The models can predict the
   required energy and the execution time of the application.
 
   take into account communication and slack times. The models can predict the
   required energy and the execution time of the application.
 


@@ -312,7 +312,7 @@ following contributions :
   platforms. The algorithm has a very small overhead and does not need any
   training nor profiling. It uses a new optimization function which
   simultaneously maximizes the performance and minimizes the energy consumption
   platforms. The algorithm has a very small overhead and does not need any
   training nor profiling. It uses a new optimization function which
   simultaneously maximizes the performance and minimizes the energy consumption

-  of a message passing  synchronous application \textcolor{blue}{with iterations}.
+  of a message passing  synchronous application with iterations.

 
 \end{enumerate}
 
 
 \end{enumerate}
 


@@ -322,25 +322,25 @@ following contributions :
 \label{sec.exe}
 
 \subsection{The execution time of message passing distributed 
 \label{sec.exe}
 
 \subsection{The execution time of message passing distributed 

-  applications \textcolor{blue}{with iterations} on a heterogeneous platform}
+  applications with iterations on a heterogeneous platform}

 
 In this paper, we are interested in reducing the energy consumption of message
 
 In this paper, we are interested in reducing the energy consumption of message

-passing distributed  synchronous applications \textcolor{blue}{with iterations} running over
+passing distributed  synchronous applications with iterations running over

 heterogeneous grid platforms. A heterogeneous grid platform could be defined as a collection of
 heterogeneous computing clusters interconnected via a long distance network which has lower bandwidth 
 and higher latency than the local networks of the clusters. Each computing cluster in the grid is composed of homogeneous nodes that are connected together via high speed network. Therefore, each cluster has different characteristics such as computing power (FLOPS), energy consumption, CPU's frequency range, network bandwidth and latency.
 
 heterogeneous grid platforms. A heterogeneous grid platform could be defined as a collection of
 heterogeneous computing clusters interconnected via a long distance network which has lower bandwidth 
 and higher latency than the local networks of the clusters. Each computing cluster in the grid is composed of homogeneous nodes that are connected together via high speed network. Therefore, each cluster has different characteristics such as computing power (FLOPS), energy consumption, CPU's frequency range, network bandwidth and latency.
 

-The overall execution time of a distributed  synchronous application  \textcolor{blue}{with iterations} 
+The overall execution time of a distributed  synchronous application  with iterations running 

 over a heterogeneous grid consists of the sum of the computation time and 
 the communication time for every iteration on a node. 
 over a heterogeneous grid consists of the sum of the computation time and 
 the communication time for every iteration on a node. 

-\textcolor{blue}{However, nodes from distinct clusters in a grid have different computing powers, thus
-while executing message passing \textcolor{blue}{with iterations} synchronous applications, fast nodes
+However, nodes from distinct clusters in a grid have different computing powers, thus
+while the application, 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
 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. } 
+periods are called idle or slack times. 

 Therefore, the
 overall execution time of the program is the execution time of the slowest task 
 Therefore, the
 overall execution time of the program is the execution time of the slowest task 

-which has the highest computation time and no slack time. \textcolor{blue}{For example, in Figure \ref{fig:heter}  the task 1 is the slower task which has no slack time (not waits for the other nodes) and it is only has the communication times.}
+which has the highest computation time and almost no slack time. For example, in Figure \ref{fig:heter}, task 1 is the slower task and it does not have to wait for the other nodes to communicate with them because they all finish their computations before it.  

 
 \begin{figure}[!t]
   \centering
 
 \begin{figure}[!t]
   \centering


@@ -361,7 +361,8 @@ as in (\ref{eq:s}).
   \label{eq:s}
   S = \frac{\Fmax}{\Fnew}
 \end{equation}
   \label{eq:s}
   S = \frac{\Fmax}{\Fnew}
 \end{equation}

-\textcolor{blue}{Where $\Fmax$ is the maximum frequency before applying DVFS and $\Fnew$  is the new frequency after applying DVFS.}
+where $\Fmax$ is the maximum frequency before applying any DVFS and $\Fnew$  is the new frequency after applying DVFS.
+

 The execution time of a compute bound sequential program is linearly
 proportional to the frequency scaling factor $S$.  On the other hand, message
 passing distributed applications consist of two parts: computation and
 The execution time of a compute bound sequential program is linearly
 proportional to the frequency scaling factor $S$.  On the other hand, message
 passing distributed applications consist of two parts: computation and


@@ -378,7 +379,7 @@ especially different frequency gears, when applying DVFS operations on the nodes
 of these clusters, they may get different scaling factors represented by a scaling vector:
 $(S_{11}, S_{12},\dots, S_{NM_i})$ where $S_{ij}$ is the scaling factor of processor $j$ in cluster $i$ . To
 be able to predict the execution time of message passing synchronous 
 of these clusters, they may get different scaling factors represented by a scaling vector:
 $(S_{11}, S_{12},\dots, S_{NM_i})$ where $S_{ij}$ is the scaling factor of processor $j$ in cluster $i$ . To
 be able to predict the execution time of message passing synchronous 

-applications \textcolor{blue}{with iterations} running over a heterogeneous grid, for different vectors of
+applications with iterations running over a heterogeneous grid, for different vectors of

 scaling factors, the communication time and the computation time for all the
 tasks must be measured during the first iteration before applying any DVFS
 operation. Then the execution time for one iteration of the application with any
 scaling factors, the communication time and the computation time for all the
 tasks must be measured during the first iteration before applying any DVFS
 operation. Then the execution time for one iteration of the application with any


@@ -394,10 +395,10 @@ where $N$ is the number of  clusters in the grid, $M_i$ is the number of  nodes
  cluster $i$, $\TcpOld[ij]$ is the computation time of processor $j$ in the cluster $i$ 
 and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ during the 
 first  iteration.  The execution time for one iteration is equal to the sum of the maximum computation time for all nodes with the new scaling factors
  cluster $i$, $\TcpOld[ij]$ is the computation time of processor $j$ in the cluster $i$ 
 and $\Tcm[hj]$ is the communication time of processor $j$ in the cluster $h$ during the 
 first  iteration.  The execution time for one iteration is equal to the sum of the maximum computation time for all nodes with the new scaling factors

-and \textcolor{blue}{the  communication time of the slower node without slack time during one iteration.
-The slower node $h$ is the node that gives maximum execution time in all clusters befor scaling its frequency.}
+and the  communication time of the slower node without slack time during one iteration.
+The slower node $h$ is the node that gives the maximum execution time in all the clusters before applying DVFS.

 It means that only the communication time without any slack time is taken into account.
 It means that only the communication time without any slack time is taken into account.

-Therefore, the execution time of the  application \textcolor{blue}{with iterations} is equal to
+Therefore, the execution time of the  application is equal to

 the execution time of one iteration as in Equation (\ref{eq:perf}) multiplied by the
 number of iterations of that application.
 
 the execution time of one iteration as in Equation (\ref{eq:perf}) multiplied by the
 number of iterations of that application.
 


@@ -1229,8 +1230,8 @@ The experimental results, the energy saving, performance degradation and trade-o
 presented in  Figures~\ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively.
 
 As shown in these figures, the proposed frequencies selection algorithm, Maxdist, outperforms the EDP algorithm in terms of energy consumption reduction and performance for all of the benchmarks executed over the two scenarios. 
 presented in  Figures~\ref{fig:edp-eng}, \ref{fig:edp-perf} and \ref{fig:edp-dist} respectively.
 
 As shown in these figures, the proposed frequencies selection algorithm, Maxdist, outperforms the EDP algorithm in terms of energy consumption reduction and performance for all of the benchmarks executed over the two scenarios. 

-The proposed algorithm gives better results than the EDP method because it 
-maximizes the energy saving and the performance at the same time. 
+The proposed algorithm gives better results than the EDP method because the former selects the set of frequencies that  
+gives the best tradeoff between energy saving and performance. 

 Moreover, the proposed scaling algorithm gives the same weight for these two metrics.
 Whereas, the EDP algorithm gives sometimes negative trade-off values for some benchmarks in the two sites scenarios.
 These negative trade-off values mean that the performance degradation percentage is higher than the energy saving percentage.
 Moreover, the proposed scaling algorithm gives the same weight for these two metrics.
 Whereas, the EDP algorithm gives sometimes negative trade-off values for some benchmarks in the two sites scenarios.
 These negative trade-off values mean that the performance degradation percentage is higher than the energy saving percentage.