corrections

[ThesisAhmed.git] / CHAPITRE_01.tex

diff --git a/CHAPITRE_01.tex b/CHAPITRE_01.tex
index c3b064142dfd0812f95f522c8da83e1ba9abdc11..306bcf72f83ebe340840f81819ce892f6045a0b5 100644 (file)
--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -323,8 +323,8 @@ some examples for each type of the parallel programming models are discussed:
 
 \section{Iterative Methods} 
 \label{ch1:3}
 
 \section{Iterative Methods} 
 \label{ch1:3}

-In this work, we are interested in solving linear equations which are well known in the scientific area.
-It is generally expressed in the following form:
+In this work, we are interested in solving system of linear equations which are very common in the scientific field. A system of linear equations can be expressed as follows:
+

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


@@ -332,14 +332,14 @@ It is generally expressed in the following form:
 \end{equation}
 
 Where $A$ is a two dimensional matrix of size $N \times N$, $x$ is the unknown vector,
 \end{equation}
 
 Where $A$ is a two dimensional matrix of size $N \times N$, $x$ is the unknown vector,

-and $b$ is a vector of constant, each of size $N$. There are two types of solution methods to solve this linear system.
-The first type of methods is called \textbf{Direct methods}, which consist of a finite number of steps depending on the 
-size  of the linear system to give the exact solution. If the problem size is very big, these methods are expensive or their
-solutions are impossible in some cases.  The second type is called \textbf{Iterative methods}, which computes 
-the same block of  operations  several times, starting from the initial vector until reaching the acceptable 
-approximation  of the exact solution. However, they can be effectively applied in parallel. Moreover, iterative methods can be used to solve both  linear and non-linear equations.
+and $b$ is a vector of constant, each of size $N$. There are two types of solution methods to solve this linear system: the \textbf{direct} and the \textbf{iterative methods}.
+A direct method executes a finite number of steps, depending on the 
+size  of the linear system and gives the exact solution of the system. If the problem  is very big, this method is expensive or its
+solution is impossible in some cases.  On the other hand, methods with iterations execute the same block of instructions many times. The number of iterations can be predefined or the application iterates until a criterion is satisfied. Iterative methods are methods with iterations that start from an initial guess and 
+improve successively the solution until reaching an acceptable approximation of the exact solution. 
+These methods are well adapted for large systems and can be easily parallelized. 

 
 

-The sequential iterative algorithm is typically organized as a series of steps essentially  of the form:
+A sequential iterative algorithm is typically organized as a series of steps essentially  of the form:

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


@@ -372,6 +372,7 @@ The sequential iterative algorithm at each iteration computes the value of the r
 \end{equation}  
 Where $N$ is the size of the vector $X$. Then, the iterative sequential algorithm stops  iterating if the maximum error between the last two successive solution vectors, as in \ref{eq:res}, is less than or equal to a threshold value. Otherwise, it replaces the new vector $X^{(k+1)}$ with the old vector $X^k$ and computes a new iteration.
 
 \end{equation}  
 Where $N$ is the size of the vector $X$. Then, the iterative sequential algorithm stops  iterating if the maximum error between the last two successive solution vectors, as in \ref{eq:res}, is less than or equal to a threshold value. Otherwise, it replaces the new vector $X^{(k+1)}$ with the old vector $X^k$ and computes a new iteration.
 

+

 \subsection{Synchronous Parallel Iterative method} 
 \label{ch1:3:1}
 The sequential iterative algorithm \ref{sia} can be parallelized by executing it on many computing units. To solve this algorithm on $M$ computing units, first the elements of the problem vector $X$ must be subdivided into $M$ sub-vectors, $X^k=(X_1^k,\dots,X_M^k)$. 
 \subsection{Synchronous Parallel Iterative method} 
 \label{ch1:3:1}
 The sequential iterative algorithm \ref{sia} can be parallelized by executing it on many computing units. To solve this algorithm on $M$ computing units, first the elements of the problem vector $X$ must be subdivided into $M$ sub-vectors, $X^k=(X_1^k,\dots,X_M^k)$. 


@@ -651,7 +652,7 @@ There are two drawbacks in this energy model as follows:
       types of  processors, which  consume different dynamic and static powers. 
 \end{itemize}
 
       types of  processors, which  consume different dynamic and static powers. 
 \end{itemize}
 

-Therefore, one of the more important goals of this work is to develop a new energy models that 
+Therefore, one of the most important goals of this work is to develop a new energy models that 

  take into consideration the communication times in addition to the  computation times in order to modelize and measure the energy consumptions of the parallel iterative methods. These models must be suitable to  homogeneous or heterogeneous parallel architectures.  
 
 \section{Conclusion}
  take into consideration the communication times in addition to the  computation times in order to modelize and measure the energy consumptions of the parallel iterative methods. These models must be suitable to  homogeneous or heterogeneous parallel architectures.  
 
 \section{Conclusion}