X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/17c6b8d0b68892da7bd60cdf9c25aeb1e1baec7b..06e283aaa22fc12cca34e85fa3c1d262d97bf23c:/paper.tex?ds=sidebyside

diff --git a/paper.tex b/paper.tex
index 09421ad..cd7f270 100644
--- a/paper.tex
+++ b/paper.tex
@@ -229,7 +229,7 @@ and experimental study results. Finally, Section~\ref{sec7} 6 concludes
 this paper and gives some hints for future research directions in this
 topic.
 
-\section{The Sequential Ehrlich-Aberth method}
+\section{Ehrlich-Aberth method}
 \label{sec1}
 A cubically convergent iteration method for finding zeros of
 polynomials was proposed by O. Aberth~\cite{Aberth73}. In the
@@ -496,47 +496,54 @@ polynomials of 48000.
 
 
 
-\subsection{Sequential Ehrlich-Aberth algorithm}
-The main steps of Ehrlich-Aberth method are shown in Algorithm.~\ref{alg1-seq} :
-%\LinesNumbered  
-\begin{algorithm}[H]
-\label{alg1-seq}
+%% \subsection{Sequential Ehrlich-Aberth algorithm}
+%% The main steps of Ehrlich-Aberth method are shown in Algorithm.~\ref{alg1-seq} :
+%% %\LinesNumbered  
+%% \begin{algorithm}[H]
+%% \label{alg1-seq}
 
-\caption{A sequential algorithm to find roots with the Ehrlich-Aberth method}
+%% \caption{A sequential algorithm to find roots with the Ehrlich-Aberth method}
 
-\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (error tolerance
-  threshold), P (Polynomial to solve), $\Delta z_{max}$ (maximum value
-  of stop condition), k (number of iteration), n (Polynomial's degrees)}
-\KwOut {Z (The solution root's vector), ZPrec (the previous solution root's vector)}
+%% \KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (error tolerance
+%%   threshold), $P$ (Polynomial to solve),$Pu$ (the derivative of P) $\Delta z_{max}$ (maximum value
+%%   of stop condition), k (number of iteration), n (Polynomial's degrees)}
+%% \KwOut {$Z$ (The solution root's vector), $ZPrec$ (the previous solution root's vector)}
 
-\BlankLine
+%% \BlankLine
 
-Initialization of the coefficients of the polynomial to solve\;
-Initialization of the solution vector $Z^{0}$\;
-$\Delta z_{max}=0$\;
- k=0\;
+%% Initialization of $P$\;
+%% Initialization of $Pu$\;
+%% Initialization of the solution vector $Z^{0}$\;
+%% $\Delta z_{max}=0$\;
+%%  k=0\;
 
-\While {$\Delta z_{max} > \varepsilon$}{
- Let $\Delta z_{max}=0$\;
-\For{$j \gets 0 $ \KwTo $n$}{
-$ZPrec\left[j\right]=Z\left[j\right]$;// save Z at the iteration k.\
+%% \While {$\Delta z_{max} > \varepsilon$}{
+%%  Let $\Delta z_{max}=0$\;
+%% \For{$j \gets 0 $ \KwTo $n$}{
+%% $ZPrec\left[j\right]=Z\left[j\right]$;// save Z at the iteration k.\
 
-$Z\left[j\right]=H\left(j,Z\right)$;//update Z with the iterative function.\
-}
-k=k+1\;
+%% $Z\left[j\right]=H\left(j, Z, P, Pu\right)$;//update Z with the iterative function.\
+%% }
+%% k=k+1\;
 
-\For{$i \gets 0 $ \KwTo $n-1$}{
-$c= testConverge(\Delta z_{max},ZPrec\left[j\right],Z\left[j\right])$\;
-\If{$c > \Delta z_{max}$ }{
-$\Delta z_{max}$=c\;}
-}
+%% \For{$i \gets 0 $ \KwTo $n-1$}{
+%% $c= testConverge(\Delta z_{max},ZPrec\left[j\right],Z\left[j\right])$\;
+%% \If{$c > \Delta z_{max}$ }{
+%% $\Delta z_{max}$=c\;}
+%% }
 
-}
-\end{algorithm}
+%% }
+%% \end{algorithm}
 
-~\\ 
-In this sequential algorithm, one CPU thread  executes all the steps. Let us look to the $3^{rd}$ step i.e. the execution of the iterative function, 2 sub-steps are needed. The first sub-step \textit{save}s the solution vector of the previous iteration, the second sub-step \textit{update}s or computes the new values of the roots vector.
-There exists two ways to execute the iterative function that we call a Jacobi one and a Gauss-Seidel one. With the Jacobi iteration, at iteration $k+1$ we need all the previous values $z^{(k)}_{i}$ to compute the new values $z^{(k+1)}_{i}$, that is :
+%% ~\\ 
+%% In this sequential algorithm, one CPU thread  executes all the steps. Let us look to the $3^{rd}$ step i.e. the execution of the iterative function, 2 sub-steps are needed. The first sub-step \textit{save}s the solution vector of the previous iteration, the second sub-step \textit{update}s or computes the new values of the roots vector.
+
+\subsection{Parallel implementation with CUDA }
+
+In order to implement the Ehrlich-Aberth method in CUDA, it is
+possible to use the Jacobi scheme or the Gauss Seidel one.  With the
+Jacobi iteration, at iteration $k+1$ we need all the previous values
+$z^{(k)}_{i}$ to compute the new values $z^{(k+1)}_{i}$, that is :
 
 \begin{equation}
 EAJ: z^{k+1}_{i}=\frac{p(z^{k}_{i})}{p'(z^{k}_{i})-p(z^{k}_{i})\sum^{n}_{j=1 j\neq i}\frac{1}{z^{k}_{i}-z^{k}_{j}}}, i=1,...,n.
@@ -552,18 +559,18 @@ Using Eq.~\ref{eq:Aberth-H-GS} to update the vector solution
 \textit{Z}, we expect the Gauss-Seidel iteration to converge more
 quickly because, just as any Jacobi algorithm (for solving linear systems of equations), it uses the most fresh computed roots $z^{k+1}_{i}$.
 
-The $4^{th}$ step of the algorithm checks the convergence condition using Eq.~\ref{eq:Aberth-Conv-Cond}.
-Both steps 3 and 4 use 1 thread to compute all the $n$ roots on CPU, which is very harmful for performance in case of the large degree polynomials.
+%The $4^{th}$ step of the algorithm checks the convergence condition using Eq.~\ref{eq:Aberth-Conv-Cond}.
+%Both steps 3 and 4 use 1 thread to compute all the $n$ roots on CPU, which is very harmful for performance in case of the large degree polynomials.
 
 
-\subsection{Parallel implementation with CUDA }
-On the CPU,  both steps 3 and 4 contain the loop \verb=for= and a single thread executes all the instructions in the loop $n$ times. In this subsection, we explain how the GPU architecture can compute this loop and reduce the execution time.
-In the GPU, the scheduler assigns the execution of this loop to a
-group of threads organised as a grid of blocks with block containing a
-number of threads. All threads within a block are executed
-concurrently in parallel. The instructions run on the GPU are grouped
-in special function called kernels. With CUDA, a programmer must
-describe the kernel execution context:  the size of the Grid, the number of blocks and the number of threads per block.
+
+%On the CPU,  both steps 3 and 4 contain the loop \verb=for= and a single thread executes all the instructions in the loop $n$ times. In this subsection, we explain how the GPU architecture can compute this loop and reduce the execution time.
+%In the GPU, the scheduler assigns the execution of this loop to a
+%group of threads organised as a grid of blocks with block containing a
+%number of threads. All threads within a block are executed
+%concurrently in parallel. The instructions run on the GPU are grouped
+%in special function called kernels. With CUDA, a programmer must
+%describe the kernel execution context:  the size of the Grid, the number of blocks and the number of threads per block.
 
 %In CUDA programming, all the instructions of the  \verb=for= loop are executed by the GPU as a kernel. A kernel is a function written in CUDA and defined by the  \verb=__global__= qualifier added before a usual \verb=C= function, which instructs the compiler to generate appropriate code to pass it to the CUDA runtime in order to be executed on the GPU. 
 
@@ -574,31 +581,34 @@ Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth algorithm using C
 %\LinesNumbered
 \caption{CUDA Algorithm to find roots with the Ehrlich-Aberth method}
 
-\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (error tolerance threshold), P(Polynomial to solve), $\Delta z_{max}$ (maximum value of stop condition)}
+\KwIn{$Z^{0}$ (Initial root's vector), $\varepsilon$ (error tolerance
+  threshold), P(Polynomial to solve), Pu (the derivative of P), $n$ (Polynomial's degrees),$\Delta z_{max}$ (maximum value of stop condition)}
 
-\KwOut {Z (The solution root's vector)}
+\KwOut {$Z$ (The solution root's vector), $ZPrec$ (the previous solution root's vector)}
 
 \BlankLine
 
-Initialization of the coefficients of the polynomial to solve\;
+Initialization of the of P\;
+Initialization of the of Pu\;
 Initialization of the solution vector $Z^{0}$\;
-Allocate and copy initial data to the GPU global memory\;
+Allocate and copy initial data to the GPU global memory ($d\_Z,d\_ZPrec,d\_P,d\_Pu$)\;
 k=0\;
-\While {$\Delta z_{max}\succ \epsilon$}{
+\While {$\Delta z_{max} > \epsilon$}{
  Let $\Delta z_{max}=0$\;
-$ kernel\_save(d\_Z^{k-1})$\;
+$ kernel\_save(d\_ZPrec,d\_Z)$\;
 k=k+1\;
-$ kernel\_update(d\_Z^{k})$\;
-$kernel\_testConverge(\Delta z_{max},d\_Z^{k},d\_Z^{k-1})$\;
+$ kernel\_update(d\_Z,d\_P,d\_Pu)$\;
+$kernel\_testConverge(\Delta z_{max},d\_Z,d\_ZPrec)$\;
 
 }
+Copy results from GPU memory to CPU memory\;
 \end{algorithm}
 ~\\ 
 
-After the initialisation step, all data of the root finding problem to be solved must be copied from the CPU memory to the GPU global memory, because the GPUs only access data already present in their memories. Next, all the data-parallel arithmetic operations inside the main loop \verb=(do ... while(...))= are executed as kernels by the GPU. The first kernel named \textit{save} in line 6 of Algorithm~\ref{alg2-cuda} consists in saving the vector of polynomial's root found at the previous time-step in GPU memory, in order to check the convergence of the roots after each iteration (line 8, Algorithm~\ref{alg2-cuda}).
+After the initialization step, all data of the root finding problem to be solved must be copied from the CPU memory to the GPU global memory, because the GPUs only access data already present in their memories. Next, all the data-parallel arithmetic operations inside the main loop \verb=(do ... while(...))= are executed as kernels by the GPU. The first kernel named \textit{save} in line 6 of Algorithm~\ref{alg2-cuda} consists in saving the vector of polynomial's root found at the previous time-step in GPU memory, in order to check the convergence of the roots after each iteration (line 8, Algorithm~\ref{alg2-cuda}).
 
 The second kernel executes the iterative function $H$ and updates
-$z^{k}$, according to Algorithm~\ref{alg3-update}. We notice that the
+$d\_Z$, according to Algorithm~\ref{alg3-update}. We notice that the
 update kernel is called in two forms, separated with the value of
 \emph{R} which determines the radius beyond which we apply the
 exponential logarithm algorithm. 
@@ -608,10 +618,10 @@ exponential logarithm algorithm.
 %\LinesNumbered
 \caption{Kernel update}
 
-\eIf{$(\left|Z^{(k)}\right|<= R)$}{
-$kernel\_update(d\_z^{k})$\;}
+\eIf{$(\left|d\_Z\right|<= R)$}{
+$kernel\_update((d\_Z,d\_Pcoef,d\_Pdegres,d\_Pucoef,d\_Pudegres)$\;}
 {
-$kernel\_update\_ExpoLog(d\_z^{k})$\;
+$kernel\_update\_ExpoLog((d\_Z,d\_Pcoef,d\_Pdegres,d\_Pucoef,d\_Pudegres))$\;
 }
 \end{algorithm}