X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/06e283aaa22fc12cca34e85fa3c1d262d97bf23c..9e9097b62ebd81e17d0ed5077bb647ca2a7d50ee:/paper.tex?ds=inline

diff --git a/paper.tex b/paper.tex
index cd7f270..384ff8b 100644
--- a/paper.tex
+++ b/paper.tex
@@ -582,7 +582,8 @@ Algorithm~\ref{alg2-cuda} shows a sketch of the Ehrlich-Aberth algorithm using C
 \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), Pu (the derivative of P), $n$ (Polynomial's degrees),$\Delta z_{max}$ (maximum value of stop condition)}
+  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), $ZPrec$ (the previous solution root's vector)}
 
@@ -605,7 +606,7 @@ Copy results from GPU memory to CPU memory\;
 \end{algorithm}
 ~\\ 
 
-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}).
+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=(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
 $d\_Z$, according to Algorithm~\ref{alg3-update}. We notice that the
@@ -616,7 +617,7 @@ exponential logarithm algorithm.
 \begin{algorithm}[H]
 \label{alg3-update}
 %\LinesNumbered
-\caption{Kernel update}
+\caption{Kernel\_update}
 
 \eIf{$(\left|d\_Z\right|<= R)$}{
 $kernel\_update((d\_Z,d\_Pcoef,d\_Pdegres,d\_Pucoef,d\_Pudegres)$\;}
@@ -714,7 +715,30 @@ of the methods are given in Section~\ref{sec:vec_initialization}.
 \label{fig:01}
 \end{figure}
 %%Figure 1 %%show a comparison of execution time between the parallel and sequential version of the Ehrlich-Aberth algorithm with sparse polynomial exceed 100000, 
-In Figure~\ref{fig:01}, we report respectively the execution time of the Ehrlich-Aberth method implemented initially on one core of the Quad-Core Xeon E5620 CPU than on four cores of the same machine with \textit{OpenMP} platform and the execution time of the same method implemented on one Nvidia Tesla K40c GPU, with sparse polynomial degrees ranging from 100,000 to 1,000,000. We can see that the method implemented on the GPU are faster than those implemented on the CPU (4 cores). This is due to the GPU ability to compute the data-parallel functions faster than its CPU counterpart. However, the execution time for the CPU(4 cores) implementation exceed 5,000 s for 250,000 degrees polynomials, in counterpart  the GPU implementation for the same polynomials not reach 100 s, more than again, with an execution time under to 2,500 s CPU (4 cores) implementation can resolve polynomials degrees of only 200,000, whereas GPU implementation can resolve polynomials more than 1,000,000 degrees. We can also notice that the GPU implementation are almost 47 faster then those implementation on the CPU(4 cores). However the CPU(4 cores) implementation are almost 4 faster then his implementation on CPU (1 core). Furthermore, we verify that the number of iterations and the convergence precision is the same for the both CPU and GPU implementation. %This reduction of time allows us to compute roots of polynomial of more important degree at the same time than with a CPU.
+In Figure~\ref{fig:01}, we report the execution times of the
+Ehrlich-Aberth method on one core of a Quad-Core Xeon E5620 CPU, on
+four cores on the same machine with \textit{OpenMP} and on a Nvidia
+Tesla K40c GPU.  We chose different sparse polynomials with degrees
+ranging from 100,000 to 1,000,000. We can see that the implementation
+on the GPU is faster than those implemented on the CPU.
+
+This is due to the GPU ability to compute the data-parallel functions
+faster than its CPU counterpart. However, the execution time for the
+CPU (4 cores) implementation exceed 5,000 s for 250,000 degrees
+polynomials, in counterpart the GPU implementation for the same
+polynomials not reach 100 s, more than again, with an execution time
+under to 2,500 s CPU (4 cores) implementation can resolve With the GPU
+we can solve very high degrees polynomials very quickly up to degree
+of 1,000,000. We can also notice that the GPU implementation are
+almost 47 faster then those implementation on the CPU (4
+cores). However the CPU(4 cores) implementation are almost 4 faster
+then his implementation on CPU (1 core). Furthermore, the number of
+iterations and the convergence precision are similar with the CPU
+and the GPU implementation.
+
+%%This reduction
+%of time allows us to compute roots of polynomial of more important
+%degree at the same time than with a CPU.
  
  %We notice that the convergence precision is a round $10^{-7}$ for the both implementation on CPU and GPU. Consequently, we can conclude that Ehrlich-Aberth on GPU are faster and accurately then CPU implementation.
 
