X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/kahina_paper1.git/blobdiff_plain/17c6b8d0b68892da7bd60cdf9c25aeb1e1baec7b..3f6a5b0bd8824bad90aead3dccfcfd8575ebadad:/paper.tex?ds=sidebyside diff --git a/paper.tex b/paper.tex index 09421ad..6a5bd46 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 +581,35 @@ 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=(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. @@ -606,12 +617,12 @@ exponential logarithm algorithm. \begin{algorithm}[H] \label{alg3-update} %\LinesNumbered -\caption{Kernel update} +\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} @@ -644,7 +655,6 @@ significant). -%%HIER END MY REVISIONS (SIDER) \section{Experimental study} \label{sec6} %\subsection{Definition of the used polynomials } @@ -704,13 +714,44 @@ 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 5,000s for 250,000 degrees +polynomials. In counterpart, the GPU implementation for the same +polynomials do not take more 100s. With the GPU +we can solve 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. -\subsection{Influence of the number of threads on the execution times of different polynomials (sparse and full)} -To optimize the performances of an algorithm on a GPU, it is necessary to maximize the use of cores GPU (maximize the number of threads executed in parallel) and to optimize the use of the various memoirs GPU. In fact, it is interesting to see the influence of the number of threads per block on the execution time of Ehrlich-Aberth algorithm. -For that, we notice that the maximum number of threads per block for the Nvidia Tesla K40 GPU is 1024, so we varied the number of threads per block from 8 to 1024. We took into account the execution time for both sparse and full of 10 different polynomials of size 50,000 and 10 different polynomials of size 500,000 degrees. +\subsection{Influence of the number of threads on the execution times + of different polynomials (sparse and full)} + +To optimize the performances of an algorithm on a GPU, it is necessary +to maximize the use of the GPU cores. In fact, it is interesting to +see the influence of the number of threads per block on the execution +time of Ehrlich-Aberth algorithm. For that, we notice that the +maximum number of threads per block for the Nvidia Tesla K40 GPU is +1024. So the number of threads per block ranges from 8 to 1024. We +took into account the execution time for both sparse and full of 10 +different polynomials of size 50,000 and 10 different polynomials of +size 500,000 degrees. \begin{figure}[htbp] \centering @@ -719,11 +760,17 @@ For that, we notice that the maximum number of threads per block for the Nvidia \label{fig:02} \end{figure} -The figure 2 show that, the best execution time for both sparse and full polynomial are given when the threads number varies between 64 and 256 threads per bloc. We notice that with small polynomials the best number of threads per block is 64, Whereas, the large polynomials the best number of threads per block is 256. However,In the following experiments we specify that the number of thread by block is 256. +Figure~\ref{fig:02} shows that, the best execution time for both +sparse and full polynomial are given when the threads number varies +between 64 and 256 threads per block. We notice that with small +polynomials the best number of threads per block is 64, whereas the +large polynomials the best number of threads per block is +256. However, in the following experiments we specify that the number +of threads per block is 256. -\subsection{The impact of exp-log solution to compute very high degrees of polynomial} +\subsection{Influence of exponential-logarithm solution to compute very high degrees polynomials} -In this experiment we report the performance of log.exp solution describe in ~\ref{sec2} to compute very high degrees polynomials. +In this experiment we report the performance of exp.log solution described in ~\ref{sec2} to compute very high degrees polynomials. \begin{figure}[htbp] \centering \includegraphics[width=0.8\textwidth]{figures/sparse_full_explog}