X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Krylov_multi.git/blobdiff_plain/f4a01eb8e780198f12449d8cd627edfb2bf8f0b9..2ba13b434c9d2aab12becfb44463be0a8bea74e7:/krylov_multi.tex?ds=sidebyside diff --git a/krylov_multi.tex b/krylov_multi.tex index 5a73670..98b8949 100644 --- a/krylov_multi.tex +++ b/krylov_multi.tex @@ -38,7 +38,7 @@ \begin{abstract} In this paper we revisit the Krylov multisplitting algorithm presented in -\cite{huang1993krylov} which uses a scalar method to minimize the Krylov +\cite{huang1993krylov} which uses a sequential method to minimize the Krylov iterations computed by a multisplitting algorithm. Our new algorithm is based on a parallel multisplitting algorithm with few blocks of large size using a parallel GMRES method inside each block and on a parallel Krylov minimization in @@ -97,7 +97,7 @@ convergence of the global system. In~\cite{couturier2008gremlins}, the authors proposed practical implementations of multisplitting algorithms to solve large scale linear systems. Inner solvers -could be based on scalar direct method with the LU method or scalar iterative +could be based on sequential direct method with the LU method or sequential iterative one with GMRES. In~\cite{prace-multi}, the authors have proposed a parallel multisplitting @@ -276,9 +276,11 @@ preconditioner it is possible to reduce the number of iterations but preconditioners are not scalable when using many cores. %Doing many experiments with many cores is not easy and requires to access to a supercomputer with several hours for developing a code and then improving it. -In the following we present some experiments we could achieved out on the -Hector architecture, the previous UK's high-end computing resource, funded by -the UK Research Councils, which has been stopped in the early 2014. +In the following we present some experiments we could achieved out on the Hector +architecture, a UK's high-end computing resource, funded by the UK Research +Councils~\cite{hector}. This is a Cray XE6 supercomputer, equipped with two +16-core AMD Opteron 2.3 Ghz and 32 GB of memory. Machines are interconnected +with a 3D torus. Table~\ref{tab1} shows the result of the experiments. The first column shows the size of the 3D Poisson problem. The size is chosen in order to have @@ -306,13 +308,13 @@ is reached. The precision and the maximum number of iterations of CGNR method ar \cline{3-8} & & Time (s) & nb Iter. & $\Delta$ & Time (s)& nb Iter. & $\Delta$ & \\ \hline -$468^3$ & 2048 (2x1024) & 299.7 & 41,028 & 5.02e-8 & 48.4 & 691(6,146) & 8.24e-08 & 6.19 \\ +$468^3$ & 2,048 (2x1,024) & 299.7 & 41,028 & 5.02e-8 & 48.4 & 691(6,146) & 8.24e-08 & 6.19 \\ \hline -$590^3$ & 4096 (2x2048) & 433.1 & 55,494 & 4.92e-7 & 74.1 & 1,101(8,211) & 6.62e-08 & 5.84 \\ +$590^3$ & 4,096 (2x2,048) & 433.1 & 55,494 & 4.92e-7 & 74.1 & 1,101(8,211) & 6.62e-08 & 5.84 \\ \hline -$743^3$ & 8192 (2x4096) & 704.4 & 87,822 & 4.80e-07 & 151.2 & 3,061(14,914) & 5.87e-08 & 4.65 \\ +$743^3$ & 8,192 (2x4,096) & 704.4 & 87,822 & 4.80e-07 & 151.2 & 3,061(14,914) & 5.87e-08 & 4.65 \\ \hline -$743^3$ & 8192 (4x2048) & 704.4 & 87,822 & 4.80e-07 & 110.3 & 1,531(12,721) & 1.47e-07& 6.39 \\ +$743^3$ & 8,192 (4x2,048) & 704.4 & 87,822 & 4.80e-07 & 110.3 & 1,531(12,721) & 1.47e-07& 6.39 \\ \hline \end{tabular} @@ -325,16 +327,47 @@ $743^3$ & 8192 (4x2048) & 704.4 & 87,822 & 4.80e-07 & 110.3 & 1 From these experiments, it can be observed that the multisplitting version is always faster than the GMRES version. The acceleration gain of the multisplitting version is between 4 and 6. It can be noticed that the number of -iterations is drastically reduced with the multisplitting version even it is not -neglectable. +iterations is drastically reduced with the multisplitting version even it is not +neglectable. Moreover, with 8,192 cores, we can see that using 4 clusters gives +better performance than simply using 2 clusters. In fact, we can remark that the +precision with 2 clusters is slightly better but in both cases the precision is +under the specified threshold. \section{Conclusion and perspectives} -We have implemented a Krylov multisplitting method to solve sparse linear systems on large-scale computing platforms. We have developed a synchronous two-stage method based on the block Jacobi multisplitting and uses GMRES iterative method as an inner iteration. Our contribution in this paper is twofold. First we have constituted a virtual multi-cluster environment based on processors of the computing platform on which each linear sub-system issued from the splitting is solved in parallel by a cluster of processors. Second, we have implemented the outer iteration of the multisplitting method as a Krylov subspace method which minimizes some error function. This increases the convergence and improves the scalability of the multisplitting method. - -We have tested our multisplitting method to solve the sparse linear system issued from the discretization of a 3D Poisson problem. We have compared its performances to the classical GMRES method on a supercomputer composed of 2048 to 8192 cores. The experimental results showed that the multisplitting method is about 4 to 6 times faster than the GMRES method for different sizes of the problem split into 2 or 4 blocks when using multisplitting method. Indeed, the GMRES method has difficulties to scale with many cores while the Krylov multisplitting method allows to hide latency and reduce the inter-cluster communications. - -In future works, we plan to conduct experiments on larger number of cores and test the scalability of our Krylov multisplitting method. It would be interesting to validate its performances to solve other linear/nonlinear and symmetric/nonsymmetric problems. Moreover, we intend to develop multisplitting methods based on asynchronous iteration in which communications are overlapped by computations. These methods would be interesting for platforms composed of distant clusters interconnected by a high-latency network. In addition, we intend to investigate the convergence improvements of our method by using preconditioning techniques for Krylov iterative methods and multisplitting methods with overlapping blocks. - +We have implemented a Krylov multisplitting method to solve sparse linear +systems on large-scale computing platforms. We have developed a synchronous +two-stage method based on the block Jacobi multisaplitting which uses GMRES +iterative method as an inner iteration. Our contribution in this paper is +twofold. First we provide a multi cluster decomposition that allows us to choose +the appropriate size of the clusters according to the architecures of the +supercomputer. Second, we have implemented the outer iteration of the +multisplitting method as a Krylov subspace method which minimizes some error +function. This increases the convergence and improves the scalability of the +multisplitting method. + +We have tested our multisplitting method to solve the sparse linear system +issued from the discretization of a 3D Poisson problem. We have compared its +performances to the classical GMRES method on a supercomputer composed of 2,048 +to 8,192 cores. The experimental results showed that the multisplitting method is +about 4 to 6 times faster than the GMRES method for different sizes of the +problem split into 2 or 4 blocks when using multisplitting method. Indeed, the +GMRES method has difficulties to scale with many cores while the Krylov +multisplitting method allows to hide latency and reduce the inter-cluster +communications. + +In future works, we plan to conduct experiments on larger number of cores and +test the scalability of our Krylov multisplitting method. It would be +interesting to validate its performances to solve other linear/nonlinear and +symmetric/nonsymmetric problems. Moreover, we intend to develop multisplitting +methods based on asynchronous iteration in which communications are overlapped +by computations. These methods would be interesting for platforms composed of +distant clusters interconnected by a high-latency network. In addition, we +intend to investigate the convergence improvements of our method by using +preconditioning techniques for Krylov iterative methods and multisplitting +methods with overlapping blocks. + +\section{Acknowledgement} +The authors would like to thank Mark Bull of the EPCC his fruitful remarks and the facilities of HECToR. %Other applications (=> other matrices)\\ %Larger experiments\\