mirror of
https://github.com/OPM/opm-simulators.git
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265 lines
8.1 KiB
C++
265 lines
8.1 KiB
C++
/*
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Copyright 2014 Dr. Markus Blatt - HPC-Simulation-Software & Services
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This file is part of the Open Porous Media project (OPM).
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OPM is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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OPM is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with OPM. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <config.h>
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#if HAVE_DYNAMIC_BOOST_TEST
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#define BOOST_TEST_DYN_LINK
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#endif
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#define NVERBOSE // to suppress our messages when throwing
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#define BOOST_TEST_MODULE OPM-ParallelIterativeSolverTest
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#include <boost/test/unit_test.hpp>
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// MPI header
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#if HAVE_MPI
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#include <mpi.h>
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#include <dune/common/parallel/indexset.hh>
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#include <dune/common/parallel/communicator.hh>
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#include <dune/common/parallel/remoteindices.hh>
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#include <dune/common/mpicollectivecommunication.hh>
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#include <dune/common/collectivecommunication.hh>
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#include <dune/istl/owneroverlapcopy.hh>
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#include <opm/core/linalg/ParallelIstlInformation.hpp>
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#else
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#error "This file needs to compiled with MPI support!"
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#endif
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#include <opm/core/linalg/LinearSolverFactory.hpp>
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#include <opm/core/utility/parameters/ParameterGroup.hpp>
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#include <memory>
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#include <cstdlib>
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#include <string>
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struct MPIFixture {
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MPIFixture()
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{
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int m_argc = boost::unit_test::framework::master_test_suite().argc;
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char** m_argv = boost::unit_test::framework::master_test_suite().argv;
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MPI_Init(&m_argc, &m_argv);
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}
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~MPIFixture()
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{
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MPI_Finalize();
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}
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};
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BOOST_GLOBAL_FIXTURE(MPIFixture);
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struct MyMatrix
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{
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MyMatrix(std::size_t rows, std::size_t nnz)
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: data(nnz, 0.0), rowStart(rows+1, -1),
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colIndex(nnz, -1)
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{}
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MyMatrix()
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: data(), rowStart(), colIndex()
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{}
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std::vector<double> data;
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std::vector<int> rowStart;
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std::vector<int> colIndex;
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};
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typedef int LocalId;
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typedef int GlobalId;
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typedef Dune::OwnerOverlapCopyCommunication<GlobalId,LocalId> Communication;
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typedef Dune::OwnerOverlapCopyAttributeSet GridAttributes;
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typedef GridAttributes::AttributeSet GridFlag;
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typedef Dune::ParallelLocalIndex<GridFlag> LocalIndex;
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/// \brief Sets up a paralle Laplacian.
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///
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/// The process stores the unknowns with indices in the range [start, end).
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/// As we use an overlapping domain decomposition, the process owns the indices
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/// in the range [istart, iend]. If we would only used the indices in this range then
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/// they form a partitioning of the whole index set.
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/// \tparam I The type of the parallel index set (for convenience)
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/// \param indexset The parallel index set for marking owner and copy region.
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/// \param N The global number of unknowns of the system.
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/// \param start The first index stored on this process
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/// \param end One past the last index stored on this process
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/// \param istart The first index that the process owns.
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/// \param iend One past the last index the process owns.
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template<class I>
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std::shared_ptr<MyMatrix> create1DLaplacian(I& indexset, int N, int start, int end,
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int istart, int iend)
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{
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indexset.beginResize();
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MyMatrix* mm=new MyMatrix(end-start, (end-start)*3);
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int nnz=0;
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mm->rowStart[0]=0;
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assert(start==0||start<istart);
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assert(end==N||iend<end);
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for(int row=start, localRow=0; row<end; row++, localRow++)
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{
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if(row<istart || row>=iend)
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{
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// We are in the overlap region of the grid
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// therefore we setup the system such that
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// right hand side will equal the left hand side
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// of the linear system.
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if(localRow>0)
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{
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mm->colIndex[nnz]=localRow-1;
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mm->data[nnz++]=0;
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}
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mm->colIndex[nnz]=localRow;
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mm->data[nnz++]=1.0;
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indexset.add(row, LocalIndex(localRow, GridAttributes::copy, true));
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if(localRow<end-1)
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{
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mm->colIndex[nnz]=localRow+1;
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mm->data[nnz++]=0;
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}
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mm->rowStart[localRow+1]=nnz;
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continue;
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}
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double dval=0;
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if(row>0)
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{
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mm->colIndex[nnz]=localRow-1;
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mm->data[nnz++]=-1;
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dval+=1;
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}
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mm->colIndex[nnz]=localRow;
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mm->data[nnz++]=2;//dval+(row<N-1);
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if(row<N-1)
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{
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mm->colIndex[nnz]=localRow+1;
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mm->data[nnz++]=-1;
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dval+=1;
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}
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mm->rowStart[localRow+1]=nnz;
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indexset.add(row, LocalIndex(localRow, GridAttributes::owner, true));
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}
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mm->data.resize(nnz);
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mm->colIndex.resize(nnz);
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indexset.endResize();
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return std::shared_ptr<MyMatrix>(mm);
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}
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template<class O>
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void createRandomVectors(O& pinfo, int NN, std::vector<double>& x, std::vector<double>& b,
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const MyMatrix& mat)
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{
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x.resize(NN);
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for(auto entry=x.begin(), end =x.end(); entry!=end; ++entry)
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*entry=((double) (rand()%100))/10.0;
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pinfo.copyOwnerToAll(x,x);
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b.resize(NN);
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// Construct the right hand side as b=A*x
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std::fill(b.begin(), b.end(), 0.0);
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for(std::size_t row=0; row<mat.rowStart.size()-1; ++row)
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{
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for(int i=mat.rowStart[row], end=mat.rowStart[row+1]; i!=end; ++i)
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{
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b[row]+= mat.data[i]*x[mat.colIndex[i]];
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}
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}
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pinfo.copyOwnerToAll(b,b);
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}
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void run_test(const Opm::parameter::ParameterGroup& param)
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{
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int N=100;
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int procs, rank;
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MPI_Comm_rank(MPI_COMM_WORLD, &rank);
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MPI_Comm_size(MPI_COMM_WORLD, &procs);
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int n = N/procs; // number of unknowns per process
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int bigger = N%procs; // number of process with n+1 unknows
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int start, end, istart, iend;
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// Compute owner region
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if(rank<bigger) {
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start = rank*(n+1);
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end = start+(n+1);
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}else{
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start = bigger*(n+1) + (rank-bigger) * n;
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end = start+n;
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}
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// Compute owner region
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if(rank<bigger) {
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istart = rank*(n+1);
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iend = start+(n+1);
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}else{
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istart = bigger*(n+1) + (rank-bigger) * n;
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iend = start+n;
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}
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// Compute overlap region
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if(istart>0)
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start = istart - 1;
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else
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start = istart;
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if(iend<N)
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end = iend + 1;
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else
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end = iend;
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Opm::ParallelISTLInformation comm(MPI_COMM_WORLD);
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auto mat = create1DLaplacian(*comm.indexSet(), N, start, end, istart, iend);
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std::vector<double> x(end-start), b(end-start);
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createRandomVectors(comm, end-start, x, b, *mat);
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std::vector<double> exact(x);
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std::fill(x.begin(), x.end(), 0.0);
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Opm::LinearSolverFactory ls(param);
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boost::any anyComm(comm);
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ls.solve(b.size(), mat->data.size(), &(mat->rowStart[0]),
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&(mat->colIndex[0]), &(mat->data[0]), &(b[0]),
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&(x[0]), anyComm);
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}
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#ifdef HAVE_DUNE_ISTL
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BOOST_AUTO_TEST_CASE(CGAMGTest)
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{
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Opm::parameter::ParameterGroup param;
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param.insertParameter(std::string("linsolver"), std::string("istl"));
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param.insertParameter(std::string("linsolver_type"), std::string("1"));
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param.insertParameter(std::string("linsolver_max_iterations"), std::string("200"));
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run_test(param);
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}
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BOOST_AUTO_TEST_CASE(CGILUTest)
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{
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Opm::parameter::ParameterGroup param;
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param.insertParameter(std::string("linsolver"), std::string("istl"));
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param.insertParameter(std::string("linsolver_type"), std::string("0"));
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param.insertParameter(std::string("linsolver_max_iterations"), std::string("200"));
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run_test(param);
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}
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BOOST_AUTO_TEST_CASE(BiCGILUTest)
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{
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Opm::parameter::ParameterGroup param;
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param.insertParameter(std::string("linsolver"), std::string("istl"));
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param.insertParameter(std::string("linsolver_type"), std::string("2"));
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param.insertParameter(std::string("linsolver_max_iterations"), std::string("200"));
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run_test(param);
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}
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#endif
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