From 2eafea1e56bc53933bfcefcfe64ab6be5d34d42a Mon Sep 17 00:00:00 2001 From: Wolfgang Bangerth Date: Sat, 18 May 2019 08:38:44 -0600 Subject: [PATCH] Update the rest of the code. --- examples/step-64/step-64.cu | 115 +++++++++++++++++++++++++----------- 1 file changed, 82 insertions(+), 33 deletions(-) diff --git a/examples/step-64/step-64.cu b/examples/step-64/step-64.cu index 959d68514c..67558f8f81 100644 --- a/examples/step-64/step-64.cu +++ b/examples/step-64/step-64.cu @@ -255,7 +255,9 @@ namespace Step64 // ghost and artificial cells. (See the glossary entries on // @ref @ref GlossGhostCell "ghost cells" // and - // @ref GlossArtificialCell "artificial cells".) + // @ref GlossArtificialCell "artificial cells".) These will simply + // be ignored in the loop over all cells in the `vmult()` function + // below. template HelmholtzOperator::HelmholtzOperator( const DoFHandler & dof_handler, @@ -281,6 +283,10 @@ namespace Step64 } + // The key step then is to use all of the previous classes to loop over + // all cells to perform the matrix-vector product. We implement this + // in the next function. + // // When applying the Helmholtz operator, we have to be careful to handle // boundary conditions correctly. Since the local operator doesn't know about // constraints, we have to copy the correct values from the source to the @@ -299,9 +305,12 @@ namespace Step64 } - // This class defines the usual framework we use for tutorial programs. The - // only point worth commenting on the solve() function and the choice of - // vector types. + // @sect3{Class HelmholtzProblem} + + // This is the main class of this program. It defines the usual + // framework we use for tutorial programs. The only point worth + // commenting on is the `solve()` function and the choice of vector + // types. template class HelmholtzProblem { @@ -332,16 +341,18 @@ namespace Step64 AffineConstraints constraints; std::unique_ptr> system_matrix_dev; - // Since all the operations in the solve functions are executed on the - // graphic card it is necessary for the vectors used to store their values + // Since all the operations in the `solve()` function are executed on the + // graphics card, it is necessary for the vectors used to store their values // on the GPU as well. LinearAlgebra::distributed::Vector can be told which // memory space to use. There is also LinearAlgebra::CUDAWrappers::Vector // that always uses GPU memory storage but doesn't work with MPI. It might // be worth noticing that the communication between different MPI processes // can be improved if the MPI implementation is CUDA-aware and the configure - // flag DEAL_II_MPI_WITH_CUDA_SUPPORT is enabled. + // flag `DEAL_II_MPI_WITH_CUDA_SUPPORT` is enabled. (The value of this + // flag is automatically determined at the time you call `cmake` when + // installing deal.II.) // - // Here, we also have a finite element vector with CPU storage such that we + // In addition, we also keep a solution vector with CPU storage such that we // can view and display the solution as usual. LinearAlgebra::distributed::Vector ghost_solution_host; @@ -354,8 +365,8 @@ namespace Step64 // The implementation of all the remaining functions of this class apart from - // Helmholtzproblem::solve() doesn't contain anything new and we won't further - // comment on it. + // `Helmholtzproblem::solve()` doesn't contain anything new and we won't + // further comment much on the overall approach. template HelmholtzProblem::HelmholtzProblem() : mpi_communicator(MPI_COMM_WORLD) @@ -384,6 +395,7 @@ namespace Step64 Functions::ZeroFunction(), constraints); constraints.close(); + system_matrix_dev.reset( new HelmholtzOperator(dof_handler, constraints)); @@ -396,6 +408,23 @@ namespace Step64 + // Unlike programs such as step-4 or step-6, we will not have to + // assemble the whole linear system but only the right hand side + // vector. This looks in essence like we did in step-4, for example, + // but we have to pay attention to using the right constraints + // object when copying local contributions into the global + // vector. In particular, we need to make sure the entries that + // correspond to boundary nodes are properly zeroed out. This is + // necessary for CG to converge. (Another solution would be to + // modify the `vmult()` function above in such a way that we pretend + // the source vector has zero entries by just not taking them into + // account in matrix-vector products. But the approach used here is + // simpler.) + // + // At the end of the function, we can't directly copy the values + // from the host to the device but need to use an intermediate + // object of type LinearAlgebra::ReadWriteVector to construct the + // correct communication pattern necessary. template void HelmholtzProblem::assemble_rhs() { @@ -431,12 +460,6 @@ namespace Step64 fe_values.JxW(q_index)); } - // Finally, transfer the contributions from @p cell_rhs into the global - // objects. Set the constraints to zero. This is necessary for CG to - // converge since the ansatz and solution space have these degrees of - // freedom constrained as well. - // The other solution is modifying vmult() so that the source - // vector sets the contrained dof to zero. cell->get_dof_indices(local_dof_indices); constraints.distribute_local_to_global(cell_rhs, local_dof_indices, @@ -444,9 +467,6 @@ namespace Step64 } system_rhs_host.compress(VectorOperation::add); - // We can't directly copy the values from the host to the device but need to - // use an intermediate object of type LinearAlgebra::ReadWriteVector to - // construct the correct communication pattern. LinearAlgebra::ReadWriteVector rw_vector(locally_owned_dofs); rw_vector.import(system_rhs_host, VectorOperation::insert); system_rhs_dev.import(rw_vector, VectorOperation::insert); @@ -456,11 +476,16 @@ namespace Step64 // This solve() function finally contains the calls to the new classes // previously dicussed. Here we don't use any preconditioner, i.e. - // precondition by the identity, to focus just on the peculiarities of the - // CUDAWrappers::MatrixFree framework. Of course, in a real application the - // choice of a suitable preconditioner is crucial but we have at least the + // precondition by the identity matrix, to focus just on the peculiarities of + // the CUDAWrappers::MatrixFree framework. Of course, in a real application + // the choice of a suitable preconditioner is crucial but we have at least the // same restrictions as in step-37 since matrix entries are computed on the // fly and not stored. + // + // After solving the linear system in the first part of the function, we + // copy the solution from the device to the host to be able to view its + // values and display it in `output_results()`. This transfer works the same + // as at the end of the previous function. template void HelmholtzProblem::solve() { @@ -475,8 +500,6 @@ namespace Step64 pcout << " Solved in " << solver_control.last_step() << " iterations." << std::endl; - // Copy the solution from the device to the host to be able to view its - // values and display it in output_results(). LinearAlgebra::ReadWriteVector rw_vector(locally_owned_dofs); rw_vector.import(solution_dev, VectorOperation::insert); ghost_solution_host.import(rw_vector, VectorOperation::insert); @@ -488,6 +511,18 @@ namespace Step64 // The output results function is as usual since we have already copied the // values back from the GPU to the CPU. + // + // While we're already doing something with the function, we might + // as well compute the $L_2$ norm of the solution. We do this by + // calling VectorTools::integrate_difference(). That function is + // meant to compute the error by evaluating the difference between + // the numerical solution (given by a vector of values for the + // degrees of freedom) and an object representing the exact + // solution. But we can easily compute the $L_2$ norm of the + // solution by passing in a zero function instead. That is, instead + // of evaluating the error $\|u_h-u\|_{L_2(\Omega)}$, we are just + // evaluating $\|u_h-\|_{L_2(\Omega)}=\|u_h\|_{L_2(\Omega)}$ + // instead. template void HelmholtzProblem::output_results( const unsigned int cycle) const @@ -565,6 +600,25 @@ namespace Step64 } } // namespace Step64 + +// @sect3{The main() function} + +// Finally for the `main()` function. By default, all the MPI ranks +// will try to access the device with number 0, which we assume to be +// the GPU device associated with the CPU on which a particular MPI +// rank runs. This works, but if we are running with MPI support it +// may be that multiple MPI processes are running on the same machine +// (for example, one per CPU core) and then they would all want to +// access the same GPU on that machine. If there is only one GPU in +// the machine, there is nothing we can do about it: All MPI ranks on +// that machine need to share it. But if there are more than one GPU, +// then it is better to address different graphic cards for different +// processes. The choice below is based on the MPI proccess id by +// assigning GPUs round robin to GPU ranks. (To work correctly, this +// scheme assumes that the MPI ranks on one machine are +// consecutive. If that were not the case, then the rank-GPU +// association may just not be optimal.) To make this work, MPI needs +// to be initialized before using this function. int main(int argc, char *argv[]) { try @@ -573,22 +627,17 @@ int main(int argc, char *argv[]) Utilities::MPI::MPI_InitFinalize mpi_init(argc, argv, 1); - // By default, all the ranks will try to access the device 0. - // If we are running with MPI support it is better to address different - // graphic cards for different processes even if only one node is used. - // The choice below is based on the MPI proccess id. MPI needs to be - // initialized before using this function. int n_devices = 0; cudaError_t cuda_error_code = cudaGetDeviceCount(&n_devices); AssertCuda(cuda_error_code); - const unsigned int my_id = + const unsigned int my_mpi_id = Utilities::MPI::this_mpi_process(MPI_COMM_WORLD); - const int device_id = my_id % n_devices; + const int device_id = my_mpi_id % n_devices; cuda_error_code = cudaSetDevice(device_id); AssertCuda(cuda_error_code); - HelmholtzProblem<3, 3> helmhotz_problem; - helmhotz_problem.run(); + HelmholtzProblem<3, 3> helmholtz_problem; + helmholtz_problem.run(); } catch (std::exception &exc) { -- 2.39.5