The preconditioned solver example.
This example depends on multigrid-preconditioned-solver, mixed-multigrid-solver.
This example shows how to use the mixed-precision multigrid preconditioner.
In this example, we first read in a matrix from a file. The preconditioned CG solver is enhanced with a mixed-precision multigrid preconditioner. The example features the generating time and runtime of the CG solver.
The commented program
Parallel graph match (Pgm) is the aggregate method introduced in the paper M.
Definition pgm.hpp:53
Print version information
const auto executor_string = argc >= 2 ? argv[1] : "reference";
static const version_info & get()
Returns an instance of version_info.
Definition version.hpp:140
Figure out where to run the code
std::map<std::string, std::function<std::shared_ptr<gko::Executor>()>>
exec_map{
{"cuda",
[] {
}},
{"hip",
[] {
}},
{"dpcpp",
[] {
0, gko::ReferenceExecutor::create());
}},
{"reference", [] { return gko::ReferenceExecutor::create(); }}};
static std::shared_ptr< CudaExecutor > create(int device_id, std::shared_ptr< Executor > master, bool device_reset, allocation_mode alloc_mode=default_cuda_alloc_mode, CUstream_st *stream=nullptr)
Creates a new CudaExecutor.
static std::shared_ptr< DpcppExecutor > create(int device_id, std::shared_ptr< Executor > master, std::string device_type="all", dpcpp_queue_property property=dpcpp_queue_property::in_order)
Creates a new DpcppExecutor.
static std::shared_ptr< HipExecutor > create(int device_id, std::shared_ptr< Executor > master, bool device_reset, allocation_mode alloc_mode=default_hip_alloc_mode, CUstream_st *stream=nullptr)
Creates a new HipExecutor.
static std::shared_ptr< OmpExecutor > create(std::shared_ptr< CpuAllocatorBase > alloc=std::make_shared< CpuAllocator >())
Creates a new OmpExecutor.
Definition executor.hpp:1345
executor where Ginkgo will perform the computation
const auto exec = exec_map.at(executor_string)();
const int mixed_int = argc >= 3 ? std::atoi(argv[2]) : 1;
const bool use_mixed = mixed_int != 0;
std::cout << "Using mixed precision? " << use_mixed << std::endl;
Read data
std::unique_ptr< MatrixType > read(StreamType &&is, MatrixArgs &&... args)
Reads a matrix stored in matrix market format from an input stream.
Definition mtx_io.hpp:160
Create RHS as 1 and initial guess as 0
auto host_x = vec::create(exec->get_master(),
gko::dim<2>(size, 1));
auto host_b = vec::create(exec->get_master(),
gko::dim<2>(size, 1));
for (auto i = 0; i < size; i++) {
host_x->at(i, 0) = 0.;
host_b->at(i, 0) = 1.;
}
auto x = vec::create(exec);
auto b = vec::create(exec);
x->copy_from(host_x);
b->copy_from(host_b);
std::size_t size_type
Integral type used for allocation quantities.
Definition types.hpp:86
A type representing the dimensions of a multidimensional object.
Definition dim.hpp:27
Calculate initial residual by overwriting b
A->apply(one, x, neg_one, b);
b->compute_norm2(initres);
std::unique_ptr< Matrix > initialize(size_type stride, std::initializer_list< typename Matrix::value_type > vals, std::shared_ptr< const Executor > exec, TArgs &&... create_args)
Creates and initializes a column-vector.
Definition dense.hpp:1540
copy b again
Prepare the stopping criteria
auto iter_stop =
gko::share(gko::stop::Iteration::build().with_max_iters(100u).on(exec));
.with_baseline(gko::stop::mode::absolute)
.with_reduction_factor(tolerance)
.on(exec));
std::shared_ptr<const gko::log::Convergence<ValueType>> logger =
iter_stop->add_logger(logger);
tol_stop->add_logger(logger);
static std::unique_ptr< Convergence > create(std::shared_ptr< const Executor >, const mask_type &enabled_events=Logger::criterion_events_mask|Logger::iteration_complete_mask)
Creates a convergence logger.
Definition convergence.hpp:74
The ResidualNorm class is a stopping criterion which stops the iteration process when the actual resi...
Definition residual_norm.hpp:110
typename detail::remove_complex_s< T >::type remove_complex
Obtain the type which removed the complex of complex/scalar type or the template parameter of class b...
Definition math.hpp:326
detail::shared_type< OwningPointer > share(OwningPointer &&p)
Marks the object pointed to by p as shared.
Definition utils_helper.hpp:226
Create smoother factory (ir with bj)
auto inner_solver_gen =
gko::share(bj::build().with_max_block_size(1u).on(exec));
auto inner_solver_gen_f =
gko::share(bj_f::build().with_max_block_size(1u).on(exec));
ir::build()
.with_solver(inner_solver_gen)
.with_relaxation_factor(static_cast<ValueType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec));
ir_f::build()
.with_solver(inner_solver_gen_f)
.with_relaxation_factor(static_cast<MixedType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec));
Create MultigridLevel factory
auto mg_level_gen =
gko::share(pgm::build().with_deterministic(
true).on(exec));
auto mg_level_gen_f =
gko::share(pgm_f::build().with_deterministic(
true).on(exec));
Create CoarsestSolver factory
ir::build()
.with_solver(inner_solver_gen)
.with_relaxation_factor(static_cast<ValueType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(4u))
.on(exec));
ir_f::build()
.with_solver(inner_solver_gen_f)
.with_relaxation_factor(static_cast<MixedType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(4u))
.on(exec));
Create multigrid factory
std::shared_ptr<gko::LinOpFactory> multigrid_gen;
if (use_mixed) {
multigrid_gen =
mg::build()
.with_max_levels(10u)
.with_min_coarse_rows(2u)
.with_pre_smoother(smoother_gen, smoother_gen_f)
.with_post_uses_pre(true)
.with_mg_level(mg_level_gen, mg_level_gen_f)
Definition lin_op.hpp:118
The first (index 0) level will use the first mg_level_gen, smoother_gen which are the factories with ValueType. The rest of levels (>= 1) will use the second (index 1) mg_level_gen2 and smoother_gen2 which use the MixedType. The rest of levels will use different type than the normal multigrid.
return level >= 1 ? 1 : 0;
})
.with_coarsest_solver(coarsest_gen_f)
.with_default_initial_guess(
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec);
} else {
multigrid_gen =
mg::build()
.with_max_levels(10u)
.with_min_coarse_rows(2u)
.with_pre_smoother(smoother_gen)
.with_post_uses_pre(true)
.with_mg_level(mg_level_gen)
.with_coarsest_solver(coarsest_gen)
.with_default_initial_guess(
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec);
}
Create solver factory
auto solver_gen = cg::build()
.with_criteria(iter_stop, tol_stop)
.with_preconditioner(multigrid_gen)
.on(exec);
Create solver
std::chrono::nanoseconds gen_time(0);
auto gen_tic = std::chrono::steady_clock::now();
auto solver = solver_gen->generate(A);
exec->synchronize();
auto gen_toc = std::chrono::steady_clock::now();
gen_time +=
std::chrono::duration_cast<std::chrono::nanoseconds>(gen_toc - gen_tic);
Solve system
exec->synchronize();
std::chrono::nanoseconds time(0);
auto tic = std::chrono::steady_clock::now();
solver->apply(b, x);
exec->synchronize();
auto toc = std::chrono::steady_clock::now();
time += std::chrono::duration_cast<std::chrono::nanoseconds>(toc - tic);
Calculate residual
A->apply(one, x, neg_one, b);
b->compute_norm2(res);
std::cout << "Initial residual norm sqrt(r^T r): \n";
write(std::cout, initres);
std::cout << "Final residual norm sqrt(r^T r): \n";
write(std::cout, res);
Print solver statistics
std::cout << "CG iteration count: " << logger->get_num_iterations()
<< std::endl;
std::cout << "CG generation time [ms]: "
<< static_cast<double>(gen_time.count()) / 1000000.0 << std::endl;
std::cout << "CG execution time [ms]: "
<< static_cast<double>(time.count()) / 1000000.0 << std::endl;
std::cout << "CG execution time per iteration[ms]: "
<< static_cast<double>(time.count()) / 1000000.0 /
logger->get_num_iterations()
<< std::endl;
}
Results
This is the expected output:
Initial residual norm sqrt(r^T r):
%%MatrixMarket matrix array real general
1 1
4.3589
Final residual norm sqrt(r^T r):
%%MatrixMarket matrix array real general
1 1
1.69858e-09
CG iteration count: 39
CG generation time [ms]: 2.04293
CG execution time [ms]: 22.3874
CG execution time per iteration[ms]: 0.574036
Comments about programming and debugging
The plain program
#include <ginkgo/ginkgo.hpp>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <map>
#include <string>
int main(int argc, char* argv[])
{
using ValueType = double;
using MixedType = float;
using IndexType = int;
const auto executor_string = argc >= 2 ? argv[1] : "reference";
std::map<std::string, std::function<std::shared_ptr<gko::Executor>()>>
exec_map{
{"cuda",
[] {
}},
{"hip",
[] {
}},
{"dpcpp",
[] {
0, gko::ReferenceExecutor::create());
}},
{"reference", [] { return gko::ReferenceExecutor::create(); }}};
const auto exec = exec_map.at(executor_string)();
const int mixed_int = argc >= 3 ? std::atoi(argv[2]) : 1;
const bool use_mixed = mixed_int != 0;
std::cout << "Using mixed precision? " << use_mixed << std::endl;
auto host_x = vec::create(exec->get_master(),
gko::dim<2>(size, 1));
auto host_b = vec::create(exec->get_master(),
gko::dim<2>(size, 1));
for (auto i = 0; i < size; i++) {
host_x->at(i, 0) = 0.;
host_b->at(i, 0) = 1.;
}
auto x = vec::create(exec);
auto b = vec::create(exec);
x->copy_from(host_x);
b->copy_from(host_b);
A->apply(one, x, neg_one, b);
b->compute_norm2(initres);
b->copy_from(host_b);
auto iter_stop =
gko::share(gko::stop::Iteration::build().with_max_iters(100u).on(exec));
.with_baseline(gko::stop::mode::absolute)
.with_reduction_factor(tolerance)
.on(exec));
std::shared_ptr<const gko::log::Convergence<ValueType>> logger =
iter_stop->add_logger(logger);
tol_stop->add_logger(logger);
auto inner_solver_gen =
gko::share(bj::build().with_max_block_size(1u).on(exec));
auto inner_solver_gen_f =
gko::share(bj_f::build().with_max_block_size(1u).on(exec));
ir::build()
.with_solver(inner_solver_gen)
.with_relaxation_factor(static_cast<ValueType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec));
ir_f::build()
.with_solver(inner_solver_gen_f)
.with_relaxation_factor(static_cast<MixedType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec));
auto mg_level_gen =
gko::share(pgm::build().with_deterministic(
true).on(exec));
auto mg_level_gen_f =
gko::share(pgm_f::build().with_deterministic(
true).on(exec));
ir::build()
.with_solver(inner_solver_gen)
.with_relaxation_factor(static_cast<ValueType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(4u))
.on(exec));
ir_f::build()
.with_solver(inner_solver_gen_f)
.with_relaxation_factor(static_cast<MixedType>(0.9))
.with_criteria(gko::stop::Iteration::build().with_max_iters(4u))
.on(exec));
std::shared_ptr<gko::LinOpFactory> multigrid_gen;
if (use_mixed) {
multigrid_gen =
mg::build()
.with_max_levels(10u)
.with_min_coarse_rows(2u)
.with_pre_smoother(smoother_gen, smoother_gen_f)
.with_post_uses_pre(true)
.with_mg_level(mg_level_gen, mg_level_gen_f)
return level >= 1 ? 1 : 0;
})
.with_coarsest_solver(coarsest_gen_f)
.with_default_initial_guess(
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec);
} else {
multigrid_gen =
mg::build()
.with_max_levels(10u)
.with_min_coarse_rows(2u)
.with_pre_smoother(smoother_gen)
.with_post_uses_pre(true)
.with_mg_level(mg_level_gen)
.with_coarsest_solver(coarsest_gen)
.with_default_initial_guess(
.with_criteria(gko::stop::Iteration::build().with_max_iters(1u))
.on(exec);
}
auto solver_gen = cg::build()
.with_criteria(iter_stop, tol_stop)
.with_preconditioner(multigrid_gen)
.on(exec);
std::chrono::nanoseconds gen_time(0);
auto gen_tic = std::chrono::steady_clock::now();
auto solver = solver_gen->generate(A);
exec->synchronize();
auto gen_toc = std::chrono::steady_clock::now();
gen_time +=
std::chrono::duration_cast<std::chrono::nanoseconds>(gen_toc - gen_tic);
exec->synchronize();
std::chrono::nanoseconds time(0);
auto tic = std::chrono::steady_clock::now();
exec->synchronize();
auto toc = std::chrono::steady_clock::now();
time += std::chrono::duration_cast<std::chrono::nanoseconds>(toc - tic);
A->apply(one, x, neg_one, b);
b->compute_norm2(res);
std::cout << "Initial residual norm sqrt(r^T r): \n";
write(std::cout, initres);
std::cout << "Final residual norm sqrt(r^T r): \n";
std::cout << "CG iteration count: " << logger->get_num_iterations()
<< std::endl;
std::cout << "CG generation time [ms]: "
<< static_cast<double>(gen_time.count()) / 1000000.0 << std::endl;
std::cout << "CG execution time [ms]: "
<< static_cast<double>(time.count()) / 1000000.0 << std::endl;
std::cout << "CG execution time per iteration[ms]: "
<< static_cast<double>(time.count()) / 1000000.0 /
logger->get_num_iterations()
<< std::endl;
}
CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matr...
Definition sparsity_csr.hpp:22
Dense is a matrix format which explicitly stores all values of the matrix.
Definition sparsity_csr.hpp:26
A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal...
Definition jacobi.hpp:190
CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for...
Definition cg.hpp:51
FCG or the flexible conjugate gradient method is an iterative type Krylov subspace method which is su...
Definition fcg.hpp:56
Iterative refinement (IR) is an iterative method that uses another coarse method to approximate the e...
Definition ir.hpp:86
Multigrid methods have a hierarchy of many levels, whose corase level is a subset of the fine level,...
Definition multigrid.hpp:111
constexpr T one()
Returns the multiplicative identity for T.
Definition math.hpp:775
void write(StreamType &&os, MatrixPtrType &&matrix, layout_type layout=detail::mtx_io_traits< std::remove_cv_t< detail::pointee< MatrixPtrType > > >::default_layout)
Writes a matrix into an output stream in matrix market format.
Definition mtx_io.hpp:296