The iterative refinement solver example.
This example depends on simple-solver.
This example shows how to use the iterative refinement solver.
In this example, we first read in a matrix from file, then generate a right-hand side and an initial guess. An inaccurate CG solver is used as the inner solver to an iterative refinement (IR) method which solves a linear system. The example features the iteration count and runtime of the IR solver.
The commented program
#include <ginkgo/ginkgo.hpp>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <map>
#include <string>
int main(int argc, char* argv[])
{
Some shortcuts
using ValueType = double;
using IndexType = int;
CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matr...
Definition csr.hpp:146
Dense is a matrix format which explicitly stores all values of the matrix.
Definition dense.hpp:136
CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for...
Definition cg.hpp:76
Iterative refinement (IR) is an iterative method that uses another coarse method to approximate the e...
Definition ir.hpp:112
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:354
Print version information
static const version_info & get()
Returns an instance of version_info.
Definition version.hpp:168
Figure out where to run the code
if (argc == 2 && (std::string(argv[1]) == "--help")) {
std::cerr << "Usage: " << argv[0] << " [executor]" << std::endl;
std::exit(-1);
}
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",
[] {
}},
{"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:1373
executor where Ginkgo will perform the computation
const auto exec = exec_map.at(executor_string)();
Read data
constexpr T one()
Returns the multiplicative identity for T.
Definition math.hpp:803
Create RHS and initial guess as 1
for (auto i = 0; i < size; i++) {
host_x->at(i, 0) = 1.;
}
std::size_t size_type
Integral type used for allocation quantities.
Definition types.hpp:120
detail::cloned_type< Pointer > clone(const Pointer &p)
Creates a unique clone of the object pointed to by p.
Definition utils_helper.hpp:203
A type representing the dimensions of a multidimensional object.
Definition dim.hpp:55
Calculate initial residual by overwriting b
A->apply(one, x, neg_one, b);
b->compute_norm2(initres);
copy b again
Create solver factory
RealValueType outer_reduction_factor{1e-12};
RealValueType inner_reduction_factor{1e-2};
auto solver_gen =
ir::build()
.with_solver(cg::build().with_criteria(
.with_reduction_factor(inner_reduction_factor)))
.with_criteria(
gko::stop::Iteration::build().with_max_iters(max_iters),
.with_reduction_factor(outer_reduction_factor))
.on(exec);
The ResidualNorm class is a stopping criterion which stops the iteration process when the actual resi...
Definition residual_norm.hpp:138
Create solver
auto solver = solver_gen->generate(A);
std::shared_ptr<const gko::log::Convergence<ValueType>> logger =
solver->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:106
Solve system
exec->synchronize();
std::chrono::nanoseconds time(0);
auto tic = std::chrono::steady_clock::now();
solver->apply(b, x);
auto toc = std::chrono::steady_clock::now();
time += std::chrono::duration_cast<std::chrono::nanoseconds>(toc - tic);
Get residual
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 << "IR iteration count: " << logger->get_num_iterations()
<< std::endl;
std::cout << "IR execution time [ms]: "
<< static_cast<double>(time.count()) / 1000000.0 << std::endl;
}
Results
This is the expected output:
Initial residual norm sqrt(r^T r):
%%MatrixMarket matrix array real general
1 1
194.679
Final residual norm sqrt(r^T r):
%%MatrixMarket matrix array real general
1 1
4.23821e-11
IR iteration count: 24
IR execution time [ms]: 0.794962
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 IndexType = int;
if (argc == 2 && (std::string(argv[1]) == "--help")) {
std::cerr << "Usage: " << argv[0] << " [executor]" << std::endl;
std::exit(-1);
}
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",
[] {
}},
{"reference", [] { return gko::ReferenceExecutor::create(); }}};
const auto exec = exec_map.at(executor_string)();
for (auto i = 0; i < size; i++) {
host_x->at(i, 0) = 1.;
}
A->apply(one, x, neg_one, b);
b->compute_norm2(initres);
b->copy_from(host_x);
RealValueType outer_reduction_factor{1e-12};
RealValueType inner_reduction_factor{1e-2};
auto solver_gen =
ir::build()
.with_solver(cg::build().with_criteria(
.with_reduction_factor(inner_reduction_factor)))
.with_criteria(
gko::stop::Iteration::build().with_max_iters(max_iters),
.with_reduction_factor(outer_reduction_factor))
.on(exec);
auto solver = solver_gen->generate(A);
std::shared_ptr<const gko::log::Convergence<ValueType>> logger =
exec->synchronize();
std::chrono::nanoseconds time(0);
auto tic = std::chrono::steady_clock::now();
auto toc = std::chrono::steady_clock::now();
time += std::chrono::duration_cast<std::chrono::nanoseconds>(toc - tic);
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 << "IR iteration count: " << logger->get_num_iterations()
<< std::endl;
std::cout << "IR execution time [ms]: "
<< static_cast<double>(time.count()) / 1000000.0 << std::endl;
}
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:324
detail::shared_type< OwningPointer > share(OwningPointer &&p)
Marks the object pointed to by p as shared.
Definition utils_helper.hpp:254