Ginkgo  Generated from pipelines/224724463 branch based on develop. Ginkgo version 1.3.0
A numerical linear algebra library targeting many-core architectures
The preconditioned-solver program

The preconditioned solver example..

This example depends on simple-solver.

Table of contents
  1. Introduction
  2. The commented program
  1. Results
  2. The plain program

Introduction

About the example

The commented program

#include <ginkgo/ginkgo.hpp>
#include <fstream>
#include <iostream>
#include <map>
#include <string>
int main(int argc, char *argv[])
{

Some shortcuts

using ValueType = double;
using RealValueType = gko::remove_complex<ValueType>;
using IndexType = int;

Print version information

std::cout << gko::version_info::get() << std::endl;

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);
}

Figure out where to run the code

const auto executor_string = argc >= 2 ? argv[1] : "reference";
std::map<std::string, std::function<std::shared_ptr<gko::Executor>()>>
exec_map{
{"omp", [] { return gko::OmpExecutor::create(); }},
{"cuda",
[] {
true);
}},
{"hip",
[] {
true);
}},
{"dpcpp",
[] {
}},
{"reference", [] { return gko::ReferenceExecutor::create(); }}};

executor where Ginkgo will perform the computation

const auto exec = exec_map.at(executor_string)(); // throws if not valid

Read data

auto A = share(gko::read<mtx>(std::ifstream("data/A.mtx"), exec));
auto b = gko::read<vec>(std::ifstream("data/b.mtx"), exec);
auto x = gko::read<vec>(std::ifstream("data/x0.mtx"), exec);
const RealValueType reduction_factor{1e-7};

Create solver factory

auto solver_gen =
cg::build()
.with_criteria(
gko::stop::Iteration::build().with_max_iters(20u).on(exec),
.with_reduction_factor(reduction_factor)
.on(exec))

Add preconditioner, these 2 lines are the only difference from the simple solver example

.with_preconditioner(bj::build().with_max_block_size(8u).on(exec))
.on(exec);

Create solver

auto solver = solver_gen->generate(A);

Solve system

solver->apply(lend(b), lend(x));

Print solution

std::cout << "Solution (x):\n";
write(std::cout, lend(x));

Calculate residual

auto one = gko::initialize<vec>({1.0}, exec);
auto neg_one = gko::initialize<vec>({-1.0}, exec);
auto res = gko::initialize<real_vec>({0.0}, exec);
A->apply(lend(one), lend(x), lend(neg_one), lend(b));
b->compute_norm2(lend(res));
std::cout << "Residual norm sqrt(r^T r):\n";
write(std::cout, lend(res));
}

Results

This is the expected output:

Solution (x):
%%MatrixMarket matrix array real general
19 1
0.252218
0.108645
0.0662811
0.0630433
0.0384088
0.0396536
0.0402648
0.0338935
0.0193098
0.0234653
0.0211499
0.0196413
0.0199151
0.0181674
0.0162722
0.0150714
0.0107016
0.0121141
0.0123025
Residual norm sqrt(r^T r):
%%MatrixMarket matrix array real general
1 1
4.82005e-08

Comments about programming and debugging

The plain program

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#include <ginkgo/ginkgo.hpp>
#include <fstream>
#include <iostream>
#include <map>
#include <string>
int main(int argc, char *argv[])
{
using ValueType = double;
using RealValueType = gko::remove_complex<ValueType>;
using IndexType = int;
std::cout << gko::version_info::get() << std::endl;
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{
{"omp", [] { return gko::OmpExecutor::create(); }},
{"cuda",
[] {
true);
}},
{"hip",
[] {
true);
}},
{"dpcpp",
[] {
}},
{"reference", [] { return gko::ReferenceExecutor::create(); }}};
const auto exec = exec_map.at(executor_string)(); // throws if not valid
auto A = share(gko::read<mtx>(std::ifstream("data/A.mtx"), exec));
auto b = gko::read<vec>(std::ifstream("data/b.mtx"), exec);
auto x = gko::read<vec>(std::ifstream("data/x0.mtx"), exec);
const RealValueType reduction_factor{1e-7};
auto solver_gen =
cg::build()
.with_criteria(
gko::stop::Iteration::build().with_max_iters(20u).on(exec),
.with_reduction_factor(reduction_factor)
.on(exec))
.with_preconditioner(bj::build().with_max_block_size(8u).on(exec))
.on(exec);
auto solver = solver_gen->generate(A);
solver->apply(lend(b), lend(x));
std::cout << "Solution (x):\n";
write(std::cout, lend(x));
auto one = gko::initialize<vec>({1.0}, exec);
auto neg_one = gko::initialize<vec>({-1.0}, exec);
auto res = gko::initialize<real_vec>({0.0}, exec);
A->apply(lend(one), lend(x), lend(neg_one), lend(b));
b->compute_norm2(lend(res));
std::cout << "Residual norm sqrt(r^T r):\n";
write(std::cout, lend(res));
}
gko::matrix::Csr
CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matr...
Definition: coo.hpp:51
gko::real
constexpr std::enable_if_t<!is_complex_s< T >::value, T > real(const T &x)
Returns the real part of the object.
Definition: math.hpp:817
gko::HipExecutor::create
static std::shared_ptr< HipExecutor > create(int device_id, std::shared_ptr< Executor > master, bool device_reset=false)
Creates a new HipExecutor.
gko::layout_type::array
The matrix should be written as dense matrix in column-major order.
gko::matrix::Dense
Dense is a matrix format which explicitly stores all values of the matrix.
Definition: coo.hpp:55
gko::CudaExecutor::create
static std::shared_ptr< CudaExecutor > create(int device_id, std::shared_ptr< Executor > master, bool device_reset=false)
Creates a new CudaExecutor.
gko::OmpExecutor::create
static std::shared_ptr< OmpExecutor > create()
Creates a new OmpExecutor.
Definition: executor.hpp:909
gko::stop::ResidualNormReduction
The ResidualNormReduction class is a stopping criterion which stops the iteration process when the re...
Definition: residual_norm.hpp:113
gko::write
void write(StreamType &&os, MatrixType *matrix, layout_type layout=layout_type::array)
Reads a matrix stored in matrix market format from an input stream.
Definition: mtx_io.hpp:134
gko::version_info::get
static const version_info & get()
Returns an instance of version_info.
Definition: version.hpp:168
gko::preconditioner::Jacobi
A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal...
Definition: jacobi.hpp:207
gko::lend
std::enable_if< detail::have_ownership_s< Pointer >::value, detail::pointee< Pointer > * >::type lend(const Pointer &p)
Returns a non-owning (plain) pointer to the object pointed to by p.
Definition: utils.hpp:253
gko::solver::Cg
CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for...
Definition: cg.hpp:72
gko::DpcppExecutor::create
static std::shared_ptr< DpcppExecutor > create(int device_id, std::shared_ptr< Executor > master, std::string device_type="all")
Creates a new DpcppExecutor.
gko::share
detail::shared_type< OwningPointer > share(OwningPointer &&p)
Marks the object pointed to by p as shared.
Definition: utils.hpp:210
gko::remove_complex
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:344
gko::one
constexpr T one()
Returns the multiplicative identity for T.
Definition: math.hpp:742