void run_amg(viennacl::linalg::cg_tag & cg_solver,
boost::numeric::ublas::vector<ScalarType> & ublas_vec,
boost::numeric::ublas::vector<ScalarType> & ublas_result,
boost::numeric::ublas::compressed_matrix<ScalarType> & ublas_matrix,
viennacl::vector<ScalarType> & vcl_vec,
viennacl::vector<ScalarType> & vcl_result,
viennacl::compressed_matrix<ScalarType> & vcl_compressed_matrix,
std::string info,
viennacl::linalg::amg_tag & amg_tag)
{
viennacl::linalg::amg_precond<boost::numeric::ublas::compressed_matrix<ScalarType> > ublas_amg = viennacl::linalg::amg_precond<boost::numeric::ublas::compressed_matrix<ScalarType> > (ublas_matrix, amg_tag);
boost::numeric::ublas::vector<ScalarType> avgstencil;
unsigned int coarselevels = amg_tag.get_coarselevels();
std::cout << "-- CG with AMG preconditioner, " << info << " --" << std::endl;
std::cout << " * Setup phase (ublas types)..." << std::endl;
// Coarse level measure might have been changed during setup. Reload!
ublas_amg.tag().set_coarselevels(coarselevels);
ublas_amg.setup();
std::cout << " * Operator complexity: " << ublas_amg.calc_complexity(avgstencil) << std::endl;
amg_tag.set_coarselevels(coarselevels);
viennacl::linalg::amg_precond<viennacl::compressed_matrix<ScalarType> > vcl_amg = viennacl::linalg::amg_precond<viennacl::compressed_matrix<ScalarType> > (vcl_compressed_matrix, amg_tag);
std::cout << " * Setup phase (ViennaCL types)..." << std::endl;
vcl_amg.tag().set_coarselevels(coarselevels);
vcl_amg.setup();
std::cout << " * CG solver (ublas types)..." << std::endl;
run_solver(ublas_matrix, ublas_vec, ublas_result, cg_solver, ublas_amg);
std::cout << " * CG solver (ViennaCL types)..." << std::endl;
run_solver(vcl_compressed_matrix, vcl_vec, vcl_result, cg_solver, vcl_amg);
}
/**
* cb_dialog_solve_clicked:
* @button:
* @state:
*
*
**/
static void
cb_dialog_solve_clicked (G_GNUC_UNUSED GtkWidget *button,
SolverState *state)
{
GnmSolverResult *res;
GnmSolverParameters *param = state->sheet->solver_parameters;
GError *err = NULL;
if (state->warning_dialog != NULL) {
gtk_widget_destroy (state->warning_dialog);
state->warning_dialog = NULL;
}
extract_settings (state);
if (!gnm_solver_param_valid (param, &err)) {
GtkWidget *top = gtk_widget_get_toplevel (state->dialog);
go_gtk_notice_dialog (GTK_WINDOW (top), GTK_MESSAGE_ERROR,
"%s", err->message);
goto out;
}
check_for_changed_options (state);
res = run_solver (state, param);
gnm_app_recalc ();
if (res != NULL) {
if ((res->quality == GNM_SOLVER_RESULT_OPTIMAL ||
res->quality == GNM_SOLVER_RESULT_FEASIBLE) &&
param->options.add_scenario)
solver_add_scenario (state, res,
param->options.scenario_name);
g_object_unref (res);
} else if (err) {
go_gtk_notice_nonmodal_dialog
(GTK_WINDOW (state->dialog),
&(state->warning_dialog),
GTK_MESSAGE_ERROR,
"%s", err->message);
}
out:
if (err)
g_error_free (err);
}
int main(int argc, char **argv) {
u32 nthreads = 0;
u32 ntrims = 0;
u32 nonce = 0;
u32 range = 1;
#ifdef SAVEEDGES
bool showcycle = 1;
#else
bool showcycle = 0;
#endif
char header[HEADERLEN];
u32 len;
bool allrounds = false;
int c;
memset(header, 0, sizeof(header));
while ((c = getopt (argc, argv, "ah:m:n:r:st:x:")) != -1) {
switch (c) {
case 'a':
allrounds = true;
break;
case 'h':
len = strlen(optarg);
assert(len <= sizeof(header));
memcpy(header, optarg, len);
break;
case 'x':
len = strlen(optarg)/2;
assert(len == sizeof(header));
for (u32 i=0; i<len; i++)
sscanf(optarg+2*i, "%2hhx", header+i);
break;
case 'n':
nonce = atoi(optarg);
break;
case 'r':
range = atoi(optarg);
break;
case 'm':
ntrims = atoi(optarg) & -2; // make even as required by solve()
break;
case 's':
showcycle = true;
break;
case 't':
nthreads = atoi(optarg);
break;
}
}
SolverParams params;
params.nthreads = nthreads;
params.ntrims = ntrims;
params.showcycle = showcycle;
params.allrounds = allrounds;
SolverCtx* ctx = create_solver_ctx(¶ms);
print_log("Looking for %d-cycle on cuckatoo%d(\"%s\",%d", PROOFSIZE, EDGEBITS, header, nonce);
if (range > 1)
print_log("-%d", nonce+range-1);
print_log(") with 50%% edges\n");
u64 sbytes = ctx->sharedbytes();
u32 tbytes = ctx->threadbytes();
int sunit,tunit;
for (sunit=0; sbytes >= 10240; sbytes>>=10,sunit++) ;
for (tunit=0; tbytes >= 10240; tbytes>>=10,tunit++) ;
print_log("Using %d%cB bucket memory at %lx,\n", sbytes, " KMGT"[sunit], (u64)ctx->trimmer.buckets);
print_log("%dx%d%cB thread memory at %lx,\n", params.nthreads, tbytes, " KMGT"[tunit], (u64)ctx->trimmer.tbuckets);
print_log("%d-way siphash, and %d buckets.\n", NSIPHASH, NX);
run_solver(ctx, header, sizeof(header), nonce, range, NULL, NULL);
destroy_solver_ctx(ctx);
}
int main()
{
//
// Print some device info
//
std::cout << std::endl;
std::cout << "----------------------------------------------" << std::endl;
std::cout << " Device Info" << std::endl;
std::cout << "----------------------------------------------" << std::endl;
#ifdef VIENNACL_WITH_OPENCL
std::cout << viennacl::ocl::current_device().info() << std::endl;
#endif
typedef float ScalarType; // feel free to change this to double if supported by your device
//
// Set up the matrices and vectors for the iterative solvers (cf. iterative.cpp)
//
boost::numeric::ublas::vector<ScalarType> ublas_vec, ublas_result;
boost::numeric::ublas::compressed_matrix<ScalarType> ublas_matrix;
viennacl::linalg::cg_tag cg_solver;
viennacl::linalg::amg_tag amg_tag;
viennacl::linalg::amg_precond<boost::numeric::ublas::compressed_matrix<ScalarType> > ublas_amg;
// Read matrix
if (!viennacl::io::read_matrix_market_file(ublas_matrix, "../examples/testdata/mat65k.mtx"))
{
std::cout << "Error reading Matrix file" << std::endl;
return EXIT_FAILURE;
}
// Set up rhs and result vector
if (!readVectorFromFile("../examples/testdata/rhs65025.txt", ublas_vec))
{
std::cout << "Error reading RHS file" << std::endl;
return 0;
}
if (!readVectorFromFile("../examples/testdata/result65025.txt", ublas_result))
{
std::cout << "Error reading Result file" << std::endl;
return 0;
}
viennacl::vector<ScalarType> vcl_vec(ublas_vec.size());
viennacl::vector<ScalarType> vcl_result(ublas_vec.size());
viennacl::compressed_matrix<ScalarType> vcl_compressed_matrix(ublas_vec.size(), ublas_vec.size());
// Copy to GPU
viennacl::copy(ublas_matrix, vcl_compressed_matrix);
viennacl::copy(ublas_vec, vcl_vec);
viennacl::copy(ublas_result, vcl_result);
//
// Run solver without preconditioner
//
std::cout << "-- CG solver (CPU, no preconditioner) --" << std::endl;
run_solver(ublas_matrix, ublas_vec, ublas_result, cg_solver, viennacl::linalg::no_precond());
std::cout << "-- CG solver (GPU, no preconditioner) --" << std::endl;
run_solver(vcl_compressed_matrix, vcl_vec, vcl_result, cg_solver, viennacl::linalg::no_precond());
//
// With AMG Preconditioner RS+DIRECT
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_RS, // coarsening strategy
VIENNACL_AMG_INTERPOL_DIRECT, // interpolation strategy
0.25, // strength of dependence threshold
0.2, // interpolation weight
0.67, // jacobi smoother weight
3, // presmoothing steps
3, // postsmoothing steps
0); // number of coarse levels to be used (0: automatically use as many as reasonable)
run_amg (cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "RS COARSENING, DIRECT INTERPOLATION", amg_tag);
//
// With AMG Preconditioner RS+CLASSIC
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_RS, VIENNACL_AMG_INTERPOL_CLASSIC, 0.25, 0.2, 0.67, 3, 3, 0);
run_amg ( cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "RS COARSENING, CLASSIC INTERPOLATION", amg_tag);
//
// With AMG Preconditioner ONEPASS+DIRECT
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_ONEPASS, VIENNACL_AMG_INTERPOL_DIRECT,0.25, 0.2, 0.67, 3, 3, 0);
run_amg (cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "ONEPASS COARSENING, DIRECT INTERPOLATION", amg_tag);
//
// With AMG Preconditioner RS0+DIRECT
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_RS0, VIENNACL_AMG_INTERPOL_DIRECT, 0.25, 0.2, 0.67, 3, 3, 0);
run_amg (cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "RS0 COARSENING, DIRECT INTERPOLATION", amg_tag);
//
// With AMG Preconditioner RS3+DIRECT
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_RS3, VIENNACL_AMG_INTERPOL_DIRECT, 0.25, 0.2, 0.67, 3, 3, 0);
run_amg (cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "RS3 COARSENING, DIRECT INTERPOLATION", amg_tag);
//
// With AMG Preconditioner AG
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_AG, VIENNACL_AMG_INTERPOL_AG, 0.08, 0, 0.67, 3, 3, 0);
run_amg (cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "AG COARSENING, AG INTERPOLATION", amg_tag);
//
// With AMG Preconditioner SA
//
amg_tag = viennacl::linalg::amg_tag(VIENNACL_AMG_COARSE_AG, VIENNACL_AMG_INTERPOL_SA, 0.08, 0.67, 0.67, 3, 3, 0);
run_amg (cg_solver, ublas_vec, ublas_result, ublas_matrix, vcl_vec, vcl_result, vcl_compressed_matrix, "AG COARSENING, SA INTERPOLATION",amg_tag);
//
// That's it.
//
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}
/**
* The main steps in this tutorial are the following:
* - Setup the systems
* - Run solvers without preconditioner and with ILUT preconditioner for comparison
* - Run solver with SPAI preconditioner on CPU
* - Run solver with SPAI preconditioner on GPU
* - Run solver with factored SPAI preconditioner on CPU
* - Run solver with factored SPAI preconditioner on GPU
*
**/
int main (int, const char **)
{
typedef float ScalarType;
typedef boost::numeric::ublas::compressed_matrix<ScalarType> MatrixType;
typedef boost::numeric::ublas::vector<ScalarType> VectorType;
typedef viennacl::compressed_matrix<ScalarType> GPUMatrixType;
typedef viennacl::vector<ScalarType> GPUVectorType;
/**
* If you have multiple OpenCL-capable devices in your system, we pick the second device for this tutorial.
**/
#ifdef VIENNACL_WITH_OPENCL
// Optional: Customize OpenCL backend
viennacl::ocl::platform pf = viennacl::ocl::get_platforms()[0];
std::vector<viennacl::ocl::device> const & devices = pf.devices();
// Optional: Set first device to first context:
viennacl::ocl::setup_context(0, devices[0]);
// Optional: Set second device for second context (use the same device for the second context if only one device available):
if (devices.size() > 1)
viennacl::ocl::setup_context(1, devices[1]);
else
viennacl::ocl::setup_context(1, devices[0]);
std::cout << viennacl::ocl::current_device().info() << std::endl;
viennacl::context ctx(viennacl::ocl::get_context(1));
#else
viennacl::context ctx;
#endif
/**
* Create uBLAS-based sparse matrix and read system matrix from file
**/
MatrixType M;
if (!viennacl::io::read_matrix_market_file(M, "../examples/testdata/mat65k.mtx"))
{
std::cerr<<"ERROR: Could not read matrix file " << std::endl;
exit(EXIT_FAILURE);
}
std::cout << "Size of matrix: " << M.size1() << std::endl;
std::cout << "Avg. Entries per row: " << double(M.nnz()) / static_cast<double>(M.size1()) << std::endl;
/**
* Use a constant load vector for simplicity
**/
VectorType rhs(M.size2());
for (std::size_t i=0; i<rhs.size(); ++i)
rhs(i) = ScalarType(1);
/**
* Create the ViennaCL matrix and vector and initialize with uBLAS data:
**/
GPUMatrixType gpu_M(M.size1(), M.size2(), ctx);
GPUVectorType gpu_rhs(M.size1(), ctx);
viennacl::copy(M, gpu_M);
viennacl::copy(rhs, gpu_rhs);
/**
* <h2>Solver Runs</h2>
* We use a relative tolerance of \f$ 10^{-10} \f$ with a maximum of 50 iterations for each use case.
* Usually more than 50 solver iterations are required for convergence, but this choice ensures shorter execution times and suffices for this tutorial.
**/
viennacl::linalg::bicgstab_tag solver_tag(1e-10, 50); //for simplicity and reasonably short execution times we use only 50 iterations here
/**
* The first reference is to use no preconditioner (CPU and GPU):
**/
std::cout << "--- Reference 1: Pure BiCGStab on CPU ---" << std::endl;
VectorType result = viennacl::linalg::solve(M, rhs, solver_tag);
std::cout << " * Solver iterations: " << solver_tag.iters() << std::endl;
VectorType residual = viennacl::linalg::prod(M, result) - rhs;
std::cout << " * Rel. Residual: " << viennacl::linalg::norm_2(residual) / viennacl::linalg::norm_2(rhs) << std::endl;
std::cout << "--- Reference 2: Pure BiCGStab on GPU ---" << std::endl;
GPUVectorType gpu_result = viennacl::linalg::solve(gpu_M, gpu_rhs, solver_tag);
std::cout << " * Solver iterations: " << solver_tag.iters() << std::endl;
GPUVectorType gpu_residual = viennacl::linalg::prod(gpu_M, gpu_result);
gpu_residual -= gpu_rhs;
std::cout << " * Rel. Residual: " << viennacl::linalg::norm_2(gpu_residual) / viennacl::linalg::norm_2(gpu_rhs) << std::endl;
/**
* The second reference is a standard ILUT preconditioner (only CPU):
**/
std::cout << "--- Reference 2: BiCGStab with ILUT on CPU ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::ilut_precond<MatrixType> ilut(M, viennacl::linalg::ilut_tag());
std::cout << " * Iterative solver run..." << std::endl;
run_solver(M, rhs, solver_tag, ilut);
/**
* <h2>Step 1: SPAI with CPU</h2>
**/
std::cout << "--- Test 1: CPU-based SPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::spai_precond<MatrixType> spai_cpu(M, viennacl::linalg::spai_tag(1e-3, 3, 5e-2));
std::cout << " * Iterative solver run..." << std::endl;
run_solver(M, rhs, solver_tag, spai_cpu);
/**
* <h2>Step 2: FSPAI with CPU</h2>
**/
std::cout << "--- Test 2: CPU-based FSPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::fspai_precond<MatrixType> fspai_cpu(M, viennacl::linalg::fspai_tag());
std::cout << " * Iterative solver run..." << std::endl;
run_solver(M, rhs, solver_tag, fspai_cpu);
/**
* <h2>Step 3: SPAI with GPU</h2>
**/
std::cout << "--- Test 3: GPU-based SPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::spai_precond<GPUMatrixType> spai_gpu(gpu_M, viennacl::linalg::spai_tag(1e-3, 3, 5e-2));
std::cout << " * Iterative solver run..." << std::endl;
run_solver(gpu_M, gpu_rhs, solver_tag, spai_gpu);
/**
* <h2>Step 4: FSPAI with GPU</h2>
**/
std::cout << "--- Test 4: GPU-based FSPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::fspai_precond<GPUMatrixType> fspai_gpu(gpu_M, viennacl::linalg::fspai_tag());
std::cout << " * Iterative solver run..." << std::endl;
run_solver(gpu_M, gpu_rhs, solver_tag, fspai_gpu);
/**
* That's it! Print success message and exit.
**/
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}
int main (int argc, const char * argv[])
{
typedef float ScalarType;
typedef boost::numeric::ublas::compressed_matrix<ScalarType> MatrixType;
typedef boost::numeric::ublas::vector<ScalarType> VectorType;
typedef viennacl::compressed_matrix<ScalarType> GPUMatrixType;
typedef viennacl::vector<ScalarType> GPUVectorType;
MatrixType M;
//
// Read system matrix from file
//
#ifdef _MSC_VER
if (!viennacl::io::read_matrix_market_file(M, "../../examples/testdata/mat65k.mtx"))
#else
if (!viennacl::io::read_matrix_market_file(M, "../examples/testdata/mat65k.mtx"))
#endif
{
std::cerr<<"ERROR: Could not read matrix file " << std::endl;
exit(EXIT_FAILURE);
}
std::cout << "Size of matrix: " << M.size1() << std::endl;
std::cout << "Avg. Entries per row: " << M.nnz() / static_cast<double>(M.size1()) << std::endl;
//
// Use uniform load vector:
//
VectorType rhs(M.size2());
for (size_t i=0; i<rhs.size(); ++i)
rhs(i) = 1;
GPUMatrixType gpu_M(M.size1(), M.size2());
GPUVectorType gpu_rhs(M.size1());
viennacl::copy(M, gpu_M);
viennacl::copy(rhs, gpu_rhs);
///////////////////////////////// Tests to follow /////////////////////////////
viennacl::linalg::bicgstab_tag solver_tag(1e-10, 50); //for simplicity and reasonably short execution times we use only 50 iterations here
//
// Reference: No preconditioner:
//
std::cout << "--- Reference 1: Pure BiCGStab on CPU ---" << std::endl;
VectorType result = viennacl::linalg::solve(M, rhs, solver_tag);
std::cout << " * Solver iterations: " << solver_tag.iters() << std::endl;
VectorType residual = viennacl::linalg::prod(M, result) - rhs;
std::cout << " * Rel. Residual: " << viennacl::linalg::norm_2(residual) / viennacl::linalg::norm_2(rhs) << std::endl;
std::cout << "--- Reference 2: Pure BiCGStab on GPU ---" << std::endl;
GPUVectorType gpu_result = viennacl::linalg::solve(gpu_M, gpu_rhs, solver_tag);
std::cout << " * Solver iterations: " << solver_tag.iters() << std::endl;
GPUVectorType gpu_residual = viennacl::linalg::prod(gpu_M, gpu_result) - gpu_rhs;
std::cout << " * Rel. Residual: " << viennacl::linalg::norm_2(gpu_residual) / viennacl::linalg::norm_2(gpu_rhs) << std::endl;
//
// Reference: ILUT preconditioner:
//
std::cout << "--- Reference 2: BiCGStab with ILUT on CPU ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::ilut_precond<MatrixType> ilut(M, viennacl::linalg::ilut_tag());
std::cout << " * Iterative solver run..." << std::endl;
run_solver(M, rhs, solver_tag, ilut);
//
// Test 1: SPAI with CPU:
//
std::cout << "--- Test 1: CPU-based SPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::spai_precond<MatrixType> spai_cpu(M, viennacl::linalg::spai_tag(1e-3, 3, 5e-2));
std::cout << " * Iterative solver run..." << std::endl;
run_solver(M, rhs, solver_tag, spai_cpu);
//
// Test 2: FSPAI with CPU:
//
std::cout << "--- Test 2: CPU-based FSPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::fspai_precond<MatrixType> fspai_cpu(M, viennacl::linalg::fspai_tag());
std::cout << " * Iterative solver run..." << std::endl;
run_solver(M, rhs, solver_tag, fspai_cpu);
//
// Test 3: SPAI with GPU:
//
std::cout << "--- Test 3: GPU-based SPAI ---" << std::endl;
std::cout << " * Preconditioner setup..." << std::endl;
viennacl::linalg::spai_precond<GPUMatrixType> spai_gpu(gpu_M, viennacl::linalg::spai_tag(1e-3, 3, 5e-2));
std::cout << " * Iterative solver run..." << std::endl;
run_solver(gpu_M, gpu_rhs, solver_tag, spai_gpu);
return EXIT_SUCCESS;
}
