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Added a GMRES solver to netlist. The generalized minimal residual method

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ist certainly more modern than Gaussian elimination and Gauss-Seidel.
However, more the current maximum matrix (KidNiki, 89x89) a combination
of Gauss-Seidel to solve for maximum one step to catch quasi-stable
conditions and fall-back to optimized Gaussian elimination (for sparse
matrix) outperforms GMRES by up to 100%. [Couriersud]
This commit is contained in:
couriersud 2015-06-12 00:29:26 +02:00
parent 6fd6de50ff
commit 3010f89079
3 changed files with 961 additions and 3 deletions
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src/emu/netlist/analog/mgmres.cpp Normal file
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# include <cstdlib>
# include <iostream>
# include <fstream>
# include <iomanip>
# include <cmath>
# include <ctime>
using namespace std;
#include "mgmres.hpp"
//****************************************************************************80
// http://people.sc.fsu.edu/~jburkardt/cpp_src/mgmres/mgmres.html
//****************************************************************************80
void gmres_t::ax_cr(const int * RESTRICT ia, const int * RESTRICT ja, const double * RESTRICT a,
const double * RESTRICT x, double * RESTRICT w)
//****************************************************************************80
//
// Purpose:
//
// AX_CR computes A*x for a matrix stored in sparse compressed row form.
//
// Discussion:
//
// The Sparse Compressed Row storage format is used.
//
// The matrix A is assumed to be sparse. To save on storage, only
// the nonzero entries of A are stored. The vector JA stores the
// column index of the nonzero value. The nonzero values are sorted
// by row, and the compressed row vector IA then has the property that
// the entries in A and JA that correspond to row I occur in indices
// IA[I] through IA[I+1]-1.
//
// For this version of MGMRES, the row and column indices are assumed
// to use the C/C++ convention, in which indexing begins at 0.
//
// If your index vectors IA and JA are set up so that indexing is based
// at 1, then each use of those vectors should be shifted down by 1.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 18 July 2007
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Reference:
//
// Richard Barrett, Michael Berry, Tony Chan, James Demmel,
// June Donato, Jack Dongarra, Victor Eijkhout, Roidan Pozo,
// Charles Romine, Henk van der Vorst,
// Templates for the Solution of Linear Systems:
// Building Blocks for Iterative Methods,
// SIAM, 1994,
// ISBN: 0898714710,
// LC: QA297.8.T45.
//
// Tim Kelley,
// Iterative Methods for Linear and Nonlinear Equations,
// SIAM, 2004,
// ISBN: 0898713528,
// LC: QA297.8.K45.
//
// Yousef Saad,
// Iterative Methods for Sparse Linear Systems,
// Second Edition,
// SIAM, 2003,
// ISBN: 0898715342,
// LC: QA188.S17.
//
// Parameters:
//
// Input, int N, the order of the system.
//
// Input, int NZ_NUM, the number of nonzeros.
//
// Input, int IA[N+1], JA[NZ_NUM], the row and column indices
// of the matrix values. The row vector has been compressed.
//
// Input, double A[NZ_NUM], the matrix values.
//
// Input, double X[N], the vector to be multiplied by A.
//
// Output, double W[N], the value of A*X.
//
{
const int n = m_n;
for ( int i = 0; i < n; i++ )
{
double tmp = 0.0;
int k1 = ia[i];
int k2 = ia[i+1];
for (int k = k1; k < k2; k++ )
{
tmp += a[k] * x[ja[k]];
}
w[i] = tmp;
}
return;
}
//****************************************************************************80
void gmres_t::diagonal_pointer_cr (const int nz_num, const int ia[], const int ja[])
//****************************************************************************80
//
// Purpose:
//
// DIAGONAL_POINTER_CR finds diagonal entries in a sparse compressed row matrix.
//
// Discussion:
//
// The matrix A is assumed to be stored in compressed row format. Only
// the nonzero entries of A are stored. The vector JA stores the
// column index of the nonzero value. The nonzero values are sorted
// by row, and the compressed row vector IA then has the property that
// the entries in A and JA that correspond to row I occur in indices
// IA[I] through IA[I+1]-1.
//
// The array UA can be used to locate the diagonal elements of the matrix.
//
// It is assumed that every row of the matrix includes a diagonal element,
// and that the elements of each row have been ascending sorted.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 18 July 2007
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Parameters:
//
// Input, int N, the order of the system.
//
// Input, int NZ_NUM, the number of nonzeros.
//
// Input, int IA[N+1], JA[NZ_NUM], the row and column indices
// of the matrix values. The row vector has been compressed. On output,
// the order of the entries of JA may have changed because of the sorting.
//
// Output, int UA[N], the index of the diagonal element of each row.
//
{
const int n = m_n;
for (int i = 0; i < n; i++)
{
m_ua[i] = -1;
const int j1 = ia[i];
const int j2 = ia[i + 1];
for (int j = j1; j < j2; j++)
{
if (ja[j] == i)
{
m_ua[i] = j;
}
}
}
return;
}
//****************************************************************************80
void gmres_t::ilu_cr (const int nz_num, const int ia[], const int ja[], const double a[])
//****************************************************************************80
//
// Purpose:
//
// ILU_CR computes the incomplete LU factorization of a matrix.
//
// Discussion:
//
// The matrix A is assumed to be stored in compressed row format. Only
// the nonzero entries of A are stored. The vector JA stores the
// column index of the nonzero value. The nonzero values are sorted
// by row, and the compressed row vector IA then has the property that
// the entries in A and JA that correspond to row I occur in indices
// IA[I] through IA[I+1]-1.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 25 July 2007
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Parameters:
//
// Input, int N, the order of the system.
//
// Input, int NZ_NUM, the number of nonzeros.
//
// Input, int IA[N+1], JA[NZ_NUM], the row and column indices
// of the matrix values. The row vector has been compressed.
//
// Input, double A[NZ_NUM], the matrix values.
//
// Input, int UA[N], the index of the diagonal element of each row.
//
// Output, double L[NZ_NUM], the ILU factorization of A.
//
{
int *iw;
int i;
int j;
int jj;
int jrow;
int jw;
int k;
double tl;
const int n = m_n;
iw = new int[n];
//
// Copy A.
//
for (k = 0; k < nz_num; k++)
{
m_l[k] = a[k];
}
for (i = 0; i < n; i++)
{
//
// IW points to the nonzero entries in row I.
//
for (j = 0; j < n; j++)
{
iw[j] = -1;
}
for (k = ia[i]; k <= ia[i + 1] - 1; k++)
{
iw[ja[k]] = k;
}
j = ia[i];
do
{
jrow = ja[j];
if (i <= jrow)
{
break;
}
tl = m_l[j] * m_l[m_ua[jrow]];
m_l[j] = tl;
for (jj = m_ua[jrow] + 1; jj <= ia[jrow + 1] - 1; jj++)
{
jw = iw[ja[jj]];
if (jw != -1)
{
m_l[jw] = m_l[jw] - tl * m_l[jj];
}
}
j = j + 1;
} while (j <= ia[i + 1] - 1);
m_ua[i] = j;
if (jrow != i)
{
cout << "\n";
cout << "ILU_CR - Fatal error!\n";
cout << " JROW != I\n";
cout << " JROW = " << jrow << "\n";
cout << " I = " << i << "\n";
exit(1);
}
if (m_l[j] == 0.0)
{
cout << "\n";
cout << "ILU_CR - Fatal error!\n";
cout << " Zero pivot on step I = " << i << "\n";
cout << " L[" << j << "] = 0.0\n";
exit(1);
}
m_l[j] = 1.0 / m_l[j];
}
for (k = 0; k < n; k++)
{
m_l[m_ua[k]] = 1.0 / m_l[m_ua[k]];
}
delete[] iw;
}
//****************************************************************************80
void gmres_t::lus_cr (const int * RESTRICT ia, const int * RESTRICT ja, double * RESTRICT r)
//****************************************************************************80
//
// Purpose:
//
// LUS_CR applies the incomplete LU preconditioner.
//
// Discussion:
//
// The linear system M * Z = R is solved for Z. M is the incomplete
// LU preconditioner matrix, and R is a vector supplied by the user.
// So essentially, we're solving L * U * Z = R.
//
// The matrix A is assumed to be stored in compressed row format. Only
// the nonzero entries of A are stored. The vector JA stores the
// column index of the nonzero value. The nonzero values are sorted
// by row, and the compressed row vector IA then has the property that
// the entries in A and JA that correspond to row I occur in indices
// IA[I] through IA[I+1]-1.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 18 July 2007
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Parameters:
//
// Input, int N, the order of the system.
//
// Input, int NZ_NUM, the number of nonzeros.
//
// Input, int IA[N+1], JA[NZ_NUM], the row and column indices
// of the matrix values. The row vector has been compressed.
//
// Input, double L[NZ_NUM], the matrix values.
//
// Input, int UA[N], the index of the diagonal element of each row.
//
// Input, double R[N], the right hand side.
//
// Output, double Z[N], the solution of the system M * Z = R.
//
{
const int n = m_n;
//
// Solve L * w = w where L is unit lower triangular.
//
for (int i = 1; i < n; i++ )
{
double tmp = 0.0;
for (int j = ia[i]; j < m_ua[i]; j++ )
{
tmp += m_l[j] * r[ja[j]];
}
r[i] -= tmp;
}
//
// Solve U * w = w, where U is upper triangular.
//
for (int i = n - 1; 0 <= i; i-- )
{
double tmp = 0.0;
for (int j = m_ua[i] + 1; j < ia[i+1]; j++ )
{
tmp += m_l[j] * r[ja[j]];
}
r[i] = (r[i] - tmp) / m_l[m_ua[i]];
}
return;
}
//****************************************************************************80
static inline void mult_givens ( const double c, const double s, const int k, double g[] )
//****************************************************************************80
//
// Purpose:
//
// MULT_GIVENS applies a Givens rotation to two successive entries of a vector.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 08 August 2006
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Reference:
//
// Richard Barrett, Michael Berry, Tony Chan, James Demmel,
// June Donato, Jack Dongarra, Victor Eijkhout, Roidan Pozo,
// Charles Romine, Henk van der Vorst,
// Templates for the Solution of Linear Systems:
// Building Blocks for Iterative Methods,
// SIAM, 1994,
// ISBN: 0898714710,
// LC: QA297.8.T45.
//
// Tim Kelley,
// Iterative Methods for Linear and Nonlinear Equations,
// SIAM, 2004,
// ISBN: 0898713528,
// LC: QA297.8.K45.
//
// Yousef Saad,
// Iterative Methods for Sparse Linear Systems,
// Second Edition,
// SIAM, 2003,
// ISBN: 0898715342,
// LC: QA188.S17.
//
// Parameters:
//
// Input, double C, S, the cosine and sine of a Givens
// rotation.
//
// Input, int K, indicates the location of the first vector entry.
//
// Input/output, double G[K+2], the vector to be modified. On output,
// the Givens rotation has been applied to entries G(K) and G(K+1).
//
{
double g1;
double g2;
g1 = c * g[k] - s * g[k+1];
g2 = s * g[k] + c * g[k+1];
g[k] = g1;
g[k+1] = g2;
return;
}
//****************************************************************************80
gmres_t::gmres_t(const int n)
: m_n(n)
{
int mr=n; /* FIXME: maximum iterations locked in here */
m_c = new double[mr+1];
m_g = new double[mr+1];
m_h = new double *[mr];
for (int i = 0; i < mr; i++)
m_h[i] = new double[mr+1];
/* This is using too much memory, but we are interested in speed for now*/
m_l = new double[n*n]; //[ia[n]+1];
m_r = new double[n];
m_s = new double[mr+1];
m_ua = new int[n];
m_v = new double *[n];
for (int i = 0; i < n; i++)
m_v[i] = new double[mr+1];
m_y = new double[mr+1];
}
gmres_t::~gmres_t()
{
delete [] m_c;
delete [] m_g;
delete [] m_h;
delete [] m_l;
delete [] m_r;
delete [] m_s;
delete [] m_ua;
delete [] m_v;
delete [] m_y;
}
int gmres_t::pmgmres_ilu_cr (const int nz_num, int ia[], int ja[], double a[],
double x[], const double rhs[], const int itr_max, const int mr, const double tol_abs,
const double tol_rel )
//****************************************************************************80
//
// Purpose:
//
// PMGMRES_ILU_CR applies the preconditioned restarted GMRES algorithm.
//
// Discussion:
//
// The matrix A is assumed to be stored in compressed row format. Only
// the nonzero entries of A are stored. The vector JA stores the
// column index of the nonzero value. The nonzero values are sorted
// by row, and the compressed row vector IA then has the property that
// the entries in A and JA that correspond to row I occur in indices
// IA[I] through IA[I+1]-1.
//
// This routine uses the incomplete LU decomposition for the
// preconditioning. This preconditioner requires that the sparse
// matrix data structure supplies a storage position for each diagonal
// element of the matrix A, and that each diagonal element of the
// matrix A is not zero.
//
// Thanks to Jesus Pueblas Sanchez-Guerra for supplying two
// corrections to the code on 31 May 2007.
//
//
// This implementation of the code stores the doubly-dimensioned arrays
// H and V as vectors. However, it follows the C convention of storing
// them by rows, rather than my own preference for storing them by
// columns. I may come back and change this some time.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 26 July 2007
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Reference:
//
// Richard Barrett, Michael Berry, Tony Chan, James Demmel,
// June Donato, Jack Dongarra, Victor Eijkhout, Roidan Pozo,
// Charles Romine, Henk van der Vorst,
// Templates for the Solution of Linear Systems:
// Building Blocks for Iterative Methods,
// SIAM, 1994.
// ISBN: 0898714710,
// LC: QA297.8.T45.
//
// Tim Kelley,
// Iterative Methods for Linear and Nonlinear Equations,
// SIAM, 2004,
// ISBN: 0898713528,
// LC: QA297.8.K45.
//
// Yousef Saad,
// Iterative Methods for Sparse Linear Systems,
// Second Edition,
// SIAM, 2003,
// ISBN: 0898715342,
// LC: QA188.S17.
//
// Parameters:
//
// Input, int N, the order of the linear system.
//
// Input, int NZ_NUM, the number of nonzero matrix values.
//
// Input, int IA[N+1], JA[NZ_NUM], the row and column indices
// of the matrix values. The row vector has been compressed.
//
// Input, double A[NZ_NUM], the matrix values.
//
// Input/output, double X[N]; on input, an approximation to
// the solution. On output, an improved approximation.
//
// Input, double RHS[N], the right hand side of the linear system.
//
// Input, int ITR_MAX, the maximum number of (outer) iterations to take.
//
// Input, int MR, the maximum number of (inner) iterations to take.
// MR must be less than N.
//
// Input, double TOL_ABS, an absolute tolerance applied to the
// current residual.
//
// Input, double TOL_REL, a relative tolerance comparing the
// current residual to the initial residual.
//
{
double av;
double delta = 1.0e-03;
double htmp;
int itr;
int itr_used = 0;
int k_copy = 0;
double mu;
double rho;
double rho_tol = 0;
const int verbose = 0;
const bool pre_ilu = true;
const int n = m_n;
rearrange_cr(nz_num, ia, ja, a);
if (pre_ilu)
{
diagonal_pointer_cr(nz_num, ia, ja);
ilu_cr(nz_num, ia, ja, a);
}
if (verbose)
{
cout << "\n";
cout << "PMGMRES_ILU_CR\n";
cout << " Number of unknowns = " << n << "\n";
}
for (itr = 0; itr < itr_max; itr++)
{
ax_cr(ia, ja, a, x, m_r);
for (int i = 0; i < n; i++)
m_r[i] = rhs[i] - m_r[i];
if (pre_ilu)
lus_cr(ia, ja, m_r);
rho = sqrt(r8vec_dot2(m_r));
if (verbose)
cout << " ITR = " << itr << " Residual = " << rho << "\n";
if (itr == 0)
rho_tol = rho * tol_rel;
double rhoq = 1.0 / rho;
for (int i = 0; i < n; i++)
m_v[0][i] = m_r[i] * rhoq;
for (int i = 0; i < mr + 1; i++)
m_g[i] = 0.0;
m_g[0] = rho;
for (int i = 0; i < mr; i++)
for (int j = 0; j < mr + 1; j++)
m_h[i][j] = 0.0;
for (int k = 0; k < mr; k++)
{
k_copy = k;
ax_cr(ia, ja, a, m_v[k], m_v[k + 1]);
if (pre_ilu)
lus_cr(ia, ja, m_v[k + 1]);
av = sqrt(r8vec_dot2(m_v[k + 1]));
for (int j = 0; j <= k; j++)
{
m_h[j][k] = r8vec_dot(m_v[k + 1], m_v[j]);
for (int i = 0; i < n; i++)
{
m_v[k + 1][i] = m_v[k + 1][i]
- m_h[j][k] * m_v[j][i];
}
}
m_h[k + 1][k] = sqrt(r8vec_dot2(m_v[k + 1]));
if ((av + delta * m_h[k + 1][k]) == av)
{
for (int j = 0; j < k + 1; j++)
{
htmp = r8vec_dot(m_v[k + 1], m_v[j]);
m_h[j][k] = m_h[j][k] + htmp;
for (int i = 0; i < n; i++)
{
m_v[k + 1][i] = m_v[k + 1][i] - htmp * m_v[j][i];
}
}
m_h[k + 1][k] = sqrt(r8vec_dot2(m_v[k + 1]));
}
if (m_h[k + 1][k] != 0.0)
{
for (int i = 0; i < n; i++)
{
m_v[k + 1][i] = m_v[k + 1][i]
/ m_h[k + 1][k];
}
}
if (0 < k)
{
for (int i = 0; i < k + 2; i++)
{
m_y[i] = m_h[i][k];
}
for (int j = 0; j < k; j++)
{
mult_givens(m_c[j], m_s[j], j, m_y);
}
for (int i = 0; i < k + 2; i++)
{
m_h[i][k] = m_y[i];
}
}
mu = sqrt(
m_h[k][k] * m_h[k][k]
+ m_h[k + 1][k] * m_h[k + 1][k]);
m_c[k] = m_h[k][k] / mu;
m_s[k] = -m_h[k + 1][k] / mu;
m_h[k][k] = m_c[k] * m_h[k][k]
- m_s[k] * m_h[k + 1][k];
m_h[k + 1][k] = 0.0;
mult_givens(m_c[k], m_s[k], k, m_g);
rho = std::abs(m_g[k + 1]);
itr_used = itr_used + 1;
if (verbose)
{
cout << " K = " << k << " Residual = " << rho << "\n";
}
if (rho <= rho_tol && rho <= tol_abs)
{
break;
}
}
m_y[k_copy] = m_g[k_copy] / m_h[k_copy][k_copy];
for (int i = k_copy - 1; 0 <= i; i--)
{
double tmp = m_g[i];
for (int j = i + 1; j < k_copy + 1; j++)
{
tmp -= m_h[i][j] * m_y[j];
}
m_y[i] = tmp / m_h[i][i];
}
double cerr = 0;
for (int i = 0; i < n; i++)
{
double tmp = 0.0;
for (int j = 0; j < k_copy + 1; j++)
{
tmp += m_v[j][i] * m_y[j];
}
cerr = std::max(std::abs(tmp), cerr);
x[i] += tmp;
}
#if 0
if (cerr < 1e-8)
return 1; //break;
#else
if (rho <= rho_tol && rho <= tol_abs)
{
break;
}
#endif
}
if (verbose)
{
cout << "\n";
;
cout << "PMGMRES_ILU_CR:\n";
cout << " Iterations = " << itr_used << "\n";
cout << " Final residual = " << rho << "\n";
}
//if ( rho >= tol_abs )
// printf("missed!\n");
return (itr_used - 1) * mr + k_copy;
}
//****************************************************************************80
double gmres_t::r8vec_dot (const double a1[], const double a2[] )
{
const int n = m_n;
double value = 0.0;
for ( int i = 0; i < n; i++ )
value = value + a1[i] * a2[i];
return value;
}
double gmres_t::r8vec_dot2 (const double a1[])
{
const int n = m_n;
double value = 0.0;
for ( int i = 0; i < n; i++ )
value = value + a1[i] * a1[i];
return value;
}
//****************************************************************************80
//****************************************************************************80
void gmres_t::rearrange_cr (const int nz_num, int ia[], int ja[], double a[] )
//****************************************************************************80
//
// Purpose:
//
// REARRANGE_CR sorts a sparse compressed row matrix.
//
// Discussion:
//
// This routine guarantees that the entries in the CR matrix
// are properly sorted.
//
// After the sorting, the entries of the matrix are rearranged in such
// a way that the entries of each column are listed in ascending order
// of their column values.
//
// The matrix A is assumed to be stored in compressed row format. Only
// the nonzero entries of A are stored. The vector JA stores the
// column index of the nonzero value. The nonzero values are sorted
// by row, and the compressed row vector IA then has the property that
// the entries in A and JA that correspond to row I occur in indices
// IA[I] through IA[I+1]-1.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 18 July 2007
//
// Author:
//
// Original C version by Lili Ju.
// C++ version by John Burkardt.
//
// Parameters:
//
// Input, int N, the order of the system.
//
// Input, int NZ_NUM, the number of nonzeros.
//
// Input, int IA[N+1], the compressed row index.
//
// Input/output, int JA[NZ_NUM], the column indices. On output,
// the order of the entries of JA may have changed because of the sorting.
//
// Input/output, double A[NZ_NUM], the matrix values. On output, the
// order of the entries may have changed because of the sorting.
//
{
double dtemp;
int i;
int is;
int itemp;
int j;
int j1;
int j2;
int k;
const int n = m_n;
for ( i = 0; i < n; i++ )
{
j1 = ia[i];
j2 = ia[i+1];
is = j2 - j1;
for ( k = 1; k < is; k++ )
{
for ( j = j1; j < j2 - k; j++ )
{
if ( ja[j+1] < ja[j] )
{
itemp = ja[j+1];
ja[j+1] = ja[j];
ja[j] = itemp;
dtemp = a[j+1];
a[j+1] = a[j];
a[j] = dtemp;
}
}
}
}
return;
}
//****************************************************************************80

43
src/emu/netlist/analog/mgmres.hpp Normal file
View File

@ -0,0 +1,43 @@
#ifndef __MGMRES
#define __MGMRES
class gmres_t
{
public:
gmres_t(const int n);
~gmres_t();
int pmgmres_ilu_cr (const int nz_num, int ia[], int ja[], double a[],
double x[], const double rhs[], const int itr_max, const int mr, const double tol_abs,
const double tol_rel );
private:
void diagonal_pointer_cr(const int nz_num, const int ia[], const int ja[]);
void rearrange_cr (const int nz_num, int ia[], int ja[], double a[] );
inline double r8vec_dot (const double a1[], const double a2[] );
inline double r8vec_dot2 (const double a1[]);
void ax_cr(const int * RESTRICT ia, const int * RESTRICT ja, const double * RESTRICT a,
const double * RESTRICT x, double * RESTRICT w);
void lus_cr (const int * RESTRICT ia, const int * RESTRICT ja, double * RESTRICT r);
void ilu_cr (const int nz_num, const int ia[], const int ja[], const double a[]);
const int m_n;
double * RESTRICT m_c;
double * RESTRICT m_g;
double ** RESTRICT m_h;
double * RESTRICT m_l;
double * RESTRICT m_r;
double * RESTRICT m_s;
double ** RESTRICT m_v;
int * RESTRICT m_ua;
double * RESTRICT m_y;
};
#endif

10
src/emu/netlist/analog/nld_solver.c
View File

@ -11,8 +11,8 @@
#if 0
#pragma GCC optimize "-ffast-math"
#pragma GCC optimize "-ftree-parallelize-loops=4"
//#pragma GCC optimize "-funroll-loops"
//#pragma GCC optimize "-ftree-parallelize-loops=4"
#pragma GCC optimize "-funroll-loops"
#pragma GCC optimize "-funswitch-loops"
#pragma GCC optimize "-fvariable-expansion-in-unroller"
#pragma GCC optimize "-funsafe-loop-optimizations"
@ -32,6 +32,7 @@
#include "nld_ms_direct2.h"
#include "nld_ms_sor.h"
#include "nld_ms_sor_mat.h"
#include "nld_ms_gmres.h"
#include "nld_twoterm.h"
#include "../nl_lists.h"
@ -410,6 +411,7 @@ netlist_matrix_solver_t * NETLIB_NAME(solver)::create_solver(int size, const int
else
{
typedef netlist_matrix_solver_SOR_t<m_N,_storage_N> solver_GS;
//typedef netlist_matrix_solver_GMRES_t<m_N,_storage_N> solver_GS;
return palloc(solver_GS, &m_params, size);
}
}
@ -423,7 +425,7 @@ netlist_matrix_solver_t * NETLIB_NAME(solver)::create_solver(int size, const int
ATTR_COLD void NETLIB_NAME(solver)::post_start()
{
netlist_analog_net_t::list_t groups[100];
netlist_analog_net_t::list_t groups[256];
int cur_group = -1;
const int gs_threshold = m_gs_threshold.Value();
const bool use_specific = true;
@ -559,3 +561,5 @@ ATTR_COLD void NETLIB_NAME(solver)::post_start()
}
}
}
#include "mgmres.cpp"