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netlist: further solver refactoring. (nw)

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This commit is contained in:
couriersud 2019-11-01 01:21:43 +01:00
parent e37b9b7b88
commit 47d938b149
11 changed files with 270 additions and 260 deletions
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2
src/lib/netlist/plib/parray.h
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@ -105,6 +105,8 @@ namespace plib {
throw plib::pexception("parray: size error " + plib::to_string(size) + ">" + plib::to_string(SIZE));
}
base_type &as_base() noexcept { return m_a; }
inline size_type size() const noexcept { return SIZE <= 0 ? m_size : SIZEABS(); }
constexpr size_type max_size() const noexcept { return base_type::max_size(); }

52
src/lib/netlist/solver/nld_matrix_solver.cpp
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@ -341,14 +341,6 @@ namespace solver
* save states
*/
m_last_V.resize(iN, plib::constants<nl_fptype>::zero());
m_DD_n_m_1.resize(iN, plib::constants<nl_fptype>::zero());
m_h_n_m_1.resize(iN, plib::constants<nl_fptype>::zero());
state().save(*this, m_last_V.as_base(), this->name(), "m_last_V");
state().save(*this, m_DD_n_m_1.as_base(), this->name(), "m_DD_n_m_1");
state().save(*this, m_h_n_m_1.as_base(), this->name(), "m_h_n_m_1");
for (std::size_t k = 0; k < iN; k++)
{
pstring num = plib::pfmt("{1}")(k);
@ -553,50 +545,6 @@ namespace solver
}
}
netlist_time matrix_solver_t::compute_next_timestep(const nl_fptype cur_ts)
{
nl_fptype new_solver_timestep = m_params.m_max_timestep;
if (m_params.m_dynamic_ts)
{
for (std::size_t k = 0; k < m_terms.size(); k++)
{
auto &t = m_terms[k];
//const nl_fptype DD_n = (n->Q_Analog() - t->m_last_V);
// avoid floating point exceptions
const nl_fptype DD_n = std::max(-fp_constants<nl_fptype>::TIMESTEP_MAXDIFF, std::min(+fp_constants<nl_fptype>::TIMESTEP_MAXDIFF,(t.getV() - m_last_V[k])));
const nl_fptype hn = cur_ts;
//printf("%g %g %g %g\n", DD_n, hn, t.m_DD_n_m_1, t.m_h_n_m_1);
nl_fptype DD2 = (DD_n / hn - m_DD_n_m_1[k] / m_h_n_m_1[k]) / (hn + m_h_n_m_1[k]);
nl_fptype new_net_timestep(0);
m_h_n_m_1[k] = hn;
m_DD_n_m_1[k] = DD_n;
if (std::fabs(DD2) > fp_constants<nl_fptype>::TIMESTEP_MINDIV) // avoid div-by-zero
new_net_timestep = std::sqrt(m_params.m_dynamic_lte / std::fabs(plib::constants<nl_fptype>::cast(0.5)*DD2));
else
new_net_timestep = m_params.m_max_timestep;
if (new_net_timestep < new_solver_timestep)
new_solver_timestep = new_net_timestep;
m_last_V[k] = t.getV();
}
if (new_solver_timestep < m_params.m_min_timestep)
{
new_solver_timestep = m_params.m_min_timestep;
}
}
//if (new_solver_timestep > 10.0 * hn)
// new_solver_timestep = 10.0 * hn;
/*
* FIXME: Factor 2 below is important. Without, we get timing issues. This must be a bug elsewhere.
*/
return std::max(netlist_time::from_double(new_solver_timestep), netlist_time::quantum() * 2);
}
void matrix_solver_t::log_stats()
{
if (this->m_stat_calculations != 0 && this->m_stat_vsolver_calls && log().verbose.is_enabled())

316
src/lib/netlist/solver/nld_matrix_solver.h
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@ -207,15 +207,42 @@ namespace solver
void update_dynamic();
virtual unsigned vsolve_non_dynamic(const bool newton_raphson) = 0;
virtual netlist_time compute_next_timestep(const nl_fptype cur_ts) = 0;
netlist_time compute_next_timestep(const nl_fptype cur_ts);
/* virtual */ void add_term(std::size_t net_idx, terminal_t *term);
void add_term(std::size_t net_idx, terminal_t *term);
std::size_t max_railstart() const noexcept
{
std::size_t max_rail = 0;
for (std::size_t k = 0; k < m_terms.size(); k++)
max_rail = std::max(max_rail, m_terms[k].railstart());
return max_rail;
}
template <typename T>
void store(const T & V);
void store(const T & V)
{
const std::size_t iN = this->m_terms.size();
for (std::size_t i = 0; i < iN; i++)
this->m_terms[i].setV(V[i]);
}
template <typename T>
auto delta(const T & V) -> typename std::decay<decltype(V[0])>::type;
auto delta(const T & V) -> typename std::decay<decltype(V[0])>::type
{
/* NOTE: Ideally we should also include currents (RHS) here. This would
* need a reevaluation of the right hand side after voltages have been updated
* and thus belong into a different calculation. This applies to all solvers.
*/
const std::size_t iN = this->m_terms.size();
using vtype = typename std::decay<decltype(V[0])>::type;
vtype cerr = 0;
for (std::size_t i = 0; i < iN; i++)
cerr = std::max(cerr, std::abs(V[i] - static_cast<vtype>(this->m_terms[i].getV())));
return cerr;
}
void set_pointers()
{
@ -229,7 +256,6 @@ namespace solver
max_rail = std::max(max_rail, m_terms[k].railstart());
}
m_mat_ptr.resize(iN, max_rail+1);
m_gtn.resize(iN, max_count);
m_gonn.resize(iN, max_count);
m_Idrn.resize(iN, max_count);
@ -247,49 +273,6 @@ namespace solver
}
}
template <typename FT>
void fill_matrix(std::size_t N, FT &RHS)
{
for (std::size_t k = 0; k < N; k++)
{
auto &net = m_terms[k];
auto **tcr_r = &(m_mat_ptr[k][0]);
const std::size_t term_count = net.count();
const std::size_t railstart = net.railstart();
const auto &go = m_gonn[k];
const auto &gt = m_gtn[k];
const auto &Idr = m_Idrn[k];
const auto &cnV = m_connected_net_Vn[k];
for (std::size_t i = 0; i < railstart; i++)
*tcr_r[i] += go[i];
typename FT::value_type gtot_t = 0.0;
typename FT::value_type RHS_t = 0.0;
for (std::size_t i = 0; i < term_count; i++)
{
gtot_t += gt[i];
RHS_t += Idr[i];
}
// FIXME: Code above is faster than vec_sum - Check this
#if 0
auto gtot_t = plib::vec_sum<FT>(term_count, m_gt);
auto RHS_t = plib::vec_sum<FT>(term_count, m_Idr);
#endif
for (std::size_t i = railstart; i < term_count; i++)
{
RHS_t += (/*m_Idr[i]*/ (- go[i]) * *cnV[i]);
}
RHS[k] = RHS_t;
// update diagonal element ...
*tcr_r[railstart] += gtot_t; //mat.A[mat.diag[k]] += gtot_t;
}
}
template <typename T, typename M>
void log_fill(const T &fill, M &mat)
@ -345,61 +328,17 @@ namespace solver
}
}
template <typename M>
void clear_square_mat(std::size_t n, M &m)
{
for (std::size_t k=0; k < n; k++)
{
auto *p = &(m[k][0]);
for (std::size_t i=0; i < n; i++)
p[i] = 0.0;
}
}
template <typename M>
void build_mat_ptr(std::size_t iN, M &mat)
{
for (std::size_t k=0; k<iN; k++)
{
std::size_t cnt(0);
/* build pointers into the compressed row format matrix for each terminal */
for (std::size_t j=0; j< this->m_terms[k].railstart();j++)
{
int other = this->m_terms[k].m_connected_net_idx[j];
if (other >= 0)
{
m_mat_ptr[k][j] = &(mat[k][static_cast<std::size_t>(other)]);
cnt++;
}
}
nl_assert_always(cnt == this->m_terms[k].railstart(), "Count and railstart mismatch");
m_mat_ptr[k][this->m_terms[k].railstart()] = &(mat[k][k]);
}
}
template <typename T>
using aligned_alloc = plib::aligned_allocator<T, PALIGN_VECTOROPT>;
plib::pmatrix2d<nl_fptype, aligned_alloc<nl_fptype>> m_gonn;
plib::pmatrix2d<nl_fptype, aligned_alloc<nl_fptype>> m_gtn;
plib::pmatrix2d<nl_fptype, aligned_alloc<nl_fptype>> m_Idrn;
plib::pmatrix2d<nl_mat_fptype *, aligned_alloc<nl_mat_fptype *>> m_mat_ptr;
plib::pmatrix2d<nl_fptype *, aligned_alloc<nl_fptype *>> m_connected_net_Vn;
plib::aligned_vector<terms_for_net_t> m_terms;
plib::aligned_vector<terms_for_net_t> m_rails_temp;
/* state - variable time_stepping */
plib::aligned_vector<nl_fptype> m_last_V;
plib::aligned_vector<nl_fptype> m_DD_n_m_1;
plib::aligned_vector<nl_fptype> m_h_n_m_1;
// FIXME: it should be like this, however dimensions are determined
// in vsetup.
//state_container<std::vector<nl_fptype>> m_last_V;
//state_container<std::vector<nl_fptype>> m_DD_n_m_1;
//state_container<std::vector<nl_fptype>> m_h_n_m_1;
std::vector<unique_pool_ptr<proxied_analog_output_t>> m_inps;
const solver_parameters_t &m_params;
@ -430,29 +369,182 @@ namespace solver
std::size_t m_ops;
};
template <typename T>
auto matrix_solver_t::delta(const T & V) -> typename std::decay<decltype(V[0])>::type
template <typename FT, int SIZE>
class matrix_solver_ext_t: public matrix_solver_t
{
/* NOTE: Ideally we should also include currents (RHS) here. This would
* need a reevaluation of the right hand side after voltages have been updated
* and thus belong into a different calculation. This applies to all solvers.
*/
friend class matrix_solver_t;
public:
const std::size_t iN = this->m_terms.size();
using vtype = typename std::decay<decltype(V[0])>::type;
vtype cerr = 0;
for (std::size_t i = 0; i < iN; i++)
cerr = std::max(cerr, std::abs(V[i] - static_cast<vtype>(this->m_terms[i].getV())));
return cerr;
}
using float_type = FT;
template <typename T>
void matrix_solver_t::store(const T & V)
{
const std::size_t iN = this->m_terms.size();
for (std::size_t i = 0; i < iN; i++)
this->m_terms[i].setV(V[i]);
}
matrix_solver_ext_t(netlist_state_t &anetlist, const pstring &name,
const analog_net_t::list_t &nets,
const solver_parameters_t *params, const std::size_t size)
: matrix_solver_t(anetlist, name, nets, params)
, m_dim(size)
, m_mat_ptr(size, this->max_railstart() + 1)
, m_last_V(size, plib::constants<float_type>::zero())
, m_DD_n_m_1(size, plib::constants<float_type>::zero())
, m_h_n_m_1(size, plib::constants<float_type>::zero())
{
/*
* save states
*/
state().save(*this, m_last_V.as_base(), this->name(), "m_last_V");
state().save(*this, m_DD_n_m_1.as_base(), this->name(), "m_DD_n_m_1");
state().save(*this, m_h_n_m_1.as_base(), this->name(), "m_h_n_m_1");
}
private:
const std::size_t m_dim;
protected:
static constexpr const std::size_t SIZEABS = plib::parray<FT, SIZE>::SIZEABS();
static constexpr const std::size_t m_pitch_ABS = (((SIZEABS + 0) + 7) / 8) * 8;
plib::parray2D<float_type *, SIZE, 0> m_mat_ptr;
/* state - variable time_stepping */
plib::parray<float_type, SIZE> m_last_V;
plib::parray<float_type, SIZE> m_DD_n_m_1;
plib::parray<float_type, SIZE> m_h_n_m_1;
// FIXME: it should be like this, however dimensions are determined
// in vsetup.
//state_container<std::vector<nl_fptype>> m_last_V;
//state_container<std::vector<nl_fptype>> m_DD_n_m_1;
//state_container<std::vector<nl_fptype>> m_h_n_m_1;
constexpr std::size_t size() const noexcept { return (SIZE > 0) ? static_cast<std::size_t>(SIZE) : m_dim; }
netlist_time compute_next_timestep(const nl_fptype cur_ts) override
{
nl_fptype new_solver_timestep = m_params.m_max_timestep;
if (m_params.m_dynamic_ts)
{
for (std::size_t k = 0; k < m_terms.size(); k++)
{
auto &t = m_terms[k];
//const nl_fptype DD_n = (n->Q_Analog() - t->m_last_V);
// avoid floating point exceptions
const nl_fptype DD_n = std::max(-fp_constants<nl_fptype>::TIMESTEP_MAXDIFF, std::min(+fp_constants<nl_fptype>::TIMESTEP_MAXDIFF,(t.getV() - m_last_V[k])));
const nl_fptype hn = cur_ts;
//printf("%g %g %g %g\n", DD_n, hn, t.m_DD_n_m_1, t.m_h_n_m_1);
nl_fptype DD2 = (DD_n / hn - m_DD_n_m_1[k] / m_h_n_m_1[k]) / (hn + m_h_n_m_1[k]);
nl_fptype new_net_timestep(0);
m_h_n_m_1[k] = hn;
m_DD_n_m_1[k] = DD_n;
if (std::fabs(DD2) > fp_constants<nl_fptype>::TIMESTEP_MINDIV) // avoid div-by-zero
new_net_timestep = std::sqrt(m_params.m_dynamic_lte / std::fabs(plib::constants<nl_fptype>::cast(0.5)*DD2));
else
new_net_timestep = m_params.m_max_timestep;
if (new_net_timestep < new_solver_timestep)
new_solver_timestep = new_net_timestep;
m_last_V[k] = t.getV();
}
if (new_solver_timestep < m_params.m_min_timestep)
{
new_solver_timestep = m_params.m_min_timestep;
}
}
//if (new_solver_timestep > 10.0 * hn)
// new_solver_timestep = 10.0 * hn;
/*
* FIXME: Factor 2 below is important. Without, we get timing issues. This must be a bug elsewhere.
*/
return std::max(netlist_time::from_double(new_solver_timestep), netlist_time::quantum() * 2);
}
template <typename M>
void build_mat_ptr(M &mat)
{
const std::size_t iN = size();
for (std::size_t k=0; k<iN; k++)
{
std::size_t cnt(0);
/* build pointers into the compressed row format matrix for each terminal */
for (std::size_t j=0; j< this->m_terms[k].railstart();j++)
{
int other = this->m_terms[k].m_connected_net_idx[j];
if (other >= 0)
{
m_mat_ptr[k][j] = &(mat[k][static_cast<std::size_t>(other)]);
cnt++;
}
}
nl_assert_always(cnt == this->m_terms[k].railstart(), "Count and railstart mismatch");
m_mat_ptr[k][this->m_terms[k].railstart()] = &(mat[k][k]);
}
}
template <typename M>
void clear_square_mat(M &m)
{
const std::size_t n = size();
for (std::size_t k=0; k < n; k++)
{
auto *p = &(m[k][0]);
for (std::size_t i=0; i < n; i++)
p[i] = 0.0;
}
}
template <typename RT>
void fill_matrix(RT &RHS)
{
const std::size_t N = size();
for (std::size_t k = 0; k < N; k++)
{
auto &net = m_terms[k];
auto **tcr_r = &(m_mat_ptr[k][0]);
const std::size_t term_count = net.count();
const std::size_t railstart = net.railstart();
const auto &go = m_gonn[k];
const auto &gt = m_gtn[k];
const auto &Idr = m_Idrn[k];
const auto &cnV = m_connected_net_Vn[k];
for (std::size_t i = 0; i < railstart; i++)
*tcr_r[i] += go[i];
typename RT::value_type gtot_t = 0.0;
typename RT::value_type RHS_t = 0.0;
for (std::size_t i = 0; i < term_count; i++)
{
gtot_t += gt[i];
RHS_t += Idr[i];
}
// FIXME: Code above is faster than vec_sum - Check this
#if 0
auto gtot_t = plib::vec_sum<FT>(term_count, m_gt);
auto RHS_t = plib::vec_sum<FT>(term_count, m_Idr);
#endif
for (std::size_t i = railstart; i < term_count; i++)
{
RHS_t += (/*m_Idr[i]*/ (- go[i]) * *cnV[i]);
}
RHS[k] = RHS_t;
// update diagonal element ...
*tcr_r[railstart] += gtot_t; //mat.A[mat.diag[k]] += gtot_t;
}
}
};
} // namespace solver
} // namespace netlist

37
src/lib/netlist/solver/nld_ms_direct.h
View File

@ -22,7 +22,7 @@ namespace solver
{
template <typename FT, int SIZE>
class matrix_solver_direct_t: public matrix_solver_t
class matrix_solver_direct_t: public matrix_solver_ext_t<FT, SIZE>
{
friend class matrix_solver_t;
public:
@ -37,7 +37,6 @@ namespace solver
private:
const std::size_t m_dim;
const std::size_t m_pitch;
protected:
@ -47,8 +46,6 @@ namespace solver
unsigned vsolve_non_dynamic(const bool newton_raphson) override;
unsigned solve_non_dynamic(const bool newton_raphson);
constexpr std::size_t size() const noexcept { return (SIZE > 0) ? static_cast<std::size_t>(SIZE) : m_dim; }
void LE_solve();
template <typename T>
@ -69,15 +66,15 @@ namespace solver
template <typename FT, int SIZE>
void matrix_solver_direct_t<FT, SIZE>::LE_solve()
{
const std::size_t kN = size();
if (!m_params.m_pivot)
const std::size_t kN = this->size();
if (!this->m_params.m_pivot)
{
for (std::size_t i = 0; i < kN; i++)
{
/* FIXME: Singular matrix? */
const FT f = 1.0 / m_A[i][i];
const auto &nzrd = m_terms[i].m_nzrd;
const auto &nzbd = m_terms[i].m_nzbd;
const auto &nzrd = this->m_terms[i].m_nzrd;
const auto &nzbd = this->m_terms[i].m_nzbd;
for (auto &j : nzbd)
{
@ -138,10 +135,10 @@ namespace solver
void matrix_solver_direct_t<FT, SIZE>::LE_back_subst(
T & x)
{
const std::size_t kN = size();
const std::size_t kN = this->size();
/* back substitution */
if (m_params.m_pivot)
if (this->m_params.m_pivot)
{
for (std::size_t j = kN; j-- > 0; )
{
@ -156,7 +153,7 @@ namespace solver
for (std::size_t j = kN; j-- > 0; )
{
FT tmp = 0;
const auto &nzrd = m_terms[j].m_nzrd;
const auto &nzrd = this->m_terms[j].m_nzrd;
const auto e = nzrd.size(); // - 1; /* exclude RHS element */
for ( std::size_t k = 0; k < e; k++)
tmp += m_A[j][nzrd[k]] * x[nzrd[k]];
@ -171,20 +168,17 @@ namespace solver
this->LE_solve();
this->LE_back_subst(m_new_V);
const FT err = (newton_raphson ? delta(m_new_V) : 0.0);
store(m_new_V);
const FT err = (newton_raphson ? this->delta(m_new_V) : 0.0);
this->store(m_new_V);
return (err > this->m_params.m_accuracy) ? 2 : 1;
}
template <typename FT, int SIZE>
unsigned matrix_solver_direct_t<FT, SIZE>::vsolve_non_dynamic(const bool newton_raphson)
{
const std::size_t iN = this->size();
/* populate matrix */
this->clear_square_mat(iN, m_A);
this->fill_matrix(iN, m_RHS);
this->clear_square_mat(m_A);
this->fill_matrix(m_RHS);
this->m_stat_calculations++;
return this->solve_non_dynamic(newton_raphson);
@ -195,14 +189,13 @@ namespace solver
const analog_net_t::list_t &nets,
const solver_parameters_t *params,
const std::size_t size)
: matrix_solver_t(anetlist, name, nets, params)
, m_dim(size)
, m_pitch(m_pitch_ABS ? m_pitch_ABS : (((m_dim + 0) + 7) / 8) * 8)
: matrix_solver_ext_t<FT, SIZE>(anetlist, name, nets, params, size)
, m_pitch(m_pitch_ABS ? m_pitch_ABS : (((size + 0) + 7) / 8) * 8)
, m_new_V(size)
, m_A(size, m_pitch)
, m_RHS(size)
{
this->build_mat_ptr(size, m_A);
this->build_mat_ptr(m_A);
}
} // namespace solver

4
src/lib/netlist/solver/nld_ms_direct1.h
View File

@ -34,8 +34,8 @@ namespace solver
// ----------------------------------------------------------------------------------------
unsigned vsolve_non_dynamic(const bool newton_raphson) override
{
this->clear_square_mat(1, this->m_A);
this->fill_matrix(1, this->m_RHS);
this->clear_square_mat(this->m_A);
this->fill_matrix(this->m_RHS);
std::array<FT, 1> new_V = { this->m_RHS[0] / this->m_A[0][0] };

4
src/lib/netlist/solver/nld_ms_direct2.h
View File

@ -34,8 +34,8 @@ namespace solver
{}
unsigned vsolve_non_dynamic(const bool newton_raphson) override
{
this->clear_square_mat(2, this->m_A);
this->fill_matrix(2, this->m_RHS);
this->clear_square_mat(this->m_A);
this->fill_matrix(this->m_RHS);
const float_type a = this->m_A[0][0];
const float_type b = this->m_A[0][1];

34
src/lib/netlist/solver/nld_ms_gcr.h
View File

@ -26,25 +26,22 @@ namespace solver
{
template <typename FT, int SIZE>
class matrix_solver_GCR_t: public matrix_solver_t
class matrix_solver_GCR_t: public matrix_solver_ext_t<FT, SIZE>
{
public:
using mat_type = plib::pGEmatrix_cr_t<plib::pmatrix_cr_t<FT, SIZE>>;
// FIXME: dirty hack to make this compile
static constexpr const std::size_t storage_N = 100;
matrix_solver_GCR_t(netlist_state_t &anetlist, const pstring &name,
const analog_net_t::list_t &nets,
const solver_parameters_t *params, const std::size_t size)
: matrix_solver_t(anetlist, name, nets, params)
, m_dim(size)
: matrix_solver_ext_t<FT, SIZE>(anetlist, name, nets, params, size)
, RHS(size)
, new_V(size)
, mat(static_cast<typename mat_type::index_type>(size))
, m_proc()
{
const std::size_t iN = this->N();
const std::size_t iN = this->size();
/* build the final matrix */
@ -65,7 +62,7 @@ namespace solver
auto gr = mat.gaussian_extend_fill_mat(fill);
log_fill(fill, mat);
this->log_fill(fill, mat);
mat.build_from_fill_mat(fill);
@ -79,13 +76,13 @@ namespace solver
for (auto i = mat.row_idx[k]; i < mat.row_idx[k+1]; i++)
if (other == static_cast<int>(mat.col_idx[i]))
{
m_mat_ptr[k][j] = &mat.A[i];
this->m_mat_ptr[k][j] = &mat.A[i];
cnt++;
break;
}
}
nl_assert(cnt == this->m_terms[k].railstart());
m_mat_ptr[k][this->m_terms[k].railstart()] = &mat.A[mat.diag[k]];
this->m_mat_ptr[k][this->m_terms[k].railstart()] = &mat.A[mat.diag[k]];
}
this->log().verbose("maximum fill: {1}", gr.first);
@ -96,7 +93,7 @@ namespace solver
// FIXME: Move me
if (state().lib().isLoaded())
if (this->state().lib().isLoaded())
{
pstring symname = static_compile_name();
m_proc.load(this->state().lib(), symname);
@ -111,8 +108,6 @@ namespace solver
}
}
constexpr std::size_t N() const { return m_dim; }
unsigned vsolve_non_dynamic(const bool newton_raphson) override;
std::pair<pstring, pstring> create_solver_code() override;
@ -125,7 +120,6 @@ namespace solver
pstring static_compile_name();
const std::size_t m_dim;
plib::parray<FT, SIZE> RHS;
plib::parray<FT, SIZE> new_V;
@ -142,7 +136,7 @@ namespace solver
template <typename FT, int SIZE>
void matrix_solver_GCR_t<FT, SIZE>::generate_code(plib::putf8_fmt_writer &strm)
{
const std::size_t iN = N();
const std::size_t iN = this->size();
for (std::size_t i = 0; i < mat.nz_num; i++)
strm("double m_A{1} = m_A[{2}];\n", i, i);
@ -232,13 +226,9 @@ namespace solver
template <typename FT, int SIZE>
unsigned matrix_solver_GCR_t<FT, SIZE>::vsolve_non_dynamic(const bool newton_raphson)
{
const std::size_t iN = this->N();
mat.set_scalar(0.0);
/* populate matrix */
this->fill_matrix(iN, RHS);
mat.set_scalar(0.0);
this->fill_matrix(RHS);
/* now solve it */
@ -257,8 +247,8 @@ namespace solver
this->m_stat_calculations++;
const FT err = (newton_raphson ? delta(new_V) : 0.0);
store(new_V);
const FT err = (newton_raphson ? this->delta(new_V) : 0.0);
this->store(new_V);
return (err > this->m_params.m_accuracy) ? 2 : 1;
}

2
src/lib/netlist/solver/nld_ms_gmres.h
View File

@ -106,7 +106,7 @@ namespace solver
m_ops.m_mat.set_scalar(0.0);
/* populate matrix and V for first estimate */
this->fill_matrix(iN, RHS);
this->fill_matrix(RHS);
for (std::size_t k = 0; k < iN; k++)
{

35
src/lib/netlist/solver/nld_ms_sm.h
View File

@ -45,7 +45,7 @@ namespace solver
{
template <typename FT, int SIZE>
class matrix_solver_sm_t: public matrix_solver_t
class matrix_solver_sm_t: public matrix_solver_ext_t<FT, SIZE>
{
friend class matrix_solver_t;
@ -59,11 +59,10 @@ namespace solver
matrix_solver_sm_t(netlist_state_t &anetlist, const pstring &name,
const analog_net_t::list_t &nets,
const solver_parameters_t *params, const std::size_t size)
: matrix_solver_t(anetlist, name, nets, params)
, m_dim(size)
: matrix_solver_ext_t<FT, SIZE>(anetlist, name, nets, params, size)
, m_cnt(0)
{
this->build_mat_ptr(this->size(), m_A);
this->build_mat_ptr(m_A);
}
void reset() override { matrix_solver_t::reset(); }
@ -72,8 +71,6 @@ namespace solver
unsigned vsolve_non_dynamic(const bool newton_raphson) override;
unsigned solve_non_dynamic(const bool newton_raphson);
constexpr std::size_t size() const { return m_dim; }
void LE_invert();
template <typename T>
@ -109,7 +106,6 @@ namespace solver
//float_ext_type m_RHSx[storage_N];
const std::size_t m_dim;
std::size_t m_cnt;
};
@ -121,7 +117,7 @@ namespace solver
template <typename FT, int SIZE>
void matrix_solver_sm_t<FT, SIZE>::LE_invert()
{
const std::size_t kN = size();
const std::size_t kN = this->size();
for (std::size_t i = 0; i < kN; i++)
{
@ -137,13 +133,13 @@ namespace solver
{
/* FIXME: Singular matrix? */
const float_type f = 1.0 / W(i,i);
const auto * const p = m_terms[i].m_nzrd.data();
const std::size_t e = m_terms[i].m_nzrd.size();
const auto * const p = this->m_terms[i].m_nzrd.data();
const std::size_t e = this->m_terms[i].m_nzrd.size();
/* Eliminate column i from row j */
const auto * const pb = m_terms[i].m_nzbd.data();
const std::size_t eb = m_terms[i].m_nzbd.size();
const auto * const pb = this->m_terms[i].m_nzbd.data();
const std::size_t eb = this->m_terms[i].m_nzbd.size();
for (std::size_t jb = 0; jb < eb; jb++)
{
const unsigned j = pb[jb];
@ -186,7 +182,7 @@ namespace solver
void matrix_solver_sm_t<FT, SIZE>::LE_compute_x(
T & x)
{
const std::size_t kN = size();
const std::size_t kN = this->size();
for (std::size_t i=0; i<kN; i++)
x[i] = 0.0;
@ -204,7 +200,7 @@ namespace solver
unsigned matrix_solver_sm_t<FT, SIZE>::solve_non_dynamic(const bool newton_raphson)
{
static constexpr const bool incremental = true;
const std::size_t iN = size();
const std::size_t iN = this->size();
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
std::array<float_type, storage_N> new_V;
@ -232,7 +228,7 @@ namespace solver
{
std::size_t colcount = 0;
auto &nz = m_terms[row].m_nz;
auto &nz = this->m_terms[row].m_nz;
for (unsigned & col : nz)
{
v[col] = A(row,col) - lA(row,col);
@ -279,8 +275,8 @@ namespace solver
this->LE_compute_x(new_V);
const float_type err = (newton_raphson ? delta(new_V) : 0.0);
store(new_V);
const float_type err = (newton_raphson ? this->delta(new_V) : 0.0);
this->store(new_V);
return (err > this->m_params.m_accuracy) ? 2 : 1;
}
@ -288,9 +284,8 @@ namespace solver
unsigned matrix_solver_sm_t<FT, SIZE>::vsolve_non_dynamic(const bool newton_raphson)
{
const std::size_t iN = this->size();
this->clear_square_mat(iN, this->m_A);
this->fill_matrix(iN, this->m_RHS);
this->clear_square_mat(this->m_A);
this->fill_matrix(this->m_RHS);
this->m_stat_calculations++;
return this->solve_non_dynamic(newton_raphson);

5
src/lib/netlist/solver/nld_ms_sor_mat.h
View File

@ -114,11 +114,10 @@ namespace solver
* Need something like that for gaussian elimination as well.
*/
const std::size_t iN = this->size();
this->clear_square_mat(iN, this->m_A);
this->fill_matrix(iN, this->m_RHS);
this->clear_square_mat(this->m_A);
this->fill_matrix(this->m_RHS);
bool resched = false;

39
src/lib/netlist/solver/nld_ms_w.h
View File

@ -52,7 +52,7 @@ namespace solver
{
template <typename FT, int SIZE>
class matrix_solver_w_t: public matrix_solver_t
class matrix_solver_w_t: public matrix_solver_ext_t<FT, SIZE>
{
friend class matrix_solver_t;
@ -66,11 +66,10 @@ namespace solver
matrix_solver_w_t(netlist_state_t &anetlist, const pstring &name,
const analog_net_t::list_t &nets,
const solver_parameters_t *params, const std::size_t size)
: matrix_solver_t(anetlist, name, nets, params)
: matrix_solver_ext_t<FT, SIZE>(anetlist, name, nets, params, size)
, m_cnt(0)
, m_dim(size)
{
this->build_mat_ptr(this->size(), m_A);
this->build_mat_ptr(m_A);
}
void reset() override { matrix_solver_t::reset(); }
@ -79,8 +78,6 @@ namespace solver
unsigned vsolve_non_dynamic(const bool newton_raphson) override;
unsigned solve_non_dynamic(const bool newton_raphson);
constexpr std::size_t size() const { return m_dim; }
void LE_invert();
template <typename T>
@ -121,11 +118,6 @@ namespace solver
std::array<unsigned, storage_N> colcount;
unsigned m_cnt;
//float_ext_type m_RHSx[storage_N];
const std::size_t m_dim;
};
// ----------------------------------------------------------------------------------------
@ -135,7 +127,7 @@ namespace solver
template <typename FT, int SIZE>
void matrix_solver_w_t<FT, SIZE>::LE_invert()
{
const std::size_t kN = size();
const std::size_t kN = this->size();
for (std::size_t i = 0; i < kN; i++)
{
@ -151,13 +143,13 @@ namespace solver
{
/* FIXME: Singular matrix? */
const float_type f = 1.0 / W(i,i);
const auto * const p = m_terms[i].m_nzrd.data();
const size_t e = m_terms[i].m_nzrd.size();
const auto * const p = this->m_terms[i].m_nzrd.data();
const size_t e = this->m_terms[i].m_nzrd.size();
/* Eliminate column i from row j */
const auto * const pb = m_terms[i].m_nzbd.data();
const size_t eb = m_terms[i].m_nzbd.size();
const auto * const pb = this->m_terms[i].m_nzbd.data();
const size_t eb = this->m_terms[i].m_nzbd.size();
for (std::size_t jb = 0; jb < eb; jb++)
{
const auto j = pb[jb];
@ -199,7 +191,7 @@ namespace solver
void matrix_solver_w_t<FT, SIZE>::LE_compute_x(
T & x)
{
const std::size_t kN = size();
const std::size_t kN = this->size();
for (std::size_t i=0; i<kN; i++)
x[i] = 0.0;
@ -217,7 +209,7 @@ namespace solver
template <typename FT, int SIZE>
unsigned matrix_solver_w_t<FT, SIZE>::solve_non_dynamic(const bool newton_raphson)
{
const auto iN = size();
const auto iN = this->size();
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
std::array<float_type, storage_N> new_V;
@ -245,7 +237,7 @@ namespace solver
for (unsigned row = 0; row < iN; row ++)
{
unsigned cc=0;
auto &nz = m_terms[row].m_nz;
auto &nz = this->m_terms[row].m_nz;
for (auto & col : nz)
{
if (A(row,col) != lA(row,col))
@ -349,17 +341,16 @@ namespace solver
plib::perrlogger("{} failed on row {}: {} RHS: {}\n", this->name(), i, std::abs(tmp-RHS(i)), RHS(i));
}
const float_type err = (newton_raphson ? delta(new_V) : 0.0);
store(new_V);
const float_type err = (newton_raphson ? this->delta(new_V) : 0.0);
this->store(new_V);
return (err > this->m_params.m_accuracy) ? 2 : 1;
}
template <typename FT, int SIZE>
unsigned matrix_solver_w_t<FT, SIZE>::vsolve_non_dynamic(const bool newton_raphson)
{
const std::size_t iN = this->size();
this->clear_square_mat(iN, this->m_A);
this->fill_matrix(iN, this->m_RHS);
this->clear_square_mat(this->m_A);
this->fill_matrix(this->m_RHS);
this->m_stat_calculations++;
return this->solve_non_dynamic(newton_raphson);