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// license:GPL-2.0+
// copyright-holders:Couriersud
/*
* nld_matrix_solver.h
*
*/
#ifndef NLD_MATRIX_SOLVER_H_
#define NLD_MATRIX_SOLVER_H_
#include "netlist/nl_base.h"
#include "netlist/nl_errstr.h"
#include "plib/palloc.h"
#include "plib/pmatrix2d.h"
#include "plib/putil.h"
#include "plib/vector_ops.h"
#include <cmath>
namespace netlist
{
namespace devices
{
/* FIXME: these should become proper devices */
struct solver_parameters_t
{
bool m_pivot;
nl_double m_accuracy;
nl_double m_dynamic_lte;
nl_double m_min_timestep;
nl_double m_max_timestep;
nl_double m_gs_sor;
bool m_dynamic_ts;
std::size_t m_gs_loops;
std::size_t m_nr_loops;
netlist_time m_nr_recalc_delay;
bool m_use_gabs;
bool m_use_linear_prediction;
};
class terms_for_net_t : plib::nocopyassignmove
{
public:
terms_for_net_t();
void clear();
void add(terminal_t *term, int net_other, bool sorted);
std::size_t count() const { return m_terms.size(); }
terminal_t **terms() { return m_terms.data(); }
std::size_t m_railstart;
std::vector<unsigned> m_nz; /* all non zero for multiplication */
std::vector<unsigned> m_nzrd; /* non zero right of the diagonal for elimination, may include RHS element */
std::vector<unsigned> m_nzbd; /* non zero below of the diagonal for elimination */
/* state */
nl_double m_last_V;
nl_double m_DD_n_m_1;
nl_double m_h_n_m_1;
std::vector<int> m_connected_net_idx;
private:
std::vector<terminal_t *> m_terms;
};
class proxied_analog_output_t : public analog_output_t
{
public:
proxied_analog_output_t(core_device_t &dev, const pstring &aname, analog_net_t *pnet)
: analog_output_t(dev, aname)
, m_proxied_net(pnet)
{ }
analog_net_t *proxied_net() const { return m_proxied_net;}
private:
analog_net_t *m_proxied_net; // only for proxy nets in analog input logic
};
class matrix_solver_t : public device_t
{
public:
using list_t = std::vector<matrix_solver_t *>;
enum eSortType
{
NOSORT,
ASCENDING,
DESCENDING,
PREFER_IDENTITY_TOP_LEFT,
PREFER_BAND_MATRIX
};
void setup(analog_net_t::list_t &nets)
{
vsetup(nets);
}
void solve_base();
/* after every call to solve, update inputs must be called.
* this can be done as well as a batch to ease parallel processing.
*/
const netlist_time solve(netlist_time now);
void update_inputs();
bool has_dynamic_devices() const { return m_dynamic_devices.size() > 0; }
bool has_timestep_devices() const { return m_step_devices.size() > 0; }
void update_forced();
void update_after(const netlist_time after)
{
m_Q_sync.net().toggle_and_push_to_queue(after);
}
/* netdevice functions */
NETLIB_UPDATEI();
NETLIB_RESETI();
public:
int get_net_idx(detail::net_t *net);
std::pair<int, int> get_left_right_of_diag(std::size_t row, std::size_t diag);
double get_weight_around_diag(std::size_t row, std::size_t diag);
virtual void log_stats();
virtual std::pair<pstring, pstring> create_solver_code()
{
return std::pair<pstring, pstring>("", plib::pfmt("/* solver doesn't support static compile */\n\n"));
}
/* return number of floating point operations for solve */
std::size_t ops() { return m_ops; }
protected:
matrix_solver_t(netlist_state_t &anetlist, const pstring &name,
eSortType sort, const solver_parameters_t *params);
void sort_terms(eSortType sort);
void setup_base(analog_net_t::list_t &nets);
void update_dynamic();
virtual void vsetup(analog_net_t::list_t &nets) = 0;
virtual unsigned vsolve_non_dynamic(const bool newton_raphson) = 0;
netlist_time compute_next_timestep(const double cur_ts);
/* virtual */ void add_term(std::size_t net_idx, terminal_t *term);
template <typename T>
void store(const T & V);
template <typename T>
auto delta(const T & V) -> typename std::decay<decltype(V[0])>::type;
template <typename T>
void build_LE_A(T &child);
template <typename T>
void build_LE_RHS(T &child);
void set_pointers()
{
const std::size_t iN = this->m_nets.size();
std::size_t max_count = 0;
std::size_t max_rail = 0;
for (std::size_t k = 0; k < iN; k++)
{
max_count = std::max(max_count, m_terms[k]->count());
max_rail = std::max(max_rail, m_terms[k]->m_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);
m_connected_net_Vn.resize(iN, max_count);
for (std::size_t k = 0; k < iN; k++)
{
auto count = m_terms[k]->count();
for (std::size_t i = 0; i < count; i++)
{
m_terms[k]->terms()[i]->set_ptrs(&m_gtn[k][i], &m_gonn[k][i], &m_Idrn[k][i]);
m_connected_net_Vn[k][i] = m_terms[k]->terms()[i]->connected_terminal()->net().Q_Analog_state_ptr();
}
}
}
template <typename AP, typename FT>
void fill_matrix(std::size_t N, AP &tcr, FT &RHS)
{
for (std::size_t k = 0; k < N; k++)
{
auto *net = m_terms[k].get();
auto **tcr_r = &(tcr[k][0]);
const std::size_t term_count = net->count();
const std::size_t railstart = net->m_railstart;
const auto &go = m_gonn[k];
const auto > = 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>
using aligned_alloc = plib::aligned_allocator<T, PALIGN_VECTOROPT>;
plib::pmatrix2d<nl_double, aligned_alloc<nl_double>> m_gonn;
plib::pmatrix2d<nl_double, aligned_alloc<nl_double>> m_gtn;
plib::pmatrix2d<nl_double, aligned_alloc<nl_double>> m_Idrn;
plib::pmatrix2d<nl_double *, aligned_alloc<nl_double *>> m_mat_ptr;
plib::pmatrix2d<nl_double *, aligned_alloc<nl_double *>> m_connected_net_Vn;
plib::pmatrix2d<nl_double> m_test;
std::vector<plib::unique_ptr<terms_for_net_t>> m_terms;
std::vector<analog_net_t *> m_nets;
std::vector<pool_owned_ptr<proxied_analog_output_t>> m_inps;
std::vector<plib::unique_ptr<terms_for_net_t>> m_rails_temp;
const solver_parameters_t &m_params;
state_var<int> m_stat_calculations;
state_var<int> m_stat_newton_raphson;
state_var<int> m_stat_vsolver_calls;
state_var<int> m_iterative_fail;
state_var<int> m_iterative_total;
private:
state_var<netlist_time> m_last_step;
std::vector<core_device_t *> m_step_devices;
std::vector<core_device_t *> m_dynamic_devices;
logic_input_t m_fb_sync;
logic_output_t m_Q_sync;
/* calculate matrix */
void setup_matrix();
void step(const netlist_time &delta);
std::size_t m_ops;
const eSortType m_sort;
};
template <typename T>
auto matrix_solver_t::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();
typename std::decay<decltype(V[0])>::type cerr = 0;
for (std::size_t i = 0; i < iN; i++)
cerr = std::max(cerr, std::abs(V[i] - this->m_nets[i]->Q_Analog()));
return cerr;
}
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_nets[i]->set_Q_Analog(V[i]);
}
template <typename T>
void matrix_solver_t::build_LE_A(T &child)
{
using float_type = typename T::float_type;
static_assert(std::is_base_of<matrix_solver_t, T>::value, "T must derive from matrix_solver_t");
const std::size_t iN = child.size();
for (std::size_t k = 0; k < iN; k++)
{
terms_for_net_t *terms = m_terms[k].get();
float_type * Ak = &child.A(k, 0ul);
for (std::size_t i=0; i < iN; i++)
Ak[i] = 0.0;
const std::size_t terms_count = terms->count();
const std::size_t railstart = terms->m_railstart;
const float_type * const gt = m_gtn[k];
{
float_type akk = 0.0;
for (std::size_t i = 0; i < terms_count; i++)
akk += gt[i];
Ak[k] = akk;
}
const float_type * const go = m_gonn[k];
int * net_other = terms->m_connected_net_idx.data();
for (std::size_t i = 0; i < railstart; i++)
Ak[net_other[i]] += go[i];
}
}
template <typename T>
void matrix_solver_t::build_LE_RHS(T &child)
{
static_assert(std::is_base_of<matrix_solver_t, T>::value, "T must derive from matrix_solver_t");
using float_type = typename T::float_type;
const std::size_t iN = child.size();
for (std::size_t k = 0; k < iN; k++)
{
float_type rhsk_a = 0.0;
float_type rhsk_b = 0.0;
const std::size_t terms_count = m_terms[k]->count();
const float_type * const go = m_gonn[k];
const float_type * const Idr = m_Idrn[k];
const float_type * const * other_cur_analog = m_connected_net_Vn[k];
for (std::size_t i = 0; i < terms_count; i++)
rhsk_a = rhsk_a + Idr[i];
for (std::size_t i = m_terms[k]->m_railstart; i < terms_count; i++)
//rhsk = rhsk + go[i] * terms[i]->m_otherterm->net().as_analog().Q_Analog();
rhsk_b = rhsk_b - go[i] * *other_cur_analog[i];
child.RHS(k) = rhsk_a + rhsk_b;
}
}
} //namespace devices
} // namespace netlist
#endif /* NLD_MS_DIRECT_H_ */
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