def __init__(self, par, mesh, _vars, params, observables):
self.par = par
self.mesh = mesh
self.vars = _vars
self.params = params
self.fixed_vortices = self.params.fixed_vortices
self.observables = observables
self.solveA = self.params.solveA
self.__order_parameter_phase_lock_krnl = self.par.get_function(
'order_parameter_phase_lock')
self.__iterate_order_parameter_jacobi_step_krnl = self.par.get_function(
'iterate_order_parameter_jacobi_step')
self.__iterate_vector_potential_jacobi_step_krnl = self.par.get_function(
'iterate_vector_potential_jacobi_step')
self.__xpy_r_krnl = self.par.get_function('xpy_r')
self.__xmy_r_krnl = self.par.get_function('xmy_r')
self._random_t = np.uint32(1)
if cfg.random_seed is not None:
self._random_t = np.uint32(cfg.random_seed)
# Alloc the rhs arrays
self.vars._tmp_node_var = GArray(like=self.vars._psi)
shapes = [(cfg.Nxa, cfg.Nya), (cfg.Nxb, cfg.Nyb)]
self.vars._tmp_edge_var = GArray(shape=shapes, dtype=cfg.dtype)
A_size = self.vars.vector_potential_h().size
self.__gab_next = gpuarray.zeros(A_size, dtype=cfg.dtype)
self.__gpsi_next = gpuarray.empty_like(self.vars.order_parameter_h())
self.__gr2_max = gpuarray.zeros(1, dtype=np.int32)
# Adjust stopping criteria according to precision
if cfg.dtype is np.float32:
if cfg.stop_criterion_order_parameter < 1e-6:
cfg.stop_criterion_order_parameter = 1e-6
if cfg.stop_criterion_vector_potential < 1e-6:
cfg.stop_criterion_vector_potential = 1e-6
else:
if cfg.stop_criterion_order_parameter < 1e-12:
cfg.stop_criterion_order_parameter = 1e-12
if cfg.stop_criterion_vector_potential < 1e-12:
cfg.stop_criterion_vector_potential = 1e-12
cfg.stop_criterion_order_parameter = cfg.dtype(
cfg.stop_criterion_order_parameter)
cfg.stop_criterion_vector_potential = cfg.dtype(
cfg.stop_criterion_vector_potential)
def __init__(self, mesh, vars):
self.mesh = mesh
self.vars = vars
self.fixed_vortices = FixedVortices(self.mesh, self.vars)
self.solveA = False
self.linear_coefficient = cfg.linear_coefficient # epsilon
self.gl_parameter = cfg.gl_parameter # kappa
self.normal_conductivity = cfg.normal_conductivity # sigma
# homogeneous external magnetic field
self._H = cfg.dtype(0.0)
self.homogeneous_external_field_reset = cfg.homogeneous_external_field
# x- and y- components of external vector potential for non-homogeneous external magnetic field
self.ae, self.be = None, None
# external and irregular vector potential
# it should be kept self._vpei = (self.ae, self.be) + (ai, bi)
self._vpei = None
# non-homogeneous external magnetic field
self.external_field = cfg.external_field
self.order_parameter_Langevin_coefficient = cfg.order_parameter_Langevin_coefficient
self.vector_potential_Langevin_coefficient = cfg.vector_potential_Langevin_coefficient
def test_sum_v(self, a_in, nv, ne, block_size=256):
assert ne is 5
assert block_size >= 0 and block_size <= 1024
ga_in = gpuarray.to_gpu(cfg.dtype(a_in))
a_out = self.gsum_v(ga_in, nv, ne, block_size=block_size)
return a_out
def gl_parameter(self, gl_parameter):
if gl_parameter is None or np.isnan(gl_parameter) or np.isinf(
gl_parameter):
gl_parameter = np.inf
assert isinstance(gl_parameter,
(np.floating, float, np.integer, int)) and (
np.isposinf(gl_parameter) or gl_parameter > 0.0)
self._kappa = cfg.dtype(gl_parameter)
self.solveA = np.bool(not np.isposinf(self._kappa))
def test_sum(self, a_in, N, block_size=256):
assert block_size >= 0 and block_size <= 1024
ga_in = gpuarray.to_gpu(cfg.dtype(a_in))
a_out = self.gsum(ga_in, block_size=block_size)
ga_in.gpudata.free()
return a_out
def _set_iterator_options(self,
iterator_type,
Nt=None,
dt=None,
T=None,
mandatory_definition=True):
assert iterator_type in ['order_parameter', 'vector_potential']
# if iterator_type == 'order_parameter':
# if Nt is None: Nt = self.Nt
# if dt is None: dt = self.dt
# if T is None: T = self.T
# elif iterator_type == 'vector_potential':
# if Nt is None: Nt = self.NtA
# if dt is None: dt = self.dtA
# if T is None: T = self.TA
if Nt is not None and T is not None and dt is None:
dt = float(T) / Nt
elif Nt is not None and T is None and dt is not None:
T = float(dt) * Nt
elif Nt is None and T is not None and dt is not None:
Nt = int(np.round(T / dt))
elif Nt is not None and T is not None and dt is not None:
assert np.isclose(T, dt * Nt)
if mandatory_definition:
assert isinstance(
dt, (np.floating, float, np.integer, int)) and dt >= 0.0
assert isinstance(Nt, (np.integer, int)) and Nt >= 0
if iterator_type == 'order_parameter':
self.Nt = np.int32(Nt) if Nt is not None else None
self.dt = cfg.dtype(dt) if dt is not None else None
self.T = cfg.dtype(T) if T is not None else None
elif iterator_type == 'vector_potential':
self.NtA = np.int32(Nt) if Nt is not None else None
self.dtA = cfg.dtype(dt) if dt is not None else None
self.TA = cfg.dtype(T) if T is not None else None
def _update_vector_potential(self, homogeneous_external_field, reset):
assert isinstance(homogeneous_external_field,
(np.floating, float, np.integer, int))
if reset:
self._H = cfg.dtype(homogeneous_external_field)
# TODO: need a fill method in GArray
# self.a.fill(0.0)
# self.b.fill(0.0)
a, b = self.vars._vp.get_vec_h()
a.fill(0.0)
b.fill(0.0)
self.vars._vp.need_htod_sync()
self.vars._vp.sync()
delta_H = self._H
else:
delta_H = -self._H
self._H = cfg.dtype(homogeneous_external_field)
delta_H += self._H
self.vars._vp.sync()
# TODO: implement GPU version of ab initialization
# Possible set of gauges, A = [g*y*H, (1-g)*x*H, 0] with any g, 0 <= g <= 1
g = 0.5
_, yg = self.mesh.xy_a_grid
xg, _ = self.mesh.xy_b_grid
a, b = self.vars._vp.get_vec_h()
a -= g * (yg - 0.5 * cfg.Ly) * delta_H
b += (1.0 - g) * (xg - 0.5 * cfg.Lx) * delta_H
self.vars._vp.need_htod_sync()
self.vars._vp.sync()
def __init__(
self,
Nx=None,
dx=None,
Lx=None, # geometry
Ny=None,
dy=None,
Ly=None,
Nt=None,
dt=None,
T=None, # parameters for order parameter iterator
NtA=None,
dtA=None,
TA=None, # parameters for order vector potential
material_tiling=None,
order_parameter='random',
random_seed=None,
random_level=1.0,
gl_parameter=np.
inf, # = lambda/xi, GL parameter; if None, np.nan, or np.inf then solveA = False
normal_conductivity=1.0, # normal-state conductivity
linear_coefficient=1.0, # linear coefficient in GL equation
homogeneous_external_field=0.0,
external_field=0.0,
fixed_vortices=None,
fixed_vortices_correction='cell centers',
phase_lock_radius=None,
device_id=0,
dtype=np.float64,
stop_criterion_order_parameter=1e-6,
stop_criterion_vector_potential=1e-6,
order_parameter_Langevin_coefficient=0.0,
vector_potential_Langevin_coefficient=0.0,
convergence_rtol=1e-6, # relative tolerance for convergence
):
self.dtypes = (np.float32, np.float64)
cfg.device_id = device_id
assert dtype in self.dtypes
cfg.dtype = dtype
cfg.dtype_complex = {
np.float32: np.complex64,
np.float64: np.complex128
}[cfg.dtype]
if Nx is not None and Lx is not None and dx is None:
dx = float(Lx) / (Nx - 1)
elif Nx is not None and Lx is None and dx is not None:
Lx = float(dx) * (Nx - 1)
elif Nx is None and Lx is not None and dx is not None:
Nx = int(np.round(Lx / dx) + 1)
elif Nx is not None and Lx is not None and dx is not None:
assert np.isclose(Lx, dx * (Nx - 1))
else:
raise 'Two out of three Nx, Lx, dx must be defined'
if Ny is not None and Ly is not None and dy is None:
dy = float(Ly) / (Ny - 1)
elif Ny is not None and Ly is None and dy is not None:
Ly = float(dy) * (Ny - 1)
elif Ny is None and Ly is not None and dy is not None:
Ny = int(np.round(Ly / dy) + 1)
elif Ny is not None and Ly is not None and dy is not None:
assert np.isclose(Ly, dy * (Ny - 1))
else:
raise 'Two out of three Ny, Ly, and dy must be defined'
assert isinstance(Lx,
(np.floating, float, np.integer, int)) and Lx > 0.0
assert isinstance(Ly,
(np.floating, float, np.integer, int)) and Ly > 0.0
assert isinstance(Nx, (np.integer, int)) and Nx >= 4
assert isinstance(Ny, (np.integer, int)) and Ny >= 4
cfg.Nx, cfg.Ny = np.int32(Nx), np.int32(Ny)
cfg.Lx, cfg.Ly = cfg.dtype(Lx), cfg.dtype(Ly)
cfg.dx, cfg.dy = cfg.dtype(dx), cfg.dtype(dy)
cfg.N = cfg.Nx * cfg.Ny
# number of centers of horizontal edges excluding boundaries
cfg.Nxa, cfg.Nya = cfg.Nx - 1, cfg.Ny
# number of centers of vertical edges excluding boundaries
cfg.Nxb, cfg.Nyb = cfg.Nx, cfg.Ny - 1
cfg.Na, cfg.Nb = cfg.Nxa * cfg.Nya, cfg.Nxb * cfg.Nyb
cfg.Nab = cfg.Na + cfg.Nb
# number of cells
cfg.Nxc, cfg.Nyc = cfg.Nx - 1, cfg.Ny - 1
cfg.Nc = cfg.Nxc * cfg.Nyc
cfg.idx, cfg.idy = 1.0 / cfg.dx, 1.0 / cfg.dy
cfg.idx2, cfg.idy2 = cfg.idx * cfg.idx, cfg.idy * cfg.idy
cfg.idx2, cfg.idy2, cfg.idxy = cfg.idx * cfg.idx, cfg.idy * cfg.idy, cfg.idx * cfg.idy
cfg.j_dx, cfg.j_dy = 1.0j * cfg.dx, 1.0j * cfg.dy
cfg.material_tiling = material_tiling
cfg.order_parameter = order_parameter
cfg.random_seed = random_seed
cfg.random_level = random_level
cfg.gl_parameter = gl_parameter
cfg.linear_coefficient = linear_coefficient
cfg.normal_conductivity = normal_conductivity
cfg.homogeneous_external_field = homogeneous_external_field
cfg.external_field = external_field
cfg.fixed_vortices = fixed_vortices
cfg.fixed_vortices_correction = fixed_vortices_correction
cfg.phase_lock_radius = phase_lock_radius
cfg.order_parameter_Langevin_coefficient = order_parameter_Langevin_coefficient
cfg.vector_potential_Langevin_coefficient = vector_potential_Langevin_coefficient
cfg.Nt, cfg.dt, cfg.T = None, None, None
cfg.NtA, cfg.dtA, cfg.TA = None, None, None
assert isinstance(stop_criterion_order_parameter,
(np.floating, float, np.integer,
int)) and stop_criterion_order_parameter > 0.0
cfg.stop_criterion_order_parameter = cfg.dtype(
stop_criterion_order_parameter)
assert isinstance(stop_criterion_vector_potential,
(np.floating, float, np.integer,
int)) and stop_criterion_vector_potential > 0.0
cfg.stop_criterion_vector_potential = cfg.dtype(
stop_criterion_vector_potential)
# relative tolerance for convergence
cfg.convergence_rtol = convergence_rtol
self.cfg = cfg
self.par = GLPar.Startup()
self.par.red = GLPar.Reduction(self.par)
self.mesh = GLMesh.Grid()
self.vars = GLVars.Vars(self.par, self.mesh)
self.params = GLVars.Params(self.mesh, self.vars)
self.observables = GLObs.Observables(self.par, self.mesh, self.vars,
self.params)
self.solve = GLSolvers.Solvers(self.par, self.mesh, self.vars,
self.params, self.observables)
self.vortex_detector = GLObs.VortexDetector(self.vars, self.params,
self.solve)
def __iterate_vector_potential_gpu(self):
"""Performs dtA-iteration of self.a/self.b on GPU"""
# self.gabi += self.gab; no memory allocation
if self.fixed_vortices._vpi is not None:
self.__xpy_r_krnl(
self.fixed_vortices.irregular_vector_potential_h(),
self.vars.vector_potential_h(),
np.uint32(cfg.N),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size, 1, 1))
gabi_gab = self.fixed_vortices.irregular_vector_potential_h(
) # just a pointer
else:
gabi_gab = self.vars.vector_potential_h()
# similar to gab_rhs = gab.copy(), but does not allocate new array
Utils.copy_dtod(self.vars._tmp_edge_var_h(),
self.vars.vector_potential_h())
#self.vars._tmp_edge_var.need_dtoh_sync()
# if self.ab_langevin_c > 1e-16:
# self.gab_rhs += self.ab_langevin_c*(curand(self.gab_rhs.shape, dtype=cfg.dtype) - 0.5)
for j in range(1024):
self.__gr2_max.fill(np.int32(0))
self.__iterate_vector_potential_jacobi_step_krnl(
self.dt,
self.params.gl_parameter_squared_h(),
self.params._rho,
self.params.homogeneous_external_field,
self.mesh.material_tiling_h(),
self.vars.order_parameter_h(),
gabi_gab,
self.vars._tmp_edge_var_h(
), # ab for right-hand side; does not change during Jacobi interactions
self.vars.vector_potential_h(), # ab^{j} in Jacobi method
self.__gab_next, # ab^{j+1} in Jacobi method
self.params.vector_potential_Langevin_coefficient,
np.uint32(j),
self._random_t,
cfg.stop_criterion_vector_potential,
self.__gr2_max,
grid=(self.par.grid_size, 1, 1),
block=(self.par.block_size, 1, 1),
)
# swap pointers, does not change arrays
self.vars._vp._gdata, self.__gab_next = self.__gab_next, self.vars._vp._gdata
#self.vars.vector_potential_h(), self.__gab_next = self.__gab_next, self.vars.vector_potential_h()
# r2_max_norm = residual/stop_criterion
r2_max_norm = 1.0e-4 * cfg.dtype(self.__gr2_max.get()[0])
# convergence criteria
if r2_max_norm < 1.0:
break
self._random_t += np.uint32(1)
self.vars._vp.need_dtoh_sync()
# self.gabi -= self.gab; no memory allocation
if self.fixed_vortices._vpi is not None:
self.__xmy_r_krnl(
self.fixed_vortices.irregular_vector_potential_h(),
self.vars.vector_potential_h(),
np.uint32(cfg.N),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size, 1, 1))
def __iterate_order_parameter_gpu(self, gab_gabi):
"""Performs dt-iteration of self.psi on GPU"""
# similar to gpsi_rhs = gpsi.copy(), but does not allocate new array
Utils.copy_dtod(self.vars._tmp_node_var_h(),
self.vars.order_parameter_h())
#self.vars._tmp_node_var.need_dtoh_sync()
for j in range(1024):
self.__gr2_max.fill(np.int32(0))
# TODO: prepare all cuda calls
self.__iterate_order_parameter_jacobi_step_krnl(
self.dt,
self.params.linear_coefficient_scalar_h(),
self.params.linear_coefficient_h(),
self.mesh.material_tiling_h(),
gab_gabi,
self.vars._tmp_node_var_h(
), # psi for right-hand side; does not change during Jacobi interactions
self.vars.order_parameter_h(), # psi^{j} in Jacobi method
self.__gpsi_next, # psi^{j+1} in Jacobi method
self.params.order_parameter_Langevin_coefficient,
np.uint32(j),
self._random_t,
cfg.stop_criterion_order_parameter,
self.__gr2_max,
grid=(self.par.grid_size, 1, 1),
block=(self.par.block_size, 1, 1),
)
# swap pointers, does not change arrays
# TODO: this is hard-wired for now since python doesn't allow
# assignment for a function call.Sync Status not updated
self.vars._psi._gdata, self.__gpsi_next = self.__gpsi_next, self.vars._psi._gdata
#self.vars.order_parameter_h(), self.__gpsi_next = self.__gpsi_next, self.vars.order_parameter_h()
# residual = max{|b-M*psi|}
# r2_max_norm = residual/stop_criterion
r2_max_norm = 1.0e-4 * cfg.dtype(self.__gr2_max.get()[0])
# convergence criteria
if r2_max_norm < 1.0:
break
self._random_t += np.uint32(1)
if self.fixed_vortices._phase_lock_ns is not None:
block_size = 2
grid_size = Utils.intceil(self.fixed_vortices._phase_lock_ns.size,
block_size)
self.__order_parameter_phase_lock_krnl(
self.vars.order_parameter_h(),
np.int32(self.fixed_vortices._phase_lock_ns.size),
self.fixed_vortices._phase_lock_ns_h(),
grid=(grid_size, 1, 1),
block=(block_size, 1, 1),
)
self.vars._psi.need_dtoh_sync()
def __free_energy_minimization(self, n_iter=1000):
"""Minimizes energy with respect to order parameter and vector potential"""
# TODO: check material tiling
# TODO: add external vector potential
# TODO: add phase lock gridpoints
# TODO: Ideally there should be one entry for both minimzation
self.vars._psi.sync()
self.vars._vp.sync()
self.cg_energies = [] # TMP
#beta_psi = 0.0 # First iteration is steepest descent, so make beta = 0.0
#beta_A = 0.0 # First iteration is steepest descent, so make beta = 0.0
# gpu arrays:
# (g)dir : search direction
# (g)jac : gradient
# (g)jac_prev: gradient from previous iteration
#cuda.start_profiler()
self.vars._alloc_free_temporary_gpu_storage('alloc')
for i in range(n_iter):
# 1. Compute jacobians
self.__gjac_psi = self._free_energy_jacobian_psi
self.__gjac_A = self._free_energy_jacobian_A
# 2. Compute betas
# use Polak–Ribière formula with resetting
# TODO: consider other formulas, see e.g. https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method
if i > 0:
self.__compute_beta_psi(self.__gjac_psi, self.__gjac_psi_prev)
self.__compute_beta_A(self.__gjac_A, self.__gjac_A_prev)
# 3. Update search directions
self.__axmy_c_krnl(self.__gdir_psi,
self.__gjac_psi,
self.__gdir_psi,
self._beta_psi,
np.uint32(cfg.N),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size, 1, 1))
self.__axmy_r_krnl(self.__gdir_A,
self.__gjac_A,
self.__gdir_A,
self._beta_A,
np.uint32(cfg.Nab),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size_A, 1, 1))
# 4. Compute alphas
self._free_energy_conjgrad_coef(self.__gdir_psi, self.__gdir_A)
alpha_psi, alpha_A = self._cg_alpha_min()
#print('iter: ', i, 'c: ', self.__c, 'alpha, beta: ', alpha_psi, alpha_A, beta_psi, beta_A, flush=True)
# 5. Update variables
self.__axpy_c_krnl(self.__gdir_psi,
self.vars.order_parameter_h(),
self.vars.order_parameter_h(),
cfg.dtype(alpha_psi),
np.uint32(cfg.N),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size, 1, 1))
self.__axpy_r_krnl(self.__gdir_A,
self.vars.vector_potential_h(),
self.vars.vector_potential_h(),
cfg.dtype(alpha_A),
np.uint32(cfg.Nab),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size_A, 1, 1))
# 6. Save previous step
Utils.copy_dtod(self.__gjac_psi_prev, self.__gjac_psi)
Utils.copy_dtod(self.__gjac_A_prev, self.__gjac_A)
E0 = self.observables.free_energy # TMP
self.cg_energies.append(E0) # TMP
# if i%10 == 0:
# print('%3.d: E = %10.10f' % (i, E0)) # TMP
if (i > 0
and np.abs(self.cg_energies[i] / self.cg_energies[i - 1] -
1.0) < self.__convergence_rtol):
#print('CG converged in %d iterations with residual %g ' % ( i, np.abs(self.cg_energies[i]/self.cg_energies[i-1] - 1.0)))
break
#cuda.stop_profiler()
self.vars._psi.need_dtoh_sync()
self.vars._vp.need_dtoh_sync()
self.vars._alloc_free_temporary_gpu_storage('free')
def gl_parameter_squared_h(self):
if self.solveA:
return cfg.dtype(self.gl_parameter**2)
return cfg.dtype(-1.0)
def linear_coefficient_scalar_h(self):
if self._epsilon.size == 1:
return self._epsilon.get_h()
return cfg.dtype(0.0)
def vector_potential_Langevin_coefficient(
self, vector_potential_Langevin_coefficient):
assert isinstance(vector_potential_Langevin_coefficient,
(np.floating, float, np.integer, int))
self._ab_langevin_c = cfg.dtype(vector_potential_Langevin_coefficient)
def order_parameter_Langevin_coefficient(
self, order_parameter_Langevin_coefficient):
assert isinstance(order_parameter_Langevin_coefficient,
(np.floating, float, np.integer, int))
self._psi_langevin_c = cfg.dtype(order_parameter_Langevin_coefficient)
def homogeneous_external_field(self, homogeneous_external_field):
self._H = cfg.dtype(homogeneous_external_field)
def __free_energy_minimization_psi(self, n_iter=1000):
"""Minimizes energy with respect to order parameter"""
# NOTE: Tests show that
# - CG minimization is much faster than TD for 1-2 vortices (at least current implementation)
# - for ~30 of vortices CG demonstrates similar "performance" as TD
# NOTE: works with material tiling
# TODO: add external vector potential
# TODO: add phase lock gridpoints
assert not self.params.solveA
self.vars._psi.sync()
self.vars._vp.sync()
self.cg_energies = [] # TMP
#beta = 0.0 # First iteration is steepest descent, so make beta = 0.0
# gpu arrays:
# (g)dir : search direction
# (g)jac : gradient
# (g)jac_prev: gradient from previous iteration
self.vars._alloc_free_temporary_gpu_storage('alloc')
self.__gdir_psi.fill(0.0)
for i in range(n_iter):
# 1. Compute jacobians
self.__gjac_psi = self._free_energy_jacobian_psi
# 2. Compute beta
# Polak–Ribière formula
# TODO: consider other formulas, see e.g. https://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method
if i > 0:
# d*(d-dp)/(dp*dp)
# = d.re, d.im]*([d.re-dp.re, d.im-dp.im])/([dp.re, dp.im]*[dp.re, dp.im])
# = (d.re*(d.re-dp.re) + d.im*(d.im-dp.im))/(dp.re*dp.re + dp.im*dp.im)
# = (j.re*(j.re-jp.re) + j.im*(j.im-jp.im))/(jp.re*jp.re + jp.im*jp.im)
# where j = -d
self.__compute_beta_psi(self.__gjac_psi, self.__gjac_psi_prev)
# 3. Update search direction
self.__axmy_c_krnl(self.__gdir_psi,
self.__gjac_psi,
self.__gdir_psi,
self._beta_psi,
np.uint32(cfg.N),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size, 1, 1))
# 4. Compute alpha
self._free_energy_conjgrad_coef_psi(self.__gdir_psi)
alpha0 = self._cg_alpha_psi_min()
#print('iter: ', i, 'F: ', c0, c1, c2, c3, c4, 'alpha: ', alpha0, 'beta: ', beta, flush=True)
# 5. Update variables
self.__axpy_c_krnl(self.__gdir_psi,
self.vars.order_parameter_h(),
self.vars.order_parameter_h(),
cfg.dtype(alpha0),
np.uint32(cfg.N),
block=(self.par.block_size, 1, 1),
grid=(self.par.grid_size, 1, 1))
# 6. Save
Utils.copy_dtod(self.__gjac_psi_prev, self.__gjac_psi)
E0 = self.observables.free_energy # TMP
self.cg_energies.append(E0) # TMP
# if i%10 == 0:
# print('%3.d: E = %10.10f' % (i, E0)) # TMP
if (i > 0
and np.abs(self.cg_energies[i] / self.cg_energies[i - 1] -
1.0) < self.__convergence_rtol):
#print('CG converged in %d iterations with residual %g ' % ( i, np.abs(self.cg_energies[i]/self.cg_energies[i-1] - 1.0)))
break
self.vars._psi.need_dtoh_sync()
self.vars._alloc_free_temporary_gpu_storage('free')
def normal_conductivity(self, normal_conductivity):
assert isinstance(normal_conductivity,
(np.floating, float, np.integer,
int)) and normal_conductivity > 0.0
self._sigma = cfg.dtype(normal_conductivity)
self._rho = cfg.dtype(1.0 / normal_conductivity)
