def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
mm = MM(shape=n, dtype=np.double, prealloc=5)
blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK
gridsz = int(math.ceil(float(n) / blksz))
stream = cuda.stream()
prng = PRNG(PRNG.MRG32K3A, stream=stream)
# Allocate device side array
d_normdist = cuda.device_array(n, dtype=np.double, stream=stream)
c0 = interest - 0.5 * volatility**2
c1 = volatility * math.sqrt(dt)
d_last = cuda.to_device(paths[:, 0], to=mm.get())
for j in range(1, paths.shape[1]):
prng.normal(d_normdist, mean=0, sigma=1)
d_paths = cuda.to_device(paths[:, j], stream=stream, to=mm.get())
step(d_last, dt, c0, c1, d_normdist, out=d_paths, stream=stream)
d_paths.copy_to_host(paths[:, j], stream=stream)
mm.free(d_last)
d_last = d_paths
stream.synchronize()
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
mm = MM(shape=n, dtype=np.double, prealloc=5)
blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK
gridsz = int(math.ceil(float(n) / blksz))
stream = cuda.stream()
prng = PRNG(PRNG.MRG32K3A, stream=stream)
# Allocate device side array
d_normdist = cuda.device_array(n, dtype=np.double, stream=stream)
c0 = interest - 0.5 * volatility ** 2
c1 = volatility * math.sqrt(dt)
d_last = cuda.to_device(paths[:, 0], to=mm.get())
for j in range(1, paths.shape[1]):
prng.normal(d_normdist, mean=0, sigma=1)
d_paths = cuda.to_device(paths[:, j], stream=stream, to=mm.get())
step(d_last, dt, c0, c1, d_normdist, out=d_paths, stream=stream)
d_paths.copy_to_host(paths[:, j], stream=stream)
mm.free(d_last)
d_last = d_paths
stream.synchronize()
def initialize_ADK_parameters(self, element, dt):
"""
Initialize parameters needed for the calculation of ADK ionization rate
Parameters
----------
element: string
The atomic symbol of the considered ionizable species
(e.g. 'He', 'N' ; do not use 'Helium' or 'Nitrogen')
dt: float (in seconds)
The timestep of the simulation. (The calculated ionization
probability is a probability *per timestep*.)
See Chen, JCP 236 (2013), equation (2) for the ionization rate formula
"""
# Get the array of energies
Uion = get_ionization_energies(element)
# Check whether the element string was valid
if Uion is None:
raise ValueError("Unknown ionizable element %s.\n" %element + \
"Please use atomic symbol (e.g. 'He') not full name (e.g. Helium)")
else:
self.element = element
# Determine the maximum level of ionization
self.level_max = len(Uion)
# Calculate the ADK prefactors (See Chen, JCP 236 (2013), equation (2))
# - Scalars
alpha = physical_constants['fine-structure constant'][0]
r_e = physical_constants['classical electron radius'][0]
wa = alpha**3 * c / r_e
Ea = m_e * c**2 / e * alpha**4 / r_e
# - Arrays (one element per ionization level)
UH = get_ionization_energies('H')[0]
Z = np.arange(len(Uion)) + 1
n_eff = Z * np.sqrt(UH / Uion)
l_eff = n_eff[0] - 1
C2 = 2**(2 * n_eff) / (n_eff * gamma(n_eff + l_eff + 1) *
gamma(n_eff - l_eff))
# For now, we assume l=0, m=0
self.adk_power = -(2 * n_eff - 1)
self.adk_prefactor = dt * wa * C2 * ( Uion/(2*UH) ) \
* ( 2*(Uion/UH)**(3./2)*Ea )**(2*n_eff - 1)
self.adk_exp_prefactor = -2. / 3 * (Uion / UH)**(3. / 2) * Ea
# Prepare random number generator
if self.use_cuda:
self.prng = PRNG()
def getPseudoRandomNumbers_Standard_cuda(shape=tuple):
# type: (object) -> object
"""
generates a an array of psuedo random numbers from standard normal distribution using CUDA
:rtype: ndarray
:param length:
:return:
"""
prng = PRNG(rndtype=PRNG.XORWOW)
rand = empty(shape)
prng.normal(rand, 0, 1)
return rand
def getPseudoRandomNumbers_Uniform_cuda(length=int):
# type: (object) -> object
"""
generates a an array of psuedo random numbers from uniform distribution using CUDA
:rtype: ndarray
:param length:
:return:
"""
prng = PRNG(rndtype=PRNG.XORWOW)
rand = empty(length)
prng.uniform(rand)
return rand
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
num_streams = 2
part_width = int(math.ceil(float(n) / num_streams))
partitions = [(0, part_width)]
for i in range(1, num_streams):
begin, end = partitions[i - 1]
begin, end = end, min(end + (end - begin), n)
partitions.append((begin, end))
partlens = [end - begin for begin, end in partitions]
mm = MM(shape=part_width, dtype=np.double, prealloc=10 * num_streams)
device = cuda.get_current_device()
blksz = device.MAX_THREADS_PER_BLOCK
gridszlist = [int(math.ceil(float(partlen) / blksz))
for partlen in partlens]
strmlist = [cuda.stream() for _ in range(num_streams)]
prnglist = [PRNG(PRNG.MRG32K3A, stream=strm)
for strm in strmlist]
# Allocate device side array
d_normlist = [cuda.device_array(partlen, dtype=np.double, stream=strm)
for partlen, strm in zip(partlens, strmlist)]
c0 = interest - 0.5 * volatility ** 2
c1 = volatility * math.sqrt(dt)
# Configure the kernel
# Similar to CUDA-C: cu_monte_carlo_pricer<<<gridsz, blksz, 0, stream>>>
steplist = [cu_step[gridsz, blksz, strm]
for gridsz, strm in zip(gridszlist, strmlist)]
d_lastlist = [cuda.to_device(paths[s:e, 0], to=mm.get(stream=strm))
for (s, e), strm in zip(partitions, strmlist)]
for j in range(1, paths.shape[1]):
for prng, d_norm in zip(prnglist, d_normlist):
prng.normal(d_norm, mean=0, sigma=1)
d_pathslist = [cuda.to_device(paths[s:e, j], stream=strm,
to=mm.get(stream=strm))
for (s, e), strm in zip(partitions, strmlist)]
for step, args in zip(steplist, zip(d_lastlist, d_pathslist, d_normlist)):
d_last, d_paths, d_norm = args
step(d_last, d_paths, dt, c0, c1, d_norm)
for d_paths, strm, (s, e) in zip(d_pathslist, strmlist, partitions):
d_paths.copy_to_host(paths[s:e, j], stream=strm)
mm.free(d_last, stream=strm)
d_lastlist = d_pathslist
for strm in strmlist:
strm.synchronize()
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK
gridsz = int(math.ceil(float(n) / blksz))
# Instantiate cuRAND PRNG
prng = PRNG(PRNG.MRG32K3A)
# Allocate device side array
d_normdist = cuda.device_array(n, dtype=np.double)
c0 = interest - 0.5 * volatility ** 2
c1 = volatility * math.sqrt(dt)
# Simulation loop
d_last = cuda.to_device(paths[:, 0])
for j in range(1, paths.shape[1]):
prng.normal(d_normdist, mean=0, sigma=1)
d_paths = cuda.to_device(paths[:, j])
step(d_last, dt, c0, c1, d_normdist, out=d_paths)
d_paths.copy_to_host(paths[:, j])
d_last = d_paths
def monte_carlo_pricer(paths, dt, interest, volatility):
n = paths.shape[0]
blksz = cuda.get_current_device().MAX_THREADS_PER_BLOCK
gridsz = int(math.ceil(float(n) / blksz))
# Instantiate cuRAND PRNG
prng = PRNG(PRNG.MRG32K3A)
# Allocate device side array
d_normdist = cuda.device_array(n, dtype=np.double)
c0 = interest - 0.5 * volatility**2
c1 = volatility * math.sqrt(dt)
# Simulation loop
d_last = cuda.to_device(paths[:, 0])
for j in range(1, paths.shape[1]):
prng.normal(d_normdist, mean=0, sigma=1)
d_paths = cuda.to_device(paths[:, j])
step(d_last, dt, c0, c1, d_normdist, out=d_paths)
d_paths.copy_to_host(paths[:, j])
d_last = d_paths
class Ionizer(object):
"""
Class that contains the data associated with ionization (on the ions side)
and has method to calculate the ionization probability.
The implemented ionization model is the ADK model. The implementation
is fully relativistic (i.e. it works in the boosted-frame as well).
Main attributes
---------------
- ionization_level: 1darray of integers (one element per particle)
which contains the ionization state of each particle
- neutral_weight: 1darray of floats (one element per particle)
which contains the number of physical particle that correspond to each
macroparticle (not multiplied by the charge, unlike `w`)
"""
def __init__(self,
element,
ionizable_species,
target_species,
level_start,
full_initialization=True):
"""
Initialize an Ionizer instance
Parameters
----------
element: string
The atomic symbol of the considered ionizable species
(e.g. 'He', 'N' ; do not use 'Helium' or 'Nitrogen')
ionizable_species: an fbpic.Particles object
This object is not modified or registered.
It is only used in order to pass a number of additional argument.
target_species: an fbpic.Particles object
This object is not modified when creating the class, but
it is modified when ionization occurs
(i.e. more particles are created)
level_start: int
The ionization level at which the macroparticles are initially
(e.g. 0 for initially neutral atoms)
full_initialization: bool
If True: initialize the parameters needed for the calculation
of the ADK ionization rate. This is not needed when adding
new particles to the same species (e.g. with the moving window).
"""
# Register a few parameters
self.target_species = target_species
self.level_start = level_start
self.use_cuda = ionizable_species.use_cuda
# Process ionized particles into batches
self.batch_size = 10
# Initialize ionization-relevant meta-data
if full_initialization:
self.initialize_ADK_parameters(element, ionizable_species.dt)
# Initialize the required arrays
Ntot = ionizable_species.Ntot
self.ionization_level = np.ones(Ntot, dtype=np.uint64) * level_start
self.neutral_weight = ionizable_species.w / ionizable_species.q
def initialize_ADK_parameters(self, element, dt):
"""
Initialize parameters needed for the calculation of ADK ionization rate
Parameters
----------
element: string
The atomic symbol of the considered ionizable species
(e.g. 'He', 'N' ; do not use 'Helium' or 'Nitrogen')
dt: float (in seconds)
The timestep of the simulation. (The calculated ionization
probability is a probability *per timestep*.)
See Chen, JCP 236 (2013), equation (2) for the ionization rate formula
"""
# Get the array of energies
Uion = get_ionization_energies(element)
# Check whether the element string was valid
if Uion is None:
raise ValueError("Unknown ionizable element %s.\n" %element + \
"Please use atomic symbol (e.g. 'He') not full name (e.g. Helium)")
else:
self.element = element
# Determine the maximum level of ionization
self.level_max = len(Uion)
# Calculate the ADK prefactors (See Chen, JCP 236 (2013), equation (2))
# - Scalars
alpha = physical_constants['fine-structure constant'][0]
r_e = physical_constants['classical electron radius'][0]
wa = alpha**3 * c / r_e
Ea = m_e * c**2 / e * alpha**4 / r_e
# - Arrays (one element per ionization level)
UH = get_ionization_energies('H')[0]
Z = np.arange(len(Uion)) + 1
n_eff = Z * np.sqrt(UH / Uion)
l_eff = n_eff[0] - 1
C2 = 2**(2 * n_eff) / (n_eff * gamma(n_eff + l_eff + 1) *
gamma(n_eff - l_eff))
# For now, we assume l=0, m=0
self.adk_power = -(2 * n_eff - 1)
self.adk_prefactor = dt * wa * C2 * ( Uion/(2*UH) ) \
* ( 2*(Uion/UH)**(3./2)*Ea )**(2*n_eff - 1)
self.adk_exp_prefactor = -2. / 3 * (Uion / UH)**(3. / 2) * Ea
# Prepare random number generator
if self.use_cuda:
self.prng = PRNG()
def handle_ionization_gpu(self, ion):
"""
Handle ionization on the GPU:
- For each ion macroparticle, decide whether it is going to
be further ionized during this timestep, based on the ADK rate.
- Add the electrons created from ionization to the `target_species`
Parameters:
-----------
ion: an fbpic.Particles object
The ionizable species, from which new electrons are created.
"""
# Process particles in batches (of typically 10, 20 particles)
N_batch = int(ion.Ntot / self.batch_size) + 1
# Create temporary arrays
is_ionized = cuda.device_array((ion.Ntot, ), dtype=np.int16)
n_ionized = cuda.device_array((N_batch, ), dtype=np.int64)
# Draw random numbers
random_draw = cuda.device_array((ion.Ntot, ), dtype=np.float32)
self.prng.uniform(random_draw)
# Ionize the ions (one thread per batch)
batch_grid_1d, batch_block_1d = cuda_tpb_bpg_1d(N_batch)
ionize_ions_cuda[batch_grid_1d, batch_block_1d](
N_batch, self.batch_size, ion.Ntot, self.level_max, n_ionized,
is_ionized, self.ionization_level, random_draw, self.adk_prefactor,
self.adk_power, self.adk_exp_prefactor, ion.ux, ion.uy, ion.uz,
ion.Ex, ion.Ey, ion.Ez, ion.Bx, ion.By, ion.Bz, ion.w,
self.neutral_weight)
# Count the total number of electrons (operation performed
# on the CPU, as this is typically difficult on the GPU)
n_ionized = n_ionized.copy_to_host()
cumulative_n_ionized = np.zeros(len(n_ionized) + 1, dtype=np.int64)
np.cumsum(n_ionized, out=cumulative_n_ionized[1:])
# If no new particle was created, skip the rest of this function
if cumulative_n_ionized[-1] == 0:
return
# Reallocate the electron species, in order to
# accomodate the electrons produced by ionization
elec = self.target_species
old_Ntot = elec.Ntot
new_Ntot = old_Ntot + cumulative_n_ionized[-1]
# Iterate over particle attributes and copy the old electrons
# (one thread per particle)
ptcl_grid_1d, ptcl_block_1d = cuda_tpb_bpg_1d(old_Ntot)
for attr in [
'x', 'y', 'z', 'ux', 'uy', 'uz', 'w', 'inv_gamma', 'Ex', 'Ey',
'Ez', 'Bx', 'By', 'Bz'
]:
old_array = getattr(elec, attr)
new_array = cuda.device_array((new_Ntot, ), dtype=np.float64)
copy_particle_data_cuda[ptcl_grid_1d,
ptcl_block_1d](old_Ntot, old_array,
new_array)
setattr(elec, attr, new_array)
if elec.tracker is not None:
old_array = elec.tracker.id
new_array = cuda.device_array((new_Ntot, ), dtype=np.uint64)
copy_particle_data_cuda[ptcl_grid_1d,
ptcl_block_1d](old_Ntot, old_array,
new_array)
elec.tracker.id = new_array
# Allocate the auxiliary arrays
elec.cell_idx = cuda.device_array((new_Ntot, ), dtype=np.int32)
elec.sorted_idx = cuda.device_array((new_Ntot, ), dtype=np.uint32)
elec.sorting_buffer = cuda.device_array((new_Ntot, ), dtype=np.float64)
if elec.n_integer_quantities > 0:
elec.int_sorting_buffer = \
cuda.device_array( (new_Ntot,), dtype=np.uint64 )
# Modify the total number of electrons
elec.Ntot = new_Ntot
# Send `cumulative_n_ionized` back to the GPU
cumulative_n_ionized = cuda.to_device(cumulative_n_ionized)
# Copy the new electrons from ionization (one thread per batch)
copy_ionized_electrons_cuda[batch_grid_1d, batch_block_1d](
N_batch, self.batch_size, old_Ntot, ion.Ntot, cumulative_n_ionized,
is_ionized, elec.x, elec.y, elec.z, elec.inv_gamma, elec.ux,
elec.uy, elec.uz, elec.w, elec.Ex, elec.Ey, elec.Ez, elec.Bx,
elec.By, elec.Bz, ion.x, ion.y, ion.z, ion.inv_gamma, ion.ux,
ion.uy, ion.uz, self.neutral_weight, ion.Ex, ion.Ey, ion.Ez,
ion.Bx, ion.By, ion.Bz)
elec.sorted = False
# If the electrons are tracked, generate new ids
if elec.tracker is not None:
elec.tracker.generate_new_ids_gpu(old_Ntot, new_Ntot)
def handle_ionization_cpu(self, ion):
"""
Handle ionization on the CPU:
- For each ion macroparticle, decide whether it is going to
be further ionized during this timestep, based on the ADK rate.
- Add the electrons created from ionization to the `target_species`
Parameters:
-----------
ion: an fbpic.Particles object
The ionizable species, from which new electrons are created.
"""
# Process particles in batches (of typically 10, 20 particles)
N_batch = int(ion.Ntot / self.batch_size) + 1
# Create temporary arrays
is_ionized = np.empty(ion.Ntot, dtype=np.int16)
n_ionized = np.empty(N_batch, dtype=np.int64)
# Draw random numbers
random_draw = np.random.rand(ion.Ntot)
# Ionize the ions (one thread per batch)
ionize_ions_numba(N_batch, self.batch_size, ion.Ntot, self.level_max,
n_ionized, is_ionized, self.ionization_level,
random_draw, self.adk_prefactor, self.adk_power,
self.adk_exp_prefactor, ion.ux, ion.uy, ion.uz,
ion.Ex, ion.Ey, ion.Ez, ion.Bx, ion.By, ion.Bz,
ion.w, self.neutral_weight)
# Count the total number of electrons
cumulative_n_ionized = np.zeros(len(n_ionized) + 1, dtype=np.int64)
np.cumsum(n_ionized, out=cumulative_n_ionized[1:])
# If no new particle was created, skip the rest of this function
if cumulative_n_ionized[-1] == 0:
return
# Reallocate the electron species, in order to
# accomodate the electrons produced by ionization
elec = self.target_species
old_Ntot = elec.Ntot
new_Ntot = old_Ntot + cumulative_n_ionized[-1]
# Iterate over particle attributes and copy the old electrons
# (one thread per particle)
for attr in [
'x', 'y', 'z', 'ux', 'uy', 'uz', 'w', 'inv_gamma', 'Ex', 'Ey',
'Ez', 'Bx', 'By', 'Bz'
]:
old_array = getattr(elec, attr)
new_array = np.empty(new_Ntot, dtype=np.float64)
new_array[:old_Ntot] = old_array
setattr(elec, attr, new_array)
if elec.tracker is not None:
old_array = elec.tracker.id
new_array = np.empty(new_Ntot, dtype=np.uint64)
new_array[:old_Ntot] = old_array
elec.tracker.id = new_array
# Modify the total number of electrons
elec.Ntot = new_Ntot
# Copy the new electrons from ionization (one thread per batch)
copy_ionized_electrons_numba(
N_batch, self.batch_size, old_Ntot, ion.Ntot, cumulative_n_ionized,
is_ionized, elec.x, elec.y, elec.z, elec.inv_gamma, elec.ux,
elec.uy, elec.uz, elec.w, elec.Ex, elec.Ey, elec.Ez, elec.Bx,
elec.By, elec.Bz, ion.x, ion.y, ion.z, ion.inv_gamma, ion.ux,
ion.uy, ion.uz, self.neutral_weight, ion.Ex, ion.Ey, ion.Ez,
ion.Bx, ion.By, ion.Bz)
# If the electrons are tracked, generate new ids
if elec.tracker is not None:
elec.tracker.id[old_Ntot:new_Ntot] = \
elec.tracker.generate_new_ids( new_Ntot - old_Ntot )
def send_to_gpu(self):
"""
Copy the ionization data to the GPU.
"""
if self.use_cuda:
# Arrays with one element per macroparticles
self.ionization_level = cuda.to_device(self.ionization_level)
self.neutral_weight = cuda.to_device(self.neutral_weight)
# Small-size arrays with ADK parameters
# (One element per ionization level)
self.adk_power = cuda.to_device(self.adk_power)
self.adk_prefactor = cuda.to_device(self.adk_prefactor)
self.adk_exp_prefactor = cuda.to_device(self.adk_exp_prefactor)
def receive_from_gpu(self):
"""
Receive the ionization data from the GPU.
"""
if self.use_cuda:
# Arrays with one element per macroparticles
self.ionization_level = self.ionization_level.copy_to_host()
self.neutral_weight = self.neutral_weight.copy_to_host()
# Small-size arrays with ADK parameters
# (One element per ionization level)
self.adk_power = self.adk_power.copy_to_host()
self.adk_prefactor = self.adk_prefactor.copy_to_host()
self.adk_exp_prefactor = self.adk_exp_prefactor.copy_to_host()
