def get_compute_kernels(self, runner, full_output, bulk):
signature, args1, args2 = self._compute_kernels_arguments(runner, full_output, bulk)
cnp_primary = runner.get_kernel(
'CollideAndPropagate', args1, signature,
needs_iteration=self.config.needs_iteration_num)
if self.config.access_pattern == 'AB':
secondary_args = args2
cnp_secondary = runner.get_kernel(
'CollideAndPropagate', secondary_args, signature,
needs_iteration=self.config.needs_iteration_num)
return KernelPair([cnp_primary], [cnp_secondary])
else:
return KernelPair([cnp_primary], [cnp_primary])
def get_compute_kernels(self, runner, full_output, bulk):
gpu_rho = runner.gpu_field(self.rho)
gpu_v = runner.gpu_field(self.v)
gpu_dist1a = runner.gpu_dist(0, 0)
gpu_dist1b = runner.gpu_dist(0, 1)
gpu_map = runner.gpu_geo_map()
args1 = [gpu_map, gpu_dist1a, gpu_dist1b, gpu_rho] + gpu_v
args2 = [gpu_map, gpu_dist1b, gpu_dist1a, gpu_rho] + gpu_v
options = 0
if full_output:
options |= 1
if bulk:
options |= 2
args1.append(np.uint32(options))
args2.append(np.uint32(options))
signature = 'P' * (len(args1) - 1) + 'i'
if runner.gpu_scratch_space is not None:
args1.append(runner.gpu_scratch_space)
args2.append(runner.gpu_scratch_space)
signature += 'P'
# Alpha field for the entropic LBM.
if self.alpha_output:
args1.append(runner.gpu_field(self.alpha))
args2.append(runner.gpu_field(self.alpha))
signature += 'P'
cnp_primary = runner.get_kernel(
'CollideAndPropagate',
args1,
signature,
needs_iteration=self.config.needs_iteration_num)
if self.config.access_pattern == 'AB':
secondary_args = args2 if self.config.access_pattern == 'AB' else args1
cnp_secondary = runner.get_kernel(
'CollideAndPropagate',
secondary_args,
signature,
needs_iteration=self.config.needs_iteration_num)
return KernelPair([cnp_primary], [cnp_secondary])
else:
return KernelPair([cnp_primary], [cnp_primary])
def get_aux_kernels(self, runner):
gpu_dist1a = runner.gpu_dist(0, 0)
gpu_dist1b = runner.gpu_dist(0, 1)
gpu_map = runner.gpu_geo_map()
gpu_entropy = runner.gpu_field(self.entropy)
ce1 = runner.get_kernel('ComputeEntropy',
[gpu_map, gpu_dist1b, gpu_entropy],
'PPP')
ce2 = runner.get_kernel('ComputeEntropy',
[gpu_map, gpu_dist1a, gpu_entropy],
'PPP')
return KernelPair([ce1], [ce2])
def get_compute_kernels(self, runner, full_output, bulk):
gpu_rho = runner.gpu_field(self.rho)
gpu_phi = runner.gpu_field(self.phi)
gpu_v = runner.gpu_field(self.v)
gpu_map = runner.gpu_geo_map()
gpu_dist1a = runner.gpu_dist(0, 0)
gpu_dist1b = runner.gpu_dist(0, 1)
gpu_dist2a = runner.gpu_dist(1, 0)
gpu_dist2b = runner.gpu_dist(1, 1)
options = 0
if full_output:
options |= 1
if bulk:
options |= 2
options = np.uint32(options)
# Primary.
args1a = ([gpu_map, gpu_dist1a, gpu_dist1b, gpu_rho, gpu_phi] + gpu_v +
[options])
args1b = ([gpu_map, gpu_dist2a, gpu_dist2b, gpu_rho, gpu_phi] + gpu_v +
[options])
# Secondary.
args2a = ([gpu_map, gpu_dist1b, gpu_dist1a, gpu_rho, gpu_phi] + gpu_v +
[options])
args2b = ([gpu_map, gpu_dist2b, gpu_dist2a, gpu_rho, gpu_phi] + gpu_v +
[options])
macro_args1 = ([gpu_map, gpu_dist1a, gpu_dist2a, gpu_rho, gpu_phi] +
gpu_v + [options])
macro_args2 = ([gpu_map, gpu_dist1b, gpu_dist2b, gpu_rho, gpu_phi] +
gpu_v + [options])
if self.config.node_addressing == 'indirect':
gpu_nodes = runner.gpu_indirect_address()
args1a = [gpu_nodes] + args1a
args1b = [gpu_nodes] + args1b
args2a = [gpu_nodes] + args2a
args2b = [gpu_nodes] + args2b
macro_args1 = [gpu_nodes] + macro_args1
macro_args2 = [gpu_nodes] + macro_args2
args_a_signature = 'P' * (len(args1a) - 1) + 'i'
args_b_signature = 'P' * (len(args1b) - 1) + 'i'
macro_signature = 'P' * (len(macro_args1) - 1) + 'i'
if runner.gpu_scratch_space is not None:
macro_args1.append(runner.gpu_scratch_space)
macro_args2.append(runner.gpu_scratch_space)
macro_signature += 'P'
args1a.append(runner.gpu_scratch_space)
args2a.append(runner.gpu_scratch_space)
args1b.append(runner.gpu_scratch_space)
args2b.append(runner.gpu_scratch_space)
args_a_signature += 'P'
args_b_signature += 'P'
macro = runner.get_kernel(
'ShanChenPrepareMacroFields',
macro_args1,
macro_signature,
needs_iteration=self.config.needs_iteration_num)
if self.config.access_pattern == 'AB':
macro_secondary = runner.get_kernel(
'ShanChenPrepareMacroFields',
macro_args2,
macro_signature,
needs_iteration=self.config.needs_iteration_num)
macro_pair = KernelPair(macro, macro_secondary)
else:
macro_pair = KernelPair(macro, macro)
# TODO(michalj): These kernels can actually run in parallel.
primary = [
runner.get_kernel('ShanChenCollideAndPropagate0',
args1a,
args_a_signature,
needs_iteration=self.config.needs_iteration_num),
runner.get_kernel('ShanChenCollideAndPropagate1',
args1b,
args_b_signature,
needs_iteration=self.config.needs_iteration_num)
]
if self.config.access_pattern == 'AB':
secondary = [
runner.get_kernel(
'ShanChenCollideAndPropagate0',
args2a,
args_a_signature,
needs_iteration=self.config.needs_iteration_num),
runner.get_kernel(
'ShanChenCollideAndPropagate1',
args2b,
args_b_signature,
needs_iteration=self.config.needs_iteration_num)
]
sim_pair = KernelPair(primary, secondary)
else:
sim_pair = KernelPair(primary, primary)
return list(zip(macro_pair, sim_pair))
def get_compute_kernels(self, runner, full_output, bulk):
gpu_rho = runner.gpu_field(self.rho)
gpu_phi = runner.gpu_field(self.phi)
gpu_lap = runner.gpu_field(self.phi_laplacian)
gpu_v = runner.gpu_field(self.v)
gpu_map = runner.gpu_geo_map()
gpu_dist1a = runner.gpu_dist(0, 0)
gpu_dist1b = runner.gpu_dist(0, 1)
gpu_dist2a = runner.gpu_dist(1, 0)
gpu_dist2b = runner.gpu_dist(1, 1)
options = 0
if full_output:
options |= 1
if bulk:
options |= 2
if hasattr(
self,
'_force_term_for_eq') and self._force_term_for_eq.get(1) == 0:
phi_args = [gpu_rho, gpu_phi]
else:
phi_args = [gpu_phi]
options = np.uint32(options)
# Primary.
args1a = ([gpu_map, gpu_dist1a, gpu_dist1b, gpu_rho, gpu_phi] + gpu_v +
[gpu_lap, options])
args1b = ([gpu_map, gpu_dist2a, gpu_dist2b] + phi_args + gpu_v +
[gpu_lap, options])
# Secondary.
args2a = ([gpu_map, gpu_dist1b, gpu_dist1a, gpu_rho, gpu_phi] + gpu_v +
[gpu_lap, options])
args2b = ([gpu_map, gpu_dist2b, gpu_dist2a] + phi_args + gpu_v +
[gpu_lap, options])
macro_args1 = [
gpu_map, gpu_dist1a, gpu_dist2a, gpu_rho, gpu_phi, options
]
macro_args2 = [
gpu_map, gpu_dist1b, gpu_dist2b, gpu_rho, gpu_phi, options
]
if self.config.node_addressing == 'indirect':
gpu_nodes = runner.gpu_indirect_address()
args1a = [gpu_nodes] + args1a
args1b = [gpu_nodes] + args1b
args2a = [gpu_nodes] + args2a
args2b = [gpu_nodes] + args2b
macro_args1 = [gpu_nodes] + macro_args1
macro_args2 = [gpu_nodes] + macro_args2
args_a_signature = 'P' * (len(args1a) - 1) + 'i'
args_b_signature = 'P' * (len(args1b) - 1) + 'i'
macro_signature = 'P' * (len(macro_args1) - 1) + 'i'
if runner.gpu_scratch_space is not None:
macro_args1.append(runner.gpu_scratch_space)
macro_args2.append(runner.gpu_scratch_space)
macro_signature += 'P'
args1a.append(runner.gpu_scratch_space)
args2a.append(runner.gpu_scratch_space)
args1b.append(runner.gpu_scratch_space)
args2b.append(runner.gpu_scratch_space)
args_a_signature += 'P'
args_b_signature += 'P'
macro = runner.get_kernel(
'FreeEnergyPrepareMacroFields',
macro_args1,
macro_signature,
needs_iteration=self.config.needs_iteration_num)
if self.config.access_pattern == 'AB':
macro_secondary = runner.get_kernel(
'FreeEnergyPrepareMacroFields',
macro_args2,
macro_signature,
needs_iteration=self.config.needs_iteration_num)
macro_pair = KernelPair(macro, macro_secondary)
else:
macro_pair = KernelPair(macro, macro)
# Note: these two kernels need to be executed in order.
primary = [
runner.get_kernel('FreeEnergyCollideAndPropagateFluid',
args1a,
args_a_signature,
needs_iteration=self.config.needs_iteration_num),
runner.get_kernel('FreeEnergyCollideAndPropagateOrderParam',
args1b,
args_b_signature,
needs_iteration=self.config.needs_iteration_num)
]
if self.config.access_pattern == 'AB':
secondary = [
runner.get_kernel(
'FreeEnergyCollideAndPropagateFluid',
args2a,
args_a_signature,
needs_iteration=self.config.needs_iteration_num),
runner.get_kernel(
'FreeEnergyCollideAndPropagateOrderParam',
args2b,
args_b_signature,
needs_iteration=self.config.needs_iteration_num)
]
sim_pair = KernelPair(primary, secondary)
else:
sim_pair = KernelPair(primary, primary)
return list(zip(macro_pair, sim_pair))