def main():
logmgr = LogManager("mylog.dat", "w") # , comm=...
# set a run property
logmgr.set_constant("myconst", uniform(0, 1))
add_run_info(logmgr)
add_general_quantities(logmgr)
add_simulation_quantities(logmgr)
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
logmgr.add_quantity(Fifteen("fifteen"))
logmgr.add_watches(["step.max", "t_sim.max", "t_step.max"])
for istep in range(200):
logmgr.tick_before()
dt = uniform(0.01, 0.1)
set_dt(logmgr, dt)
sleep(dt)
# Illustrate custom timers
if istep % 10 == 0:
with vis_timer.start_sub_timer():
sleep(0.05)
# Illustrate warnings capture
if uniform(0, 1) < 0.05:
warn("Oof. Something went awry.")
logmgr.tick_after()
logmgr.close()
def my_post_step(step, t, dt, state):
# Logmgr needs to know about EOS, dt, dim?
# imo this is a design/scope flaw
if logmgr:
set_dt(logmgr, dt)
set_sim_state(logmgr, dim, state, eos)
logmgr.tick_after()
return state, dt
def my_post_step(step, t, dt, state):
cv, tseed = state
fluid_state = construct_fluid_state(cv, tseed)
# Logmgr needs to know about EOS, dt, dim?
# imo this is a design/scope flaw
if logmgr:
set_dt(logmgr, dt)
set_sim_state(logmgr, dim, cv, gas_model.eos)
logmgr.tick_after()
return make_obj_array([cv, fluid_state.temperature]), dt
def main(ctx_factory=cl.create_some_context, use_logmgr=True,
use_leap=False, use_profiling=False, casename=None,
rst_filename=None, actx_class=PyOpenCLArrayContext):
"""Run the example."""
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
from mpi4py import MPI
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
logmgr = initialize_logmgr(use_logmgr,
filename="heat-source.sqlite", mode="wu", mpi_comm=comm)
if use_profiling:
queue = cl.CommandQueue(
cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
else:
queue = cl.CommandQueue(cl_ctx)
actx = actx_class(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
t = 0
t_final = 0.0002
istep = 0
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(-0.5,)*dim,
b=(0.5,)*dim,
nelements_per_axis=(nel_1d,)*dim,
boundary_tag_to_face={
"dirichlet": ["+x", "-x"],
"neumann": ["+y", "-y"]
}
)
print("%d elements" % mesh.nelements)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element, num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
order = 3
discr = EagerDGDiscretization(actx, local_mesh, order=order,
mpi_communicator=comm)
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.0025/(nel_1d*order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
source_width = 0.2
from arraycontext import thaw
nodes = thaw(discr.nodes(), actx)
boundaries = {
DTAG_BOUNDARY("dirichlet"): DirichletDiffusionBoundary(0.),
DTAG_BOUNDARY("neumann"): NeumannDiffusionBoundary(0.)
}
u = discr.zeros(actx)
if logmgr:
logmgr_add_device_name(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
logmgr.add_watches(["step.max", "t_step.max", "t_log.max"])
try:
logmgr.add_watches(["memory_usage_python.max", "memory_usage_gpu.max"])
except KeyError:
pass
if use_profiling:
logmgr.add_watches(["multiply_time.max"])
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
vis = make_visualizer(discr)
def rhs(t, u):
return (
diffusion_operator(
discr, quad_tag=DISCR_TAG_BASE,
alpha=1, boundaries=boundaries, u=u)
+ actx.np.exp(-np.dot(nodes, nodes)/source_width**2))
compiled_rhs = actx.compile(rhs)
rank = comm.Get_rank()
while t < t_final:
if logmgr:
logmgr.tick_before()
if istep % 10 == 0:
print(istep, t, actx.to_numpy(actx.np.linalg.norm(u[0])))
vis.write_vtk_file("fld-heat-source-mpi-%03d-%04d.vtu" % (rank, istep),
[
("u", u)
], overwrite=True)
u = rk4_step(u, t, dt, compiled_rhs)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
logmgr.tick_after()
final_answer = discr.norm(u, np.inf)
resid = abs(final_answer - 0.00020620711665201585)
if resid > 1e-15:
raise ValueError(f"Run did not produce the expected result {resid=}")
def _advance_state_stepper_func(rhs,
timestepper,
state,
t_final,
dt=0,
t=0.0,
istep=0,
pre_step_callback=None,
post_step_callback=None,
logmgr=None,
eos=None,
dim=None):
"""Advance state from some time (t) to some time (t_final).
Parameters
----------
rhs
Function that should return the time derivative of the state.
This function should take time and state as arguments, with
a call with signature ``rhs(t, state)``.
timestepper
Function that advances the state from t=time to t=(time+dt), and
returns the advanced state. Has a call with signature
``timestepper(state, t, dt, rhs)``.
state: numpy.ndarray
Agglomerated object array containing at least the state variables that
will be advanced by this stepper
t_final: float
Simulated time at which to stop
t: float
Time at which to start
dt: float
Initial timestep size to use, optional if dt is adaptive
istep: int
Step number from which to start
pre_step_callback
An optional user-defined function, with signature:
``state, dt = pre_step_callback(step, t, dt, state)``,
to be called before the timestepper is called for that particular step.
post_step_callback
An optional user-defined function, with signature:
``state, dt = post_step_callback(step, t, dt, state)``,
to be called after the timestepper is called for that particular step.
Returns
-------
istep: int
the current step number
t: float
the current time
state: numpy.ndarray
"""
t = np.float64(t)
if t_final <= t:
return istep, t, state
actx = get_container_context_recursively(state)
compiled_rhs = _compile_rhs(actx, rhs)
while t < t_final:
state = _force_evaluation(actx, state)
if logmgr:
logmgr.tick_before()
if pre_step_callback is not None:
state, dt = pre_step_callback(state=state, step=istep, t=t, dt=dt)
state = timestepper(state=state, t=t, dt=dt, rhs=compiled_rhs)
t += dt
istep += 1
if post_step_callback is not None:
state, dt = post_step_callback(state=state, step=istep, t=t, dt=dt)
if logmgr:
set_dt(logmgr, dt)
set_sim_state(logmgr, dim, state, eos)
logmgr.tick_after()
return istep, t, state
def _advance_state_stepper_func(rhs,
timestepper,
checkpoint,
get_timestep,
state,
t_final,
t=0.0,
istep=0,
logmgr=None,
eos=None,
dim=None):
"""Advance state from some time (t) to some time (t_final).
Parameters
----------
rhs
Function that should return the time derivative of the state.
This function should take time and state as arguments, with
a call with signature ``rhs(t, state)``.
timestepper
Function that advances the state from t=time to t=(time+dt), and
returns the advanced state. Has a call with signature
``timestepper(state, t, dt, rhs)``.
checkpoint
Function is user-defined and can be used to preform simulation status
reporting, viz, and restart i/o. A non-zero return code from this function
indicates that this function should stop gracefully.
get_timestep
Function that should return dt for the next step. This interface allows
user-defined adaptive timestepping. A negative return value indicated that
the stepper should stop gracefully. Takes only state as an argument.
state: numpy.ndarray
Agglomerated object array containing at least the state variables that
will be advanced by this stepper
t_final: float
Simulated time at which to stop
t: float
Time at which to start
istep: int
Step number from which to start
Returns
-------
istep: int
the current step number
t: float
the current time
state: numpy.ndarray
"""
if t_final <= t:
return istep, t, state
while t < t_final:
if logmgr:
logmgr.tick_before()
dt = get_timestep(state=state)
if dt < 0:
return istep, t, state
if checkpoint:
checkpoint(state=state, step=istep, t=t, dt=dt)
state = timestepper(state=state, t=t, dt=dt, rhs=rhs)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
set_sim_state(logmgr, dim, state, eos)
logmgr.tick_after()
return istep, t, state
def _advance_state_leap(rhs,
timestepper,
checkpoint,
get_timestep,
state,
t_final,
component_id="state",
t=0.0,
istep=0,
logmgr=None,
eos=None,
dim=None):
"""Advance state from some time *t* to some time *t_final* using :mod:`leap`.
Parameters
----------
rhs
Function that should return the time derivative of the state.
This function should take time and state as arguments, with
a call with signature ``rhs(t, state)``.
timestepper
An instance of :class:`leap.MethodBuilder`.
checkpoint
Function is user-defined and can be used to preform simulation status
reporting, viz, and restart i/o. A non-zero return code from this function
indicates that this function should stop gracefully.
get_timestep
Function that should return dt for the next step. This interface allows
user-defined adaptive timestepping. A negative return value indicated that
the stepper should stop gracefully.
state: numpy.ndarray
Agglomerated object array containing at least the state variables that
will be advanced by this stepper
t_final: float
Simulated time at which to stop
component_id
State id (required input for leap method generation)
t: float
Time at which to start
istep: int
Step number from which to start
Returns
-------
istep: int
the current step number
t: float
the current time
state: numpy.ndarray
"""
if t_final <= t:
return istep, t, state
# Generate code for Leap method.
dt = get_timestep(state=state)
stepper_cls = generate_singlerate_leap_advancer(timestepper, component_id,
rhs, t, dt, state)
while t < t_final:
if logmgr:
logmgr.tick_before()
dt = get_timestep(state=state)
if dt < 0:
return istep, t, state
checkpoint(state=state, step=istep, t=t, dt=dt)
# Leap interface here is *a bit* different.
for event in stepper_cls.run(t_end=t + dt):
if isinstance(event, stepper_cls.StateComputed):
state = event.state_component
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
set_sim_state(logmgr, dim, state, eos)
logmgr.tick_after()
return istep, t, state
def main(use_profiling=False, use_logmgr=False, lazy_eval: bool = False):
"""Drive the example."""
cl_ctx = cl.create_some_context()
logmgr = initialize_logmgr(use_logmgr, filename="wave.sqlite", mode="wu")
if use_profiling:
if lazy_eval:
raise RuntimeError("Cannot run lazy with profiling.")
queue = cl.CommandQueue(
cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
actx = PyOpenCLProfilingArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
else:
queue = cl.CommandQueue(cl_ctx)
if lazy_eval:
actx = PytatoPyOpenCLArrayContext(queue)
else:
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
dim = 2
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 3
discr = EagerDGDiscretization(actx, mesh, order=order)
current_cfl = 0.485
wave_speed = 1.0
from grudge.dt_utils import characteristic_lengthscales
nodal_dt = characteristic_lengthscales(actx, discr) / wave_speed
from grudge.op import nodal_min
dt = actx.to_numpy(current_cfl * nodal_min(discr, "vol", nodal_dt))[()]
print("%d elements" % mesh.nelements)
fields = flat_obj_array(bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)])
if logmgr:
logmgr_add_cl_device_info(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
logmgr.add_watches(["step.max", "t_step.max", "t_log.max"])
try:
logmgr.add_watches(
["memory_usage_python.max", "memory_usage_gpu.max"])
except KeyError:
pass
if use_profiling:
logmgr.add_watches(["multiply_time.max"])
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
vis = make_visualizer(discr)
def rhs(t, w):
return wave_operator(discr, c=wave_speed, w=w)
compiled_rhs = actx.compile(rhs)
t = 0
t_final = 1
istep = 0
while t < t_final:
if logmgr:
logmgr.tick_before()
fields = thaw(freeze(fields, actx), actx)
fields = rk4_step(fields, t, dt, compiled_rhs)
if istep % 10 == 0:
if use_profiling:
print(actx.tabulate_profiling_data())
print(istep, t, actx.to_numpy(discr.norm(fields[0], np.inf)))
vis.write_vtk_file("fld-wave-%04d.vtu" % istep, [
("u", fields[0]),
("v", fields[1:]),
],
overwrite=True)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
logmgr.tick_after()
def main(snapshot_pattern="wave-mpi-{step:04d}-{rank:04d}.pkl", restart_step=None,
use_profiling=False, use_logmgr=False, actx_class=PyOpenCLArrayContext):
"""Drive the example."""
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
num_parts = comm.Get_size()
logmgr = initialize_logmgr(use_logmgr,
filename="wave-mpi.sqlite", mode="wu", mpi_comm=comm)
if use_profiling:
queue = cl.CommandQueue(cl_ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
actx = actx_class(queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
logmgr=logmgr)
else:
queue = cl.CommandQueue(cl_ctx)
actx = actx_class(queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
if restart_step is None:
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(-0.5,)*dim, b=(0.5,)*dim,
nelements_per_axis=(nel_1d,)*dim)
print("%d elements" % mesh.nelements)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element, num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
fields = None
else:
from mirgecom.restart import read_restart_data
restart_data = read_restart_data(
actx, snapshot_pattern.format(step=restart_step, rank=rank)
)
local_mesh = restart_data["local_mesh"]
nel_1d = restart_data["nel_1d"]
assert comm.Get_size() == restart_data["num_parts"]
order = 3
discr = EagerDGDiscretization(actx, local_mesh, order=order,
mpi_communicator=comm)
current_cfl = 0.485
wave_speed = 1.0
from grudge.dt_utils import characteristic_lengthscales
dt = current_cfl * characteristic_lengthscales(actx, discr) / wave_speed
from grudge.op import nodal_min
dt = nodal_min(discr, "vol", dt)
t_final = 1
if restart_step is None:
t = 0
istep = 0
fields = flat_obj_array(
bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)]
)
else:
t = restart_data["t"]
istep = restart_step
assert istep == restart_step
restart_fields = restart_data["fields"]
old_order = restart_data["order"]
if old_order != order:
old_discr = EagerDGDiscretization(actx, local_mesh, order=old_order,
mpi_communicator=comm)
from meshmode.discretization.connection import make_same_mesh_connection
connection = make_same_mesh_connection(actx, discr.discr_from_dd("vol"),
old_discr.discr_from_dd("vol"))
fields = connection(restart_fields)
else:
fields = restart_fields
if logmgr:
logmgr_add_cl_device_info(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
logmgr.add_watches(["step.max", "t_step.max", "t_log.max"])
try:
logmgr.add_watches(["memory_usage_python.max", "memory_usage_gpu.max"])
except KeyError:
pass
if use_profiling:
logmgr.add_watches(["multiply_time.max"])
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
vis = make_visualizer(discr)
def rhs(t, w):
return wave_operator(discr, c=wave_speed, w=w)
compiled_rhs = actx.compile(rhs)
while t < t_final:
if logmgr:
logmgr.tick_before()
# restart must happen at beginning of step
if istep % 100 == 0 and (
# Do not overwrite the restart file that we just read.
istep != restart_step):
from mirgecom.restart import write_restart_file
write_restart_file(
actx, restart_data={
"local_mesh": local_mesh,
"order": order,
"fields": fields,
"t": t,
"step": istep,
"nel_1d": nel_1d,
"num_parts": num_parts},
filename=snapshot_pattern.format(step=istep, rank=rank),
comm=comm
)
if istep % 10 == 0:
print(istep, t, discr.norm(fields[0]))
vis.write_parallel_vtk_file(
comm,
"fld-wave-mpi-%03d-%04d.vtu" % (rank, istep),
[
("u", fields[0]),
("v", fields[1:]),
], overwrite=True
)
fields = thaw(freeze(fields, actx), actx)
fields = rk4_step(fields, t, dt, compiled_rhs)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
logmgr.tick_after()
final_soln = discr.norm(fields[0])
assert np.abs(final_soln - 0.04409852463947439) < 1e-14
