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https://gitee.com/sunhf/gprMax.git
已同步 2025-08-08 07:24:19 +08:00
Add halo swap function to MPIGrid
Distribute Model to each rank
这个提交包含在：
nmannall
@@ -23,9 +23,11 @@ import sys
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from typing import Any, Dict, List, Optional
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import humanize
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import numpy as np
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from colorama import Fore, Style, init
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from gprMax.hash_cmds_file import parse_hash_commands
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from gprMax.mpi_model import MPIModel
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from gprMax.scene import Scene
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init()
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@@ -157,37 +159,71 @@ class Context:
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class MPIContext(Context):
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def __init__(self, x_dim: int, y_dim: int, z_dim: int):
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def __init__(self):
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super().__init__()
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from mpi4py import MPI
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self.comm = MPI.COMM_WORLD
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self.rank = self.comm.rank
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if self.rank >= x_dim * y_dim * z_dim:
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requested_mpi_size = np.product(config.sim_config.mpi)
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if self.comm.size < requested_mpi_size:
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logger.exception(
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(
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"MPI_COMM_WORLD size of {self.comm.size} is too small for requested dimensions of"
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f" {config.sim_config.mpi}. {requested_mpi_size} ranks are required."
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)
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)
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raise ValueError
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if self.rank >= requested_mpi_size:
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logger.warn(
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(
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f"Rank {self.rank}: Only {x_dim * y_dim * z_dim} MPI ranks required for the"
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f"Rank {self.rank}: Only {requested_mpi_size} MPI ranks required for the"
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" dimensions specified. Either increase your MPI dimension size, or request"
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" fewer MPI tasks."
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)
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)
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self.x = -1
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self.y = -1
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self.z = -1
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else:
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self.x = self.rank % x_dim
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self.y = (self.rank // x_dim) % y_dim
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self.z = (self.rank // (x_dim * y_dim)) % z_dim
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exit()
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def _create_model(self) -> MPIModel:
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return MPIModel()
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def run(self) -> Dict:
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print(f"I am rank {self.rank} and I will run at grid position {self.x}, {self.y}, {self.z}")
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return super().run()
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if self.rank == 0:
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print("Rank 0 is running the simulation")
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return super().run()
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else:
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return {}
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def _run_model(self, model_num: int) -> None:
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"""Process for running a single model.
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Args:
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model_num: index of model to be run
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"""
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config.sim_config.set_current_model(model_num)
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model_config = self._create_model_config(model_num)
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config.sim_config.set_model_config(model_config)
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if not model_config.reuse_geometry():
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model = self._create_model()
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if model.is_coordinator():
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scene = self._get_scene(model_num)
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scene.create_internal_objects(model)
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model.build()
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return
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if not config.sim_config.geometry_only:
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solver = create_solver(model)
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model.solve(solver)
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del solver
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if not config.sim_config.geometry_fixed:
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# Manual garbage collection required to stop memory leak on GPUs
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# when using pycuda
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del model.G, model
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gc.collect()
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class TaskfarmContext(Context):
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@@ -269,10 +269,7 @@ def run_main(args):
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context = TaskfarmContext()
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# MPI running to divide model between ranks
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elif config.sim_config.args.mpi is not None:
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x = config.sim_config.args.mpi[0]
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y = config.sim_config.args.mpi[1]
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z = config.sim_config.args.mpi[2]
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context = MPIContext(x, y, z)
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context = MPIContext()
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# Standard running (OpenMP/CUDA/OpenCL)
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else:
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context = Context()
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@@ -16,58 +16,188 @@
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# You should have received a copy of the GNU General Public License
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# along with gprMax. If not, see <http://www.gnu.org/licenses/>.
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from typing import Optional
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from enum import IntEnum, unique
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from typing import List
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import numpy as np
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import numpy.typing as npt
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from matplotlib.sankey import UP
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from mpi4py import MPI
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from numpy import ndarray
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from gprMax.grid.fdtd_grid import FDTDGrid
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@unique
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class Dim(IntEnum):
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X = 0
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Y = 1
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Z = 2
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@unique
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class Dir(IntEnum):
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NEG = 0
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POS = 1
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class MPIGrid(FDTDGrid):
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def __init__(
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self,
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mpi_tasks_x: int,
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mpi_tasks_y: int,
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mpi_tasks_z: int,
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comm: Optional[MPI.Intracomm] = None,
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):
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HALO_SIZE = 1
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def __init__(self, comm: MPI.Cartcomm):
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self.size = np.zeros(3, dtype=int)
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super().__init__()
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if comm is None:
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self.comm = MPI.COMM_WORLD
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self.comm = comm
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self.mpi_tasks = np.array(self.comm.dims)
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self.lower_extent: npt.NDArray[np.intc] = np.zeros(3, dtype=int)
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self.upper_extent: npt.NDArray[np.intc] = np.zeros(3, dtype=int)
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self.global_size: npt.NDArray[np.intc] = np.zeros(3, dtype=int)
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self.neighbours = np.full((3, 2), -1, dtype=int)
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self.neighbours[Dim.X] = self.comm.Shift(direction=Dim.X, disp=1)
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self.neighbours[Dim.Y] = self.comm.Shift(direction=Dim.Y, disp=1)
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self.neighbours[Dim.Z] = self.comm.Shift(direction=Dim.Z, disp=1)
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print(f"[Rank {self.rank}] Neighbours = {self.neighbours}")
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self.send_halo_map = np.empty((3, 2), dtype=MPI.Datatype)
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self.recv_halo_map = np.empty((3, 2), dtype=MPI.Datatype)
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@property
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def rank(self) -> int:
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return self.comm.Get_rank()
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@property
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def coords(self) -> List[int]:
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return self.comm.coords
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@property
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def nx(self) -> int:
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return self.size[Dim.X]
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@nx.setter
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def nx(self, value: int):
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self.size[Dim.X] = value
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@property
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def ny(self) -> int:
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return self.size[Dim.Y]
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@ny.setter
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def ny(self, value: int):
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self.size[Dim.Y] = value
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@property
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def nz(self) -> int:
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return self.size[Dim.Z]
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@nz.setter
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def nz(self, value: int):
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self.size[Dim.Z] = value
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def broadcast_grid(self):
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self.calculate_local_extents()
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print(
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f"[Rank {self.rank}] - Global size: {self.global_size}, Local size: {self.size}, Cart comm dims: {self.mpi_tasks}, Local coordinates: {self.coords}, Lower extent = {self.lower_extent}, Upper extent = {self.upper_extent}"
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)
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def _get_halo(self, array: ndarray, dim: Dim, direction: Dir) -> ndarray:
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if direction == Dir.NEG:
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index = 0
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else: # Direction.UP
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index = -1
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if dim == Dim.X:
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halo = array[:, index, index]
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elif dim == Dim.Y:
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halo = array[index, :, index]
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else: # Dim.Z
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halo = array[index, index, :]
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return halo
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def _set_halo(self, array: ndarray, halo: ndarray, dim: Dim, direction: Dir):
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if direction == Dir.NEG:
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index = 0
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else: # Direction.UP
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index = -1
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if dim == Dim.X:
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array[:, index, index] = halo
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elif dim == Dim.Y:
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array[index, :, index] = halo
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else: # Dim.Z
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array[index, index, :] = halo
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def _halo_swap(self, array: ndarray, dim: Dim, dir: Dir):
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neighbour = self.neighbours[dim][dir]
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if neighbour != -1:
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self.comm.Sendrecv(
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[array, self.send_halo_map[dim][dir]],
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neighbour,
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0,
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[array, self.recv_halo_map[dim][dir]],
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neighbour,
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0,
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None,
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)
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def _halo_swap_dimension(self, array: ndarray, dim: Dim):
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if self.coords[dim] % 2 == 0:
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self._halo_swap(array, dim, Dir.NEG)
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self._halo_swap(array, dim, Dir.POS)
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else:
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self.comm = comm
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self._halo_swap(array, dim, Dir.POS)
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self._halo_swap(array, dim, Dir.NEG)
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if mpi_tasks_x * mpi_tasks_y * mpi_tasks_z > self.comm.size:
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# TODO: Raise expection - insufficient MPI tasks to create the grid as requested
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pass
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def halo_swap(self, array: ndarray):
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self._halo_swap_dimension(array, Dim.X)
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self._halo_swap_dimension(array, Dim.Y)
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self._halo_swap_dimension(array, Dim.Z)
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self.mpi_tasks_x = mpi_tasks_x
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self.mpi_tasks_y = mpi_tasks_y
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self.mpi_tasks_z = mpi_tasks_z
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def build(self):
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self.calculate_local_extents()
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self.set_halo_map()
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super().build()
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self.halo_swap(self.Ex)
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self.rank = self.comm.rank
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self.size = self.comm.size
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def set_halo_map(self):
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size = self.size.tolist()
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self.xmin = 0
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self.ymin = 0
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self.zmin = 0
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self.xmax = 0
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self.ymax = 0
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self.zmax = 0
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for dim in Dim:
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halo_size = (self.size - 2).tolist()
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halo_size[dim] = 1
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start = [1, 1, 1]
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self.send_halo_map[dim][Dir.NEG] = MPI.DOUBLE.Create_subarray(size, halo_size, start)
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start[dim] = size[dim] - 2
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self.send_halo_map[dim][Dir.POS] = MPI.DOUBLE.Create_subarray(size, halo_size, start)
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start[dim] = 0
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self.recv_halo_map[dim][Dir.NEG] = MPI.DOUBLE.Create_subarray(size, halo_size, start)
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start[dim] = size[dim] - 1
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self.recv_halo_map[dim][Dir.POS] = MPI.DOUBLE.Create_subarray(size, halo_size, start)
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self.send_halo_map[dim][Dir.NEG].Commit()
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self.send_halo_map[dim][Dir.POS].Commit()
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self.recv_halo_map[dim][Dir.NEG].Commit()
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self.recv_halo_map[dim][Dir.POS].Commit()
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def calculate_local_extents(self):
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self.size = self.global_size // self.mpi_tasks
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self.lower_extent = self.size * self.coords
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at_end = (self.mpi_tasks - self.coords) <= 1
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self.size += at_end * self.global_size % self.mpi_tasks
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self.upper_extent = self.lower_extent + self.size
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# Account for halo
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self.size += 2
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def initialise_field_arrays(self):
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super().initialise_field_arrays()
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local_grid_size_x = self.nx // self.mpi_tasks_x
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local_grid_size_y = self.ny // self.mpi_tasks_y
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local_grid_size_z = self.nz // self.mpi_tasks_z
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self.xmin = (self.rank % self.nx) * local_grid_size_x
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self.ymin = ((self.mpi_tasks_x * self.rank) % self.ny) * local_grid_size_y
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self.zmin = (
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(self.mpi_tasks_y * self.mpi_tasks_x * self.rank) % self.nz
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) * local_grid_size_z
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self.xmax = self.xmin + local_grid_size_x
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self.ymax = self.ymin + local_grid_size_y
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self.zmax = self.zmin + local_grid_size_z
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67
gprMax/mpi_model.py
普通文件
67
gprMax/mpi_model.py
普通文件
@@ -0,0 +1,67 @@
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import logging
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from re import L
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from typing import Optional
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import numpy as np
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from mpi4py import MPI
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from gprMax import config
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from gprMax.grid.mpi_grid import MPIGrid
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from gprMax.model import Model
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logger = logging.getLogger(__name__)
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class MPIModel(Model):
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def __init__(self, comm: Optional[MPI.Intracomm] = None):
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if comm is None:
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self.comm = MPI.COMM_WORLD
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else:
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self.comm = comm
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self.rank = self.comm.Get_rank()
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self.G = self._create_grid()
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return super().__init__()
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def is_coordinator(self):
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return self.rank == 0
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def build(self):
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self.build_geometry()
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return
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return super().build()
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def build_geometry(self):
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if self.is_coordinator():
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self._check_for_dispersive_materials([self.G])
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self._check_memory_requirements([self.G])
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self._broadcast_model()
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self.G.global_size = np.array([self.gnx, self.gny, self.gnz], dtype=int)
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self.G.build()
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return
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self.G.dispersion_analysis(self.iterations)
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def _broadcast_model(self):
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self.gnx = self.comm.bcast(self.gnx)
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self.gny = self.comm.bcast(self.gny)
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self.gnz = self.comm.bcast(self.gnz)
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self.comm.Bcast(self.dl)
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self.dt = self.comm.bcast(self.dt)
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self.iterations = self.comm.bcast(self.iterations)
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self.srcsteps = self.comm.bcast(self.srcsteps)
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self.rxsteps = self.comm.bcast(self.rxsteps)
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def _output_geometry(self):
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if self.is_coordinator():
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logger.info("Geometry views and geometry objects are not currently supported with MPI.")
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def _create_grid(self) -> MPIGrid:
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cart_comm = MPI.COMM_WORLD.Create_cart(config.sim_config.mpi)
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return MPIGrid(cart_comm)
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