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FiniteVolumeGPU/MPITest.ipynb
Anthony Berg d99ec9420b feat(mpi): add parallel netCDF
2025-07-24 13:42:03 +02:00

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{
"cells": [
{
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"metadata": {},
"source": [
"# Starting cluster\n",
"\n",
"## Prerequisites\n",
"First, you need to install MPI, on windows use MS-MPI:\n",
"https://msdn.microsoft.com/en-us/library/bb524831(v=vs.85).aspx\n",
"\n",
"\n",
"## With a profile (not working)\n",
"In theory, you should be able to create a profile using\n",
"```\n",
"ipython profile create --parallel --profile=myprofile\n",
"```\n",
"and then set\n",
"```\n",
"c.IPClusterEngines.engine_launcher_class = 'MPIEngineSetLauncher'\n",
"```\n",
"in ```<IPYTHON-DIR>/profile_myprofile/ipcluster_config.py```. This should then enable you to start a cluster using\n",
"```\n",
"ipcluster start --profile=myprofile\n",
"```\n",
"or alternatively through the Clusters tab in Jupyter\n",
"\n",
"\n",
"## Without a profile (not working)\n",
"An alternative is to run\n",
"```\n",
"ipcluster start --engines=MPI\n",
"```\n",
"\n",
"\n",
"## Manual start (working)\n",
"This, however, does *not* work for me on Windows. What does work is the following:\n",
"\n",
"Start a controller using\n",
"```\n",
"ipcontroller --ip='*'\n",
"```\n",
"and then start several engines using mpiexec:\n",
"```\n",
"mpiexec -n 4 ipengine --mpi\n",
"```"
]
},
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"execution_count": 1,
"metadata": {},
"outputs": [],
"source": ""
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Console logger using level INFO\n",
"File logger using level DEBUG to mpi.log\n",
"Python version 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 14:00:49) [MSC v.1915 64 bit (AMD64)]\n"
]
}
],
"source": [
"%setup_logging --out mpi.log my_logger"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Starting IPController\n",
"Starting IPEngines\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Waiting for cluster...\n",
"Done\n"
]
}
],
"source": [
"%setup_mpi mpi_context --num_engines 3"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from mpi4py import MPI\n",
"import json\n",
"\n",
"import GPUSimulators.mpi as MPISimulator\n",
"from GPUSimulators.common import run_simulation, DataDumper"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"%setup_logging --out \"'mpi_' + str(MPI.COMM_WORLD.rank) + '.log'\" my_logger"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"%cuda_context_handler my_context"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"def plot_extent(extent, *args, **kwargs):\n",
" x0, x1, y0, y1 = extent\n",
" plt.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], *args, **kwargs)"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"import importlib\n",
"\n",
"importlib.reload(MPISimulator)\n",
"\n",
"grid = MPISimulator.MPIGrid(MPI.COMM_WORLD)\n",
"print(grid.get_local_rank())"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"from GPUSimulators.model import EE2DKP07Dimsplit\n",
"\n",
"my_context.autotuner = None\n",
"\n",
"nx = 128\n",
"ny = 128\n",
"gamma = 1.4\n",
"save_times = np.linspace(0, 5.0, 10)\n",
"outfile = \"mpi_out_\" + str(MPI.COMM_WORLD.rank) + \".nc\"\n",
"save_var_names = ['rho', 'rho_u', 'rho_v', 'E']\n",
"\n",
"grid = MPISimulator.MPIGrid(MPI.COMM_WORLD)\n",
"print(grid.get_local_rank())\n",
"\n",
"#arguments = InitialConditions.genShockBubble(nx, ny, gamma, grid=grid)\n",
"arguments = InitialConditions.gen_kelvin_helmholtz(nx, ny, gamma, grid=grid)\n",
"#arguments = InitialConditions.genRayleighTaylor(nx, ny, gamma, grid=grid)\n",
"\n",
"arguments['context'] = my_context\n",
"arguments['theta'] = 1.2\n",
"arguments['grid'] = grid\n",
"\n",
"\n",
"def gen_sim(grid, **kwargs):\n",
" local_sim = EE2DKP07Dimsplit(**kwargs)\n",
" sim = MPISimulator.MPISimulator(local_sim, grid)\n",
" return sim\n",
"\n",
"\n",
"outfile = run_simulation(gen_sim, arguments, outfile, save_times, save_var_names)"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"with DataDumper(outfile, 'r') as indata:\n",
" t = indata.nc.variables['time'][:]\n",
" x = indata.nc.variables['x'][:]\n",
" y = indata.nc.variables['y'][:]\n",
"\n",
" x_data = grid.gather(x)\n",
" y_data = grid.gather(y)\n",
"\n",
" created = indata.nc.created\n",
" sim_args = json.loads(indata.nc.sim_args)\n",
" for key in sim_args:\n",
" if isinstance(sim_args[key], list):\n",
" sim_args[key] = \"[...]\"\n",
" num_steps = len(t)\n",
" print(f\"{outfile} created {created} contains {num_steps} timesteps\")\n",
" print(\"Simulator arguments: \\n\", sim_args)\n",
"\n",
" for i in range(num_steps):\n",
" rho = indata.nc.variables['rho'][i]\n",
" rho_data = grid.gather(rho)\n",
"\n",
" #Plot on rank 0\n",
" if grid.comm.rank == 0:\n",
" plt.figure(figsize=(16, 12))\n",
" for k in range(grid.comm.size):\n",
" extent = [x_data[k].min(), x_data[k].max(), y_data[k].min(), y_data[k].max()]\n",
" plt.imshow(rho_data[k], extent=extent, origin='lower', vmin=1.0, vmax=2.0)\n",
" plot_extent(extent, 'k:', alpha=0.1)\n",
" plt.title(\"t=\" + str(t[i]))\n",
" plt.colorbar(shrink=0.33)"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"from GPUSimulators.model import HLL2\n",
"from GPUSimulators.Simulator import BoundaryCondition\n",
"\n",
"nx = 128\n",
"ny = 128\n",
"g = 9.81\n",
"dt = 0.05\n",
"width = 50\n",
"height = 100\n",
"save_times = np.linspace(0, 80, 10)\n",
"outfile = \"mpi_out_\" + str(MPI.COMM_WORLD.rank) + \".nc\"\n",
"save_var_names = ['h', 'hu', 'hv']\n",
"\n",
"if MPI.COMM_WORLD.rank == 0:\n",
" h0, hu0, hv0, dx, dy = InitialConditions.bump(nx, ny, width, height, g, x_center=0.75, y_center=0.55)\n",
"else:\n",
" h0, hu0, hv0, dx, dy = InitialConditions.bump(nx, ny, width, height, g, h_ref=0.5, h_amp=0.0, u_amp=0.0, v_amp=0.0)\n",
"\n",
"bc = BoundaryCondition({\n",
" 'north': BoundaryCondition.Type.Reflective,\n",
" 'south': BoundaryCondition.Type.Reflective,\n",
" 'east': BoundaryCondition.Type.Reflective,\n",
" 'west': BoundaryCondition.Type.Reflective\n",
"})\n",
"grid = MPISimulator.MPIGrid(MPI.COMM_WORLD)\n",
"\n",
"arguments = {\n",
" 'context': my_context,\n",
" 'h0': h0, 'hu0': hu0, 'hv0': hv0,\n",
" 'nx': nx, 'ny': ny,\n",
" 'dx': dx, 'dy': dy,\n",
" 'g': g,\n",
" 'boundary_conditions': bc,\n",
" 'grid': grid\n",
"}\n",
"\n",
"\n",
"def gen_sim(grid, **kwargs):\n",
" local_sim = HLL2(**kwargs)\n",
" sim = MPISimulator.MPISimulator(local_sim, grid)\n",
" return sim\n",
"\n",
"\n",
"outfile = run_simulation(gen_sim, arguments, outfile, save_times, save_var_names)"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"%%px\n",
"\n",
"with DataDumper(outfile, 'r') as indata:\n",
" t = indata.nc.variables['time'][:]\n",
" x = indata.nc.variables['x'][:]\n",
" y = indata.nc.variables['y'][:]\n",
"\n",
" x_data = grid.gather(x)\n",
" y_data = grid.gather(y)\n",
"\n",
" created = indata.nc.created\n",
" sim_args = json.loads(indata.nc.sim_args)\n",
" for key in sim_args:\n",
" if isinstance(sim_args[key], list):\n",
" sim_args[key] = \"[...]\"\n",
" num_steps = len(t)\n",
" print(f\"{outfile} created {created} contains {num_steps} timesteps\")\n",
" print(\"Simulator arguments: \\n\", sim_args)\n",
"\n",
" for i in range(num_steps):\n",
" h = indata.nc.variables['h'][i]\n",
" #hu = indata.ncfile.variables['hu'][i]\n",
" #hv = indata.ncfile.variables['hv'][i]\n",
" h_data = grid.gather(h)\n",
"\n",
" #Plot on rank 0\n",
" if grid.comm.rank == 0:\n",
" plt.figure(figsize=(16, 12))\n",
" for k in range(grid.comm.size):\n",
" extent = [x_data[k].min(), x_data[k].max(), y_data[k].min(), y_data[k].max()]\n",
" plt.imshow(h_data[k], extent=extent, origin='lower', vmin=0.49, vmax=0.52)\n",
" plot_extent(extent, 'k:', alpha=0.1)\n",
" plt.title(\"t=\" + str(t[i]))\n",
" plt.colorbar(shrink=0.33)"
]
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": ""
}
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