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https://github.com/smyalygames/FiniteVolumeGPU.git
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Running multiple CUDA contexts per process/thread
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Martin Lilleeng Sætra
358
GPUSimulators/SHMEMSimulator.py
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358
GPUSimulators/SHMEMSimulator.py
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# -*- coding: utf-8 -*-
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"""
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This python module implements SHMEM simulator class
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Copyright (C) 2020 Norwegian Meteorological Institute
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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import logging
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from GPUSimulators import Simulator, CudaContext
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import numpy as np
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import pycuda.driver as cuda
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class SHMEMGrid(object):
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"""
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Class which represents an SHMEM grid of GPUs. Facilitates easy communication between
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neighboring subdomains in the grid. Contains one CUDA context per subdomain.
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"""
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def __init__(self, ngpus=None, ndims=2):
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self.logger = logging.getLogger(__name__)
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cuda.init(flags=0)
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self.logger.info("Initializing CUDA")
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num_cuda_devices = cuda.Device.count()
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if ngpus is None:
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ngpus = num_cuda_devices
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assert ngpus <= num_cuda_devices, "Trying to allocate more GPUs than are available in the system."
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assert ndims == 2, "Unsupported number of dimensions. Must be two at the moment"
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assert ngpus >= 2, "Must have at least two GPUs available to run multi-GPU simulations."
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self.ngpus = ngpus
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self.ndims = ndims
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self.grid = SHMEMGrid.getGrid(self.ngpus, self.ndims)
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self.logger.debug("Created {:}-dimensional SHMEM grid, using {:} GPUs".format(
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self.ndims, self.ngpus))
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self.cuda_contexts = []
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for i in range(self.ngpus):
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self.cuda_contexts.append(CudaContext.CudaContext(device=i, autotuning=False))
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def getCoordinate(self, index):
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i = (index % self.grid[0])
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j = (index // self.grid[0])
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return i, j
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def getIndex(self, i, j):
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return j*self.grid[0] + i
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def getEast(self, index):
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i, j = self.getCoordinate(index)
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i = (i+1) % self.grid[0]
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return self.getIndex(i, j)
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def getWest(self, index):
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i, j = self.getCoordinate(index)
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i = (i+self.grid[0]-1) % self.grid[0]
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return self.getIndex(i, j)
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def getNorth(self, index):
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i, j = self.getCoordinate(index)
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j = (j+1) % self.grid[1]
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return self.getIndex(i, j)
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def getSouth(self, index):
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i, j = self.getCoordinate(index)
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j = (j+self.grid[1]-1) % self.grid[1]
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return self.getIndex(i, j)
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def getGrid(num_gpus, num_dims):
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assert(isinstance(num_gpus, int))
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assert(isinstance(num_dims, int))
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# Adapted from https://stackoverflow.com/questions/28057307/factoring-a-number-into-roughly-equal-factors
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# Original code by https://stackoverflow.com/users/3928385/ishamael
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# Factorizes a number into n roughly equal factors
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#Dictionary to remember already computed permutations
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memo = {}
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def dp(n, left): # returns tuple (cost, [factors])
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"""
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Recursively searches through all factorizations
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"""
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#Already tried: return existing result
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if (n, left) in memo:
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return memo[(n, left)]
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#Spent all factors: return number itself
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if left == 1:
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return (n, [n])
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#Find new factor
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i = 2
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best = n
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bestTuple = [n]
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while i * i < n:
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#If factor found
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if n % i == 0:
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#Factorize remainder
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rem = dp(n // i, left - 1)
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#If new permutation better, save it
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if rem[0] + i < best:
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best = rem[0] + i
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bestTuple = [i] + rem[1]
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i += 1
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#Store calculation
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memo[(n, left)] = (best, bestTuple)
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return memo[(n, left)]
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grid = dp(num_gpus, num_dims)[1]
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if (len(grid) < num_dims):
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#Split problematic 4
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if (4 in grid):
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grid.remove(4)
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grid.append(2)
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grid.append(2)
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#Pad with ones to guarantee num_dims
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grid = grid + [1]*(num_dims - len(grid))
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#Sort in descending order
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grid = np.sort(grid)
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grid = grid[::-1]
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return grid
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class SHMEMSimulator(Simulator.BaseSimulator):
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"""
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Class which handles communication between simulators on different GPUs
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"""
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def __init__(self, sim, grid):
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self.logger = logging.getLogger(__name__)
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autotuner = sim.context.autotuner
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sim.context.autotuner = None;
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boundary_conditions = sim.getBoundaryConditions()
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super().__init__(sim.context,
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sim.nx, sim.ny,
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sim.dx, sim.dy,
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boundary_conditions,
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sim.cfl_scale,
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sim.num_substeps,
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sim.block_size[0], sim.block_size[1])
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sim.context.autotuner = autotuner
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self.sim = sim
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self.grid = grid
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#Get neighbor subdomain ids
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self.east = grid.getEast()
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self.west = grid.getWest()
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self.north = grid.getNorth()
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self.south = grid.getSouth()
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#Get coordinate of this subdomain
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#and handle global boundary conditions
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new_boundary_conditions = Simulator.BoundaryCondition({
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'north': Simulator.BoundaryCondition.Type.Dirichlet,
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'south': Simulator.BoundaryCondition.Type.Dirichlet,
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'east': Simulator.BoundaryCondition.Type.Dirichlet,
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'west': Simulator.BoundaryCondition.Type.Dirichlet
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})
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gi, gj = grid.getCoordinate()
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if (gi == 0 and boundary_conditions.west != Simulator.BoundaryCondition.Type.Periodic):
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self.west = None
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new_boundary_conditions.west = boundary_conditions.west;
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if (gj == 0 and boundary_conditions.south != Simulator.BoundaryCondition.Type.Periodic):
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self.south = None
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new_boundary_conditions.south = boundary_conditions.south;
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if (gi == grid.grid[0]-1 and boundary_conditions.east != Simulator.BoundaryCondition.Type.Periodic):
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self.east = None
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new_boundary_conditions.east = boundary_conditions.east;
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if (gj == grid.grid[1]-1 and boundary_conditions.north != Simulator.BoundaryCondition.Type.Periodic):
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self.north = None
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new_boundary_conditions.north = boundary_conditions.north;
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sim.setBoundaryConditions(new_boundary_conditions)
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#Get number of variables
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self.nvars = len(self.getOutput().gpu_variables)
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#Shorthands for computing extents and sizes
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gc_x = int(self.sim.getOutput()[0].x_halo)
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gc_y = int(self.sim.getOutput()[0].y_halo)
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nx = int(self.sim.nx)
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ny = int(self.sim.ny)
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#Set regions for ghost cells to read from
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#These have the format [x0, y0, width, height]
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self.read_e = np.array([ nx, 0, gc_x, ny + 2*gc_y])
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self.read_w = np.array([gc_x, 0, gc_x, ny + 2*gc_y])
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self.read_n = np.array([gc_x, ny, nx, gc_y])
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self.read_s = np.array([gc_x, gc_y, nx, gc_y])
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#Set regions for ghost cells to write to
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self.write_e = self.read_e + np.array([gc_x, 0, 0, 0])
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self.write_w = self.read_w - np.array([gc_x, 0, 0, 0])
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self.write_n = self.read_n + np.array([0, gc_y, 0, 0])
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self.write_s = self.read_s - np.array([0, gc_y, 0, 0])
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#Allocate data for receiving
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#Note that east and west also transfer ghost cells
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#whilst north/south only transfer internal cells
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#Reuses the width/height defined in the read-extets above
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self.in_e = np.empty((self.nvars, self.read_e[3], self.read_e[2]), dtype=np.float32)
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self.in_w = np.empty((self.nvars, self.read_w[3], self.read_w[2]), dtype=np.float32)
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self.in_n = np.empty((self.nvars, self.read_n[3], self.read_n[2]), dtype=np.float32)
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self.in_s = np.empty((self.nvars, self.read_s[3], self.read_s[2]), dtype=np.float32)
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#Allocate data for sending
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self.out_e = np.empty_like(self.in_e)
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self.out_w = np.empty_like(self.in_w)
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self.out_n = np.empty_like(self.in_n)
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self.out_s = np.empty_like(self.in_s)
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self.logger.debug("Simlator subdomain {:d} initialized on {:s}".format(self.grid.comm.rank, MPI.Get_processor_name()))
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def substep(self, dt, step_number):
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self.exchange()
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self.sim.substep(dt, step_number)
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def getOutput(self):
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return self.sim.getOutput()
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def synchronize(self):
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self.sim.synchronize()
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def check(self):
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return self.sim.check()
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def computeDt(self):
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local_dt = np.array([np.float32(self.sim.computeDt())]);
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global_dt = np.empty(1, dtype=np.float32)
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self.grid.comm.Allreduce(local_dt, global_dt, op=MPI.MIN)
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self.logger.debug("Local dt: {:f}, global dt: {:f}".format(local_dt[0], global_dt[0]))
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return global_dt[0]
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def getExtent(self):
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"""
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Function which returns the extent of node with rank
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rank in the grid
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"""
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width = self.sim.nx*self.sim.dx
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height = self.sim.ny*self.sim.dy
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i, j = self.grid.getCoordinate()
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x0 = i * width
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y0 = j * height
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x1 = x0 + width
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y1 = y0 + height
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return [x0, x1, y0, y1]
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def exchange(self):
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####
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# First transfer internal cells north-south
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####
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#Download from the GPU
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if self.north is not None:
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for k in range(self.nvars):
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self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_n[k,:,:], asynch=True, extent=self.read_n)
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if self.south is not None:
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for k in range(self.nvars):
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self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_s[k,:,:], asynch=True, extent=self.read_s)
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self.sim.stream.synchronize()
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#Send/receive to north/south neighbours
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comm_send = []
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comm_recv = []
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if self.north is not None:
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comm_send += [self.grid.comm.Isend(self.out_n, dest=self.north, tag=4*self.nt + 0)]
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comm_recv += [self.grid.comm.Irecv(self.in_n, source=self.north, tag=4*self.nt + 1)]
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if self.south is not None:
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comm_send += [self.grid.comm.Isend(self.out_s, dest=self.south, tag=4*self.nt + 1)]
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comm_recv += [self.grid.comm.Irecv(self.in_s, source=self.south, tag=4*self.nt + 0)]
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#Wait for incoming transfers to complete
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for comm in comm_recv:
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comm.wait()
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#Upload to the GPU
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if self.north is not None:
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for k in range(self.nvars):
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self.sim.u0[k].upload(self.sim.stream, self.in_n[k,:,:], extent=self.write_n)
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if self.south is not None:
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for k in range(self.nvars):
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self.sim.u0[k].upload(self.sim.stream, self.in_s[k,:,:], extent=self.write_s)
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#Wait for sending to complete
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for comm in comm_send:
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comm.wait()
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####
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# Then transfer east-west including ghost cells that have been filled in by north-south transfer above
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####
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#Download from the GPU
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if self.east is not None:
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for k in range(self.nvars):
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self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_e[k,:,:], asynch=True, extent=self.read_e)
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if self.west is not None:
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for k in range(self.nvars):
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self.sim.u0[k].download(self.sim.stream, cpu_data=self.out_w[k,:,:], asynch=True, extent=self.read_w)
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self.sim.stream.synchronize()
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#Send/receive to east/west neighbours
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comm_send = []
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comm_recv = []
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if self.east is not None:
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comm_send += [self.grid.comm.Isend(self.out_e, dest=self.east, tag=4*self.nt + 2)]
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comm_recv += [self.grid.comm.Irecv(self.in_e, source=self.east, tag=4*self.nt + 3)]
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if self.west is not None:
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comm_send += [self.grid.comm.Isend(self.out_w, dest=self.west, tag=4*self.nt + 3)]
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comm_recv += [self.grid.comm.Irecv(self.in_w, source=self.west, tag=4*self.nt + 2)]
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#Wait for incoming transfers to complete
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for comm in comm_recv:
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comm.wait()
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#Upload to the GPU
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if self.east is not None:
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for k in range(self.nvars):
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self.sim.u0[k].upload(self.sim.stream, self.in_e[k,:,:], extent=self.write_e)
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if self.west is not None:
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for k in range(self.nvars):
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self.sim.u0[k].upload(self.sim.stream, self.in_w[k,:,:], extent=self.write_w)
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#Wait for sending to complete
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for comm in comm_send:
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comm.wait()
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@@ -1,7 +1,7 @@
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# -*- coding: utf-8 -*-
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"""
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This python module implements Cuda context handling
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This python module implements visualization techniques/modes
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Copyright (C) 2018 SINTEF ICT
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