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https://github.com/smyalygames/FiniteVolumeGPU.git
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125 lines
4.7 KiB
Python
125 lines
4.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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This python module implements the Weighted average flux (WAF) described in
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E. Toro, Shock-Capturing methods for free-surface shallow flows, 2001
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Copyright (C) 2016 SINTEF ICT
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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 packages we need
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from GPUSimulators import Simulator, Common
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import numpy as np
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"""
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Class that solves the SW equations using the Forward-Backward linear scheme
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"""
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class WAF (Simulator.BaseSimulator):
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"""
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Initialization routine
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h0: Water depth incl ghost cells, (nx+1)*(ny+1) cells
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hu0: Initial momentum along x-axis incl ghost cells, (nx+1)*(ny+1) cells
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hv0: Initial momentum along y-axis incl ghost cells, (nx+1)*(ny+1) cells
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nx: Number of cells along x-axis
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ny: Number of cells along y-axis
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dx: Grid cell spacing along x-axis (20 000 m)
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dy: Grid cell spacing along y-axis (20 000 m)
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dt: Size of each timestep (90 s)
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g: Gravitational accelleration (9.81 m/s^2)
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"""
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def __init__(self, \
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context, \
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h0, hu0, hv0, \
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nx, ny, \
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dx, dy, dt, \
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g, \
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block_width=16, block_height=16):
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# Call super constructor
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super().__init__(context, \
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nx, ny, \
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dx, dy, dt, \
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block_width, block_height);
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self.g = np.float32(g)
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#Get kernels
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self.kernel = context.get_prepared_kernel("cuda/SWE2D_WAF.cu", "WAFKernel", \
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"iiffffiPiPiPiPiPiPi", \
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defines={
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'BLOCK_WIDTH': self.block_size[0],
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'BLOCK_HEIGHT': self.block_size[1]
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}, \
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compile_args={
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'no_extern_c': True,
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'options': ["--use_fast_math"],
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}, \
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jit_compile_args={})
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#Create data by uploading to device
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self.u0 = Common.ArakawaA2D(self.stream, \
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nx, ny, \
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2, 2, \
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[h0, hu0, hv0])
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self.u1 = Common.ArakawaA2D(self.stream, \
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nx, ny, \
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2, 2, \
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[None, None, None])
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def simulate(self, t_end):
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return super().simulateDimsplit(t_end)
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def stepEuler(self, dt):
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return self.stepDimsplitXY(dt)
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def stepDimsplitXY(self, dt):
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self.kernel.prepared_async_call(self.grid_size, self.block_size, self.stream, \
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self.nx, self.ny, \
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self.dx, self.dy, dt, \
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self.g, \
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np.int32(0), \
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self.u0[0].data.gpudata, self.u0[0].data.strides[0], \
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self.u0[1].data.gpudata, self.u0[1].data.strides[0], \
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self.u0[2].data.gpudata, self.u0[2].data.strides[0], \
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self.u1[0].data.gpudata, self.u1[0].data.strides[0], \
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self.u1[1].data.gpudata, self.u1[1].data.strides[0], \
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self.u1[2].data.gpudata, self.u1[2].data.strides[0])
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self.u0, self.u1 = self.u1, self.u0
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self.t += dt
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self.nt += 1
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def stepDimsplitYX(self, dt):
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self.kernel.prepared_async_call(self.grid_size, self.block_size, self.stream, \
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self.nx, self.ny, \
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self.dx, self.dy, dt, \
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self.g, \
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np.int32(1), \
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self.u0[0].data.gpudata, self.u0[0].data.strides[0], \
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self.u0[1].data.gpudata, self.u0[1].data.strides[0], \
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self.u0[2].data.gpudata, self.u0[2].data.strides[0], \
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self.u1[0].data.gpudata, self.u1[0].data.strides[0], \
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self.u1[1].data.gpudata, self.u1[1].data.strides[0], \
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self.u1[2].data.gpudata, self.u1[2].data.strides[0])
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self.u0, self.u1 = self.u1, self.u0
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self.t += dt
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self.nt += 1
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def download(self):
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return self.u0.download(self.stream) |