mirror of
https://github.com/smyalygames/FiniteVolumeGPU.git
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160 lines
5.3 KiB
Python
160 lines
5.3 KiB
Python
# -*- coding: utf-8 -*-
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"""
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This python module implements the 2nd order HLL flux
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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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import numpy as np
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import pycuda.compiler as cuda_compiler
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import pycuda.gpuarray
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import pycuda.driver as cuda
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from SWESimulators import Common
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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 HLL2:
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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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theta=1.8, \
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block_width=16, block_height=16):
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#Create a CUDA stream
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self.stream = cuda.Stream()
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#Get kernels
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self.hll2_module = context.get_kernel("HLL2_kernel.cu", block_width, block_height)
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self.hll2_kernel = self.hll2_module.get_function("HLL2Kernel")
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self.hll2_kernel.prepare("iifffffiPiPiPiPiPiPi")
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#Create data by uploading to device
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ghost_cells_x = 2
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ghost_cells_y = 2
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self.data = Common.SWEDataArakawaA(self.stream, \
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nx, ny, \
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ghost_cells_x, ghost_cells_y, \
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h0, hu0, hv0)
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#Save input parameters
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#Notice that we need to specify them in the correct dataformat for the
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#OpenCL kernel
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self.nx = np.int32(nx)
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self.ny = np.int32(ny)
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self.dx = np.float32(dx)
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self.dy = np.float32(dy)
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self.dt = np.float32(dt)
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self.g = np.float32(g)
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self.theta = np.float32(theta)
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#Initialize time
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self.t = np.float32(0.0)
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#Compute kernel launch parameters
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self.local_size = (block_width, block_height, 1)
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self.global_size = ( \
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int(np.ceil(self.nx / float(self.local_size[0]))), \
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int(np.ceil(self.ny / float(self.local_size[1]))) \
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)
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def __str__(self):
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return "Harten-Lax-van Leer (2nd order)"
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"""
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Function which steps n timesteps
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"""
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def step(self, t_end=0.0):
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n = int(t_end / (2.0*self.dt) + 1)
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for i in range(0, n):
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#Dimensional splitting: second order accurate for every other timestep,
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#thus run two timesteps in a go
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local_dt = np.float32(0.5*min(2*self.dt, t_end-2*i*self.dt))
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if (local_dt <= 0.0):
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break
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#Along X, then Y
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self.hll2_kernel.prepared_async_call(self.global_size, self.local_size, self.stream, \
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self.nx, self.ny, \
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self.dx, self.dy, local_dt, \
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self.g, \
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self.theta, \
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np.int32(0), \
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self.data.h0.data.gpudata, self.data.h0.pitch, \
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self.data.hu0.data.gpudata, self.data.hu0.pitch, \
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self.data.hv0.data.gpudata, self.data.hv0.pitch, \
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self.data.h1.data.gpudata, self.data.h1.pitch, \
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self.data.hu1.data.gpudata, self.data.hu1.pitch, \
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self.data.hv1.data.gpudata, self.data.hv1.pitch)
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self.data.swap()
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#Along Y, then X
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self.hll2_kernel.prepared_async_call(self.global_size, self.local_size, self.stream, \
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self.nx, self.ny, \
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self.dx, self.dy, local_dt, \
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self.g, \
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self.theta, \
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np.int32(1), \
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self.data.h0.data.gpudata, self.data.h0.pitch, \
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self.data.hu0.data.gpudata, self.data.hu0.pitch, \
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self.data.hv0.data.gpudata, self.data.hv0.pitch, \
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self.data.h1.data.gpudata, self.data.h1.pitch, \
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self.data.hu1.data.gpudata, self.data.hu1.pitch, \
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self.data.hv1.data.gpudata, self.data.hv1.pitch)
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self.data.swap()
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self.t += local_dt
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return self.t
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def download(self):
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return self.data.download(self.stream)
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