# -*- coding: utf-8 -*- """ This python module implements the classical Lax-Friedrichs numerical scheme for the shallow water equations Copyright (C) 2016 SINTEF ICT This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . """ #Import packages we need import numpy as np import logging import pycuda.compiler as cuda_compiler import pycuda.gpuarray import pycuda.driver as cuda from GPUSimulators import Common class BaseSimulator: """ Initialization routine context: GPU context to use kernel_wrapper: wrapper function of GPU kernel h0: Water depth incl ghost cells, (nx+1)*(ny+1) cells hu0: Initial momentum along x-axis incl ghost cells, (nx+1)*(ny+1) cells hv0: Initial momentum along y-axis incl ghost cells, (nx+1)*(ny+1) cells nx: Number of cells along x-axis ny: Number of cells along y-axis dx: Grid cell spacing along x-axis (20 000 m) dy: Grid cell spacing along y-axis (20 000 m) dt: Size of each timestep (90 s) g: Gravitational accelleration (9.81 m/s^2) """ def __init__(self, \ context, \ nx, ny, \ dx, dy, dt, \ g, \ block_width, block_height): #Get logger self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__) self.context = context if (self.context.autotuner): peak_configuration = self.context.autotuner.get_peak_performance(self.__class__) block_width = int(peak_configuration["block_width"]) block_height = int(peak_configuration["block_height"]) self.logger.debug("Used autotuning to get block size [%d x %d]", block_width, block_height) #Create a CUDA stream self.stream = cuda.Stream() #Save input parameters #Notice that we need to specify them in the correct dataformat for the #GPU kernel self.nx = np.int32(nx) self.ny = np.int32(ny) self.dx = np.float32(dx) self.dy = np.float32(dy) self.dt = np.float32(dt) self.g = np.float32(g) #Keep track of simulation time self.t = 0.0; #Compute kernel launch parameters self.local_size = (block_width, block_height, 1) self.global_size = ( \ int(np.ceil(self.nx / float(self.local_size[0]))), \ int(np.ceil(self.ny / float(self.local_size[1]))) \ ) """ Function which simulates forward in time using the default simulation type """ def simulate(self, t_end): raise(exceptions.NotImplementedError("Needs to be implemented in subclass")) """ Function which simulates t_end seconds using forward Euler Requires that the stepEuler functionality is implemented in the subclasses """ def simulateEuler(self, t_end): with Common.Timer(self.__class__.__name__ + ".simulateEuler") as t: # Compute number of timesteps to perform n = int(t_end / self.dt + 1) for i in range(0, n): # Compute timestep for "this" iteration local_dt = np.float32(min(self.dt, t_end-i*self.dt)) # Stop if end reached (should not happen) if (local_dt <= 0.0): break # Step with forward Euler self.stepEuler(local_dt) self.logger.info("%s simulated %f seconds to %f with %d steps in %f seconds", self.__class__.__name__, t_end, self.t, n, t.secs) return self.t, n """ Function which simulates t_end seconds using Runge-Kutta 2 Requires that the stepRK functionality is implemented in the subclasses """ def simulateRK(self, t_end, order): with Common.Timer(self.__class__.__name__ + ".simulateRK") as t: # Compute number of timesteps to perform n = int(t_end / self.dt + 1) for i in range(0, n): # Compute timestep for "this" iteration local_dt = np.float32(min(self.dt, t_end-i*self.dt)) # Stop if end reached (should not happen) if (local_dt <= 0.0): break # Perform all the Runge-Kutta substeps self.stepRK(local_dt, order) self.logger.info("%s simulated %f seconds to %f with %d steps in %f seconds", self.__class__.__name__, t_end, self.t, n, t.secs) return self.t, n """ Function which simulates t_end seconds using second order dimensional splitting (XYYX) Requires that the stepDimsplitX and stepDimsplitY functionality is implemented in the subclasses """ def simulateDimsplit(self, t_end): with Common.Timer(self.__class__.__name__ + ".simulateDimsplit") as t: # Compute number of timesteps to perform n = int(t_end / (2.0*self.dt) + 1) for i in range(0, n): # Compute timestep for "this" iteration local_dt = np.float32(0.5*min(2*self.dt, t_end-2*i*self.dt)) # Stop if end reached (should not happen) if (local_dt <= 0.0): break # Perform the dimensional split substeps self.stepDimsplitXY(local_dt) self.stepDimsplitYX(local_dt) self.logger.info("%s simulated %f seconds to %f with %d steps in %f seconds", self.__class__.__name__, t_end, self.t, 2*n, t.secs) return self.t, 2*n """ Function which performs one single timestep of size dt using forward euler """ def stepEuler(self, dt): raise(NotImplementedError("Needs to be implemented in subclass")) def stepRK(self, dt, substep): raise(NotImplementedError("Needs to be implemented in subclass")) def stepDimsplitXY(self, dt): raise(NotImplementedError("Needs to be implemented in subclass")) def stepDimsplitYX(self, dt): raise(NotImplementedError("Needs to be implemented in subclass")) def sim_time(self): return self.t def download(self): raise(NotImplementedError("Needs to be implemented in subclass")) def synchronize(self): self.stream.synchronize()