# -*- 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 from enum import IntEnum import pycuda.compiler as cuda_compiler import pycuda.gpuarray import pycuda.driver as cuda from GPUSimulators import Common class BoundaryCondition(object): """ Class for holding boundary conditions for global boundaries """ class Type(IntEnum): """ Enum that describes the different types of boundary conditions WARNING: MUST MATCH THAT OF common.h IN CUDA """ Dirichlet = 0, Neumann = 1, Periodic = 2, Reflective = 3 def __init__(self, types={ 'north': Type.Reflective, 'south': Type.Reflective, 'east': Type.Reflective, 'west': Type.Reflective }): """ Constructor """ self.north = types['north'] self.south = types['south'] self.east = types['east'] self.west = types['west'] if (self.north == BoundaryCondition.Type.Neumann \ or self.south == BoundaryCondition.Type.Neumann \ or self.east == BoundaryCondition.Type.Neumann \ or self.west == BoundaryCondition.Type.Neumann): raise(NotImplementedError("Neumann boundary condition not supported")) def __str__(self): return '[north={:s}, south={:s}, east={:s}, west={:s}]'.format(str(self.north), str(self.south), str(self.east), str(self.west)) def asCodedInt(self): """ Helper function which packs four boundary conditions into one integer """ bc = 0 bc = bc | (self.north & 0x0000000F) << 24 bc = bc | (self.south & 0x0000000F) << 16 bc = bc | (self.east & 0x0000000F) << 8 bc = bc | (self.west & 0x0000000F) #for t in types: # print("{0:s}, {1:d}, {1:032b}, {1:08b}".format(t, types[t])) #print("bc: {0:032b}".format(bc)) return np.int32(bc) class BaseSimulator(object): def __init__(self, context, nx, ny, dx, dy, cfl_scale, num_substeps, block_width, block_height): """ 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) cfl_scale: Courant number num_substeps: Number of substeps to perform for a full step """ #Get logger self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__) #Save input parameters #Notice that we need to specify them in the correct dataformat for the #GPU kernel self.context = context self.nx = np.int32(nx) self.ny = np.int32(ny) self.dx = np.float32(dx) self.dy = np.float32(dy) self.dt = None self.cfl_scale = cfl_scale self.num_substeps = num_substeps #Handle autotuning block size 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) #Compute kernel launch parameters self.block_size = (block_width, block_height, 1) self.grid_size = ( int(np.ceil(self.nx / float(self.block_size[0]))), int(np.ceil(self.ny / float(self.block_size[1]))) ) #Create a CUDA stream self.stream = cuda.Stream() #Keep track of simulation time and number of timesteps self.t = 0.0 self.nt = 0 def __str__(self): return "{:s} [{:d}x{:d}]".format(self.__class__.__name__, self.nx, self.ny) def simulate(self, t, dt=None): """ Function which simulates t_end seconds using the step function Requires that the step() function is implemented in the subclasses """ printer = Common.ProgressPrinter(t) t_start = self.simTime() t_end = t_start + t update_dt = True if (dt is not None): update_dt = False self.dt = dt while(self.simTime() < t_end): # Update dt every 100 timesteps and cross your fingers it works # for the next 100 if (update_dt and (self.simSteps() % 100 == 0)): self.dt = self.computeDt()*self.cfl_scale # Compute timestep for "this" iteration (i.e., shorten last timestep) current_dt = np.float32(min(self.dt, t_end-self.simTime())) # Stop if end reached (should not happen) if (current_dt <= 0.0): self.logger.warning("Timestep size {:d} is less than or equal to zero!".format(self.simSteps())) break # Step forward in time self.step(current_dt) #Print info print_string = printer.getPrintString(self.simTime() - t_start) if (print_string): self.logger.info("%s: %s", self, print_string) try: self.check() except AssertionError as e: e.args += ("Step={:d}, time={:f}".format(self.simSteps(), self.simTime()),) raise def step(self, dt): """ Function which performs one single timestep of size dt """ for i in range(self.num_substeps): self.substep(dt, i) self.t += dt self.nt += 1 def substep(self, dt, step_number): """ Function which performs one single substep with stepsize dt """ raise(NotImplementedError("Needs to be implemented in subclass")) def download(self): raise(NotImplementedError("Needs to be implemented in subclass")) def synchronize(self): self.stream.synchronize() def check(self): self.logger.warning("check() is not implemented - please implement") #raise(NotImplementedError("Needs to be implemented in subclass")) def simTime(self): return self.t def simSteps(self): return self.nt def computeDt(self): raise(NotImplementedError("Needs to be implemented in subclass")) def stepOrderToCodedInt(step, order): """ Helper function which packs the step and order into a single integer """ step_order = (step << 16) | (order & 0x0000ffff) #print("Step: {0:032b}".format(step)) #print("Order: {0:032b}".format(order)) #print("Mix: {0:032b}".format(step_order)) return np.int32(step_order)