# -*- 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.driver as cuda from GPUSimulators.common import ProgressPrinter from GPUSimulators.gpu import KernelContext def get_types(bc): types = {'north': BoundaryCondition.Type((bc >> 24) & 0x0000000F), 'south': BoundaryCondition.Type((bc >> 16) & 0x0000000F), 'east': BoundaryCondition.Type((bc >> 8) & 0x0000000F), 'west': BoundaryCondition.Type((bc >> 0) & 0x0000000F)} return types def step_order_to_coded_int(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) 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: dict[str: 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 f"[north={str(self.north)}, south={str(self.south)}, east={str(self.east)}, west={str(self.west)}]" def as_coded_int(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) << 0 # 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: KernelContext, nx: int, ny: int, dx: int, dy: int, boundary_conditions: BoundaryCondition, cfl_scale: float, num_substeps: int, block_width: int, block_height: int): """ Initialization routine Args: 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.set_boundary_conditions(boundary_conditions) 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(f"Used autotuning to get block size [{block_width} x {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() self.internal_stream = cuda.Stream() # Keep track of simulation time and number of timesteps self.t = 0.0 self.nt = 0 def __str__(self): return f"{self.__class__.__name__} [{self.nx}x{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 = ProgressPrinter(t) t_start = self.sim_time() t_end = t_start + t update_dt = True if dt is not None: update_dt = False self.dt = dt while self.sim_time() < t_end: # Update dt every 100 timesteps and cross your fingers it works # for the next 100 if update_dt and (self.sim_steps() % 100 == 0): self.dt = self.compute_dt() * self.cfl_scale # Compute timestep for "this" iteration (i.e., shorten last timestep) current_dt = np.float32(min(self.dt, t_end - self.sim_time())) # Stop if end reached (should not happen) if current_dt <= 0.0: self.logger.warning(f"Timestep size {self.sim_steps()} is less than or equal to zero!") break # Step forward in time self.step(current_dt) # Print info print_string = printer.get_print_string(self.sim_time() - t_start) if print_string: self.logger.info(f"{self}: {print_string}") try: self.check() except AssertionError as e: e.args += f"Step={self.sim_steps()}, time={self.sim_time()}" raise def step(self, dt: int): """ Function which performs one single timestep of size dt Args: dt: Size of each timestep (seconds) """ for i in range(self.num_substeps): self.substep(dt, i) self.t += dt self.nt += 1 def download(self, variables=None): return self.get_output().download(self.stream, variables) def synchronize(self): self.stream.synchronize() def sim_time(self): return self.t def sim_steps(self): return self.nt def get_extent(self): return [0, 0, self.nx * self.dx, self.ny * self.dy] def set_boundary_conditions(self, boundary_conditions): self.logger.debug(f"Boundary conditions set to {str(boundary_conditions)}") self.boundary_conditions = boundary_conditions.as_coded_int() def get_boundary_conditions(self): return BoundaryCondition(get_types()) def substep(self, dt, step_number): """ Function which performs one single substep with stepsize dt """ raise (NotImplementedError("Needs to be implemented in subclass")) def get_output(self): raise (NotImplementedError("Needs to be implemented in subclass")) def check(self): self.logger.warning("check() is not implemented - please implement") # raise(NotImplementedError("Needs to be implemented in subclass")) def compute_dt(self): raise (NotImplementedError("Needs to be implemented in subclass"))