lbm_cl.py 4.28 KB
 ph committed Nov 28, 2017 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ``````#!/usr/bin/env python3 # -*- coding: utf-8 -*- # resolution of a transport equation by the finite volume method # on regular grid # regular python implementation compared to a pyopencl version from __future__ import absolute_import, print_function import pyopencl as cl import numpy as np import matplotlib.pyplot as plt import time ##################" definition of default values # number of conservative variables _m = 1 # number of kinetic variables _n = 4 * _m # grid size `````` ph committed Nov 28, 2017 28 29 ``````_nx = 1024 _ny = 1024 `````` ph committed Nov 28, 2017 30 31 32 33 34 35 36 37 38 39 40 `````` Lx = 1. Ly = 1. _dx = Lx / _nx _dy = Ly / _ny # transport velocity vel = np.array([1., 1.]) # lattice speed `````` ph committed Nov 28, 2017 41 ``````_vmax = 3. `````` ph committed Nov 28, 2017 42 43 44 `````` # time stepping `````` ph committed Nov 28, 2017 45 46 ``````_Tmax = 10. / _vmax #_Tmax = 0. `````` ph committed Nov 28, 2017 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 ``````cfl = 1 _dt = cfl * _dx / _vmax ############# end of default values def exact_sol(xy, t): x = xy[0] - t * vel[0] - 0.5 y = xy[1] - t * vel[1] - 0.5 d2 = x * x + y *y w = np.exp(-30*d2) return w def solve_ocl(m = _m, n = _n, nx = _nx, ny = _ny, Tmax = _Tmax, vmax = _vmax, dx = _dx, dy = _dy, dt = _dt, exact_sol = exact_sol, animate = False): # load and adjust C program source = open("lbm_kernels.cl", "r").read() source = source.replace("_nx_", "("+str(nx)+")") source = source.replace("_ny_", "("+str(ny)+")") source = source.replace("_dx_", "("+str(dx)+"f)") source = source.replace("_dy_", "("+str(dy)+"f)") source = source.replace("_dt_", "("+str(dt)+"f)") source = source.replace("_m_", "("+str(m)+")") source = source.replace("_n_", "("+str(n)+")") source = source.replace("_vx_", "("+str(vel[0])+"f)") source = source.replace("_vy_", "("+str(vel[1])+"f)") source = source.replace("_lambda_", "("+str(vmax)+"f)") #print(source) #exit(0) # OpenCL init ctx = cl.create_some_context() mf = cl.mem_flags # compile OpenCL C program prg = cl.Program(ctx, source).build(options = "-cl-strict-aliasing \ -cl-fast-relaxed-math") # create OpenCL buffers fn_gpu = cl.Buffer(ctx, mf.READ_WRITE, size=(4 * m * nx * ny * np.dtype('float32').itemsize)) fnp1_gpu = cl.Buffer(ctx, mf.READ_WRITE, size=(4 * m * nx * ny * np.dtype('float32').itemsize)) # create a queue (for submitting opencl operations) queue = cl.CommandQueue(ctx, properties=cl.command_queue_properties.PROFILING_ENABLE) # init data event = prg.init_sol(queue, (nx * ny, ), (32, ), fn_gpu) event.wait() # number of animation frames `````` ph committed Nov 28, 2017 107 `````` nbplots = 100 `````` ph committed Nov 28, 2017 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 `````` itermax = int(np.floor(Tmax / dt)) iterplot = int(itermax / nbplots) # time loop t = 0 iter = 0 elapsed = 0.; fn_cpu = np.empty((4 * m * nx * ny, ), dtype = np.float32) print("start OpenCL computations...") while t < Tmax: t = t + dt iter = iter + 1 #event = prg.time_step(queue, (nx * ny, ), (32, ), wn_gpu, wnp1_gpu) event = prg.time_step(queue, (nx * ny, ), (64, ), fn_gpu, fnp1_gpu) #event = prg.time_step(queue, (nx * ny, ), (32, ), wn_gpu, wnp1_gpu, wait_for = [event]) event.wait() elapsed += 1e-9 * (event.profile.end - event.profile.start) # exchange buffer references for avoiding a copy fn_gpu, fnp1_gpu = fnp1_gpu, fn_gpu print("iter=",iter, " t=",t, "elapsed (s)=",elapsed) if iter % iterplot == 0 and animate: cl.enqueue_copy(queue, fn_cpu, fn_gpu).wait() `````` ph committed Nov 28, 2017 131 `````` wplot = np.reshape(fn_cpu, (n, nx, ny)) `````` ph committed Nov 28, 2017 132 `````` plt.clf() `````` ph committed Nov 28, 2017 133 134 `````` #plt.imshow(np.sum(wplot, axis = 0),vmin=0, vmax=1) plt.imshow(np.sum(wplot, axis = 0)) `````` ph committed Nov 28, 2017 135 136 137 138 139 140 141 `````` plt.gca().invert_yaxis() plt.colorbar() plt.pause(0.01) # copy OpenCL data to CPU and return the results cl.enqueue_copy(queue, fn_cpu, fn_gpu).wait() `````` ph committed Nov 28, 2017 142 `````` wplot_gpu = np.reshape(fn_cpu,(n, nx, ny)) `````` ph committed Nov 28, 2017 143 144 145 `````` return wplot_gpu # gpu solve `````` ph committed Nov 28, 2017 146 ``````wplot_gpu = solve_ocl(animate = True) `````` ph committed Nov 28, 2017 147 ``````plt.clf() `````` ph committed Nov 28, 2017 148 ``````plt.imshow(np.sum(wplot_gpu,axis=0), vmin=0, vmax=1) `````` ph committed Nov 28, 2017 149 150 151 152 ``````plt.gca().invert_yaxis() plt.colorbar() plt.show() `````` ph committed Nov 28, 2017 153 154 155 156 157 158 ``````# for iv in range(4): # plt.imshow(wplot_gpu[iv,:,:]) # plt.gca().invert_yaxis() # plt.colorbar() # plt.show() `````` ph committed Nov 28, 2017 159 160 161 162 163 164 165 166 167 `````` # check difference # plt.clf() # plt.imshow(wplot_cpu-wplot_gpu) # plt.gca().invert_yaxis() # plt.colorbar() # plt.show() ``````