1#!/usr/bin/env python3 2# Copyright (c) 2017-2018, Lawrence Livermore National Security, LLC. 3# Produced at the Lawrence Livermore National Laboratory. LLNL-CODE-734707. 4# All Rights reserved. See files LICENSE and NOTICE for details. 5# 6# This file is part of CEED, a collection of benchmarks, miniapps, software 7# libraries and APIs for efficient high-order finite element and spectral 8# element discretizations for exascale applications. For more information and 9# source code availability see http://github.com/ceed 10# 11# The CEED research is supported by the Exascale Computing Project 17-SC-20-SC, 12# a collaborative effort of two U.S. Department of Energy organizations (Office 13# of Science and the National Nuclear Security Administration) responsible for 14# the planning and preparation of a capable exascale ecosystem, including 15# software, applications, hardware, advanced system engineering and early 16# testbed platforms, in support of the nation's exascale computing imperative. 17 18 19# Adjustable plot parameters: 20from pylab import * 21from matplotlib import use 22from postprocess_base import read_logs 23import pandas as pd 24log_y = 0 # use log scale on the y-axis? 25x_range = (1e1, 4e6) # plot range for the x-axis; comment out for auto 26y_range = (0, 2e9) # plot range for the y-axis; comment out for auto 27draw_iter_lines = 0 # draw the "iter/s" lines? 28ymin_iter_lines = 3e5 # minimal y value for the "iter/s" lines 29ymax_iter_lines = 8e8 # maximal y value for the "iter/s" lines 30legend_ncol = (2 if log_y else 1) # number of columns in the legend 31write_figures = 1 # save the figures to files? 32show_figures = 1 # display the figures on the screen? 33 34 35# Load the data 36 37runs = read_logs() 38 39# Sample plot output 40if not show_figures: 41 use('pdf') 42 43rcParams['font.sans-serif'].insert(0, 'Noto Sans') 44rcParams['font.sans-serif'].insert(1, 'Open Sans') 45rcParams['figure.figsize'] = [10, 8] # default: 8 x 6 46 47cm_size = 16 48colors = ['dimgrey', 'black', 'saddlebrown', 'firebrick', 'red', 'orange', 49 'gold', 'lightgreen', 'green', 'cyan', 'teal', 'blue', 'navy', 50 'purple', 'magenta', 'pink'] 51 52# Get test names 53sel_runs = runs 54tests = list(sel_runs.test.unique()) 55test = tests[0] 56 57# Run information 58print('Using test:', test) 59 60if 'CEED Benchmark Problem' in test: 61 test_short = test.strip().split()[0] + ' BP' + test.strip().split()[-1] 62 63# Plot same BP 64sel_runs = sel_runs.loc[sel_runs['test'] == test] 65 66# Plot same case (scalar vs vector) 67cases = list(sel_runs.case.unique()) 68case = cases[0] 69vdim = 1 if case == 'scalar' else 3 70print('Using case:', case) 71sel_runs = sel_runs.loc[sel_runs['case'] == case] 72 73# Plot same 'code' 74codes = list(sel_runs.code.unique()) 75code = codes[0] 76sel_runs = sel_runs.loc[sel_runs['code'] == code] 77 78# Group plots by backend and number of processes 79pl_set = sel_runs[['backend', 80 'backend_memtype', 81 'num_procs', 82 'num_procs_node']] 83pl_set = pl_set.drop_duplicates() 84 85# Plotting 86for index, row in pl_set.iterrows(): 87 backend = row['backend'] 88 backend_memtype = row['backend_memtype'] 89 num_procs = float(row['num_procs']) 90 num_procs_node = float(row['num_procs_node']) 91 num_nodes = num_procs / num_procs_node 92 pl_runs = sel_runs[(sel_runs.backend == backend) | 93 (sel_runs.num_procs == num_procs) | 94 (sel_runs.num_procs_node == num_procs_node)] 95 if len(pl_runs.index) == 0: 96 continue 97 98 print('backend: %s, compute nodes: %i, number of MPI tasks = %i' % ( 99 backend, num_nodes, num_procs)) 100 101 figure() 102 i = 0 103 sol_p_set = sel_runs['degree'].drop_duplicates() 104 sol_p_set = sol_p_set.sort_values() 105 # Iterate over P 106 for sol_p in sol_p_set: 107 qpts = sel_runs['quadrature_pts'].loc[pl_runs['degree'] == sol_p] 108 qpts = qpts.drop_duplicates().sort_values(ascending=False) 109 qpts = qpts.reset_index(drop=True) 110 print('Degree: %i, quadrature points:' % sol_p, qpts[0]) 111 # Generate plot data 112 d = [[run['degree'], run['num_elem'], 1. * run['num_unknowns'] / num_nodes / vdim, 113 run['cg_iteration_dps'] / num_nodes] 114 for index, run in 115 pl_runs.loc[(pl_runs['degree'] == sol_p) | 116 (pl_runs['quadrature_pts'] == qpts[0])].iterrows()] 117 d = [[e[2], e[3]] for e in d if e[0] == sol_p] 118 # (DOFs/[sec/iter]/node)/(DOFs/node) = iter/sec 119 d = [[nun, 120 min([e[1] for e in d if e[0] == nun]), 121 max([e[1] for e in d if e[0] == nun])] 122 for nun in set([e[0] for e in d])] 123 d = asarray(sorted(d)) 124 # Plot 125 plot(d[:, 0], d[:, 2], 'o-', color=colors[i % cm_size], 126 label='p=%i' % sol_p) 127 if list(d[:, 1]) != list(d[:, 2]): 128 plot(d[:, 0], d[:, 1], 'o-', color=colors[i]) 129 fill_between(d[:, 0], d[:, 1], d[:, 2], 130 facecolor=colors[i], alpha=0.2) 131 # Continue if only 1 set of qpts 132 if len(qpts) == 1: 133 i = i + 1 134 continue 135 # Second set of qpts 136 d = [[run['degree'], run['num_elem'], 1. * run['num_unknowns'] / num_nodes / vdim, 137 run['cg_iteration_dps'] / num_nodes] 138 for index, run in 139 pl_runs.loc[(pl_runs['degree'] == sol_p) | 140 (pl_runs['quadrature_pts'] == qpts[1])].iterrows()] 141 d = [[e[2], e[3]] for e in d if e[0] == sol_p] 142 if len(d) == 0: 143 i = i + 1 144 continue 145 d = [[nun, 146 min([e[1] for e in d if e[0] == nun]), 147 max([e[1] for e in d if e[0] == nun])] 148 for nun in set([e[0] for e in d])] 149 d = asarray(sorted(d)) 150 plot(d[:, 0], d[:, 2], 's--', color=colors[i], 151 label='p=%i' % sol_p) 152 if list(d[:, 1]) != list(d[:, 2]): 153 plot(d[:, 0], d[:, 1], 's--', color=colors[i]) 154 ## 155 i = i + 1 156 ## 157 if draw_iter_lines: 158 y0, y1 = ymin_iter_lines, ymax_iter_lines 159 y = asarray([y0, y1]) if log_y else exp(linspace(log(y0), log(y1))) 160 slope1 = 600. 161 slope2 = 6000. 162 plot(y / slope1, y, 'k--', label='%g iter/s' % (slope1 / vdim)) 163 plot(y / slope2, y, 'k-', label='%g iter/s' % (slope2 / vdim)) 164 165 # Plot information 166 title(r'%i node%s $\times$ %i ranks, %s, %s, %s' % ( 167 num_nodes, '' if num_nodes == 1 else 's', 168 num_procs_node, backend, backend_memtype, test_short), fontsize=16) 169 xscale('log') # subsx=[2,4,6,8] 170 if log_y: 171 yscale('log') 172 if 'x_range' in vars() and len(x_range) == 2: 173 xlim(x_range) 174 if 'y_range' in vars() and len(y_range) == 2: 175 ylim(y_range) 176 grid('on', color='gray', ls='dotted') 177 grid('on', axis='both', which='minor', color='gray', ls='dotted') 178 plt.tick_params(labelsize=14) 179 exptext = gca().yaxis.get_offset_text() 180 exptext.set_size(14) 181 gca().set_axisbelow(True) 182 xlabel('Points per compute node', fontsize=14) 183 ylabel('[DOFs x CG iterations] / [compute nodes x seconds]', fontsize=14) 184 legend(ncol=legend_ncol, loc='best', fontsize=13) 185 186 # Write 187 if write_figures: # write .pdf file? 188 short_backend = backend.replace('/', '') 189 test_short_save = test_short.replace(' ', '') 190 pdf_file = 'plot_%s_%s_%s_%s_N%03i_pn%i.pdf' % ( 191 code, test_short_save, short_backend, backend_memtype, num_nodes, num_procs_node) 192 print('\nsaving figure --> %s' % pdf_file) 193 savefig(pdf_file, format='pdf', bbox_inches='tight') 194 195if show_figures: # show the figures? 196 print('\nShowing figures ...') 197 show() 198