1 | #!/usr/bin/python |
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2 | |
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3 | import subprocess |
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4 | import os |
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5 | import re |
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6 | import sys |
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7 | |
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8 | |
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9 | #apps = [ 'histogram', 'mandel', 'filter', 'radix', 'fft_ga' ] |
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10 | apps = [ 'histogram', 'mandel', 'filter', 'radix', 'radix_ga', 'fft', 'fft_ga', 'filt_ga', 'kmeans', 'pca', 'lu' ] |
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11 | #apps = [ 'fft' ] |
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12 | nb_procs = [ 1, 4, 8, 16, 32, 64, 128, 256 ] |
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13 | single_protocols = ['dhccp'] |
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14 | #joint_protocols = ['dhccp', 'rwt'] |
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15 | joint_protocols = [] |
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16 | |
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17 | top_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..") |
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18 | scripts_path = os.path.join(top_path, 'scripts') |
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19 | counter_defs_name = os.path.join(scripts_path, "counter_defs.py") |
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20 | |
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21 | exec(file(counter_defs_name)) |
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22 | |
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23 | gen_dir = 'generated' |
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24 | graph_dir = 'graph' |
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25 | template_dir = 'templates' |
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26 | data_dir = 'data' |
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27 | |
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28 | log_stdo_name = '_stdo_' |
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29 | log_term_name = '_term_' |
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30 | |
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31 | coherence_tmpl = os.path.join(scripts_path, template_dir, 'coherence_template.gp') # 1 graph per appli |
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32 | speedup_tmpl = os.path.join(scripts_path, template_dir, 'speedup_template.gp') |
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33 | metric_tmpl = os.path.join(scripts_path, template_dir, 'metric_template.gp') # 1 graph per metric |
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34 | stacked_tmpl = os.path.join(scripts_path, template_dir, 'stacked_template.gp') |
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35 | |
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36 | |
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37 | |
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38 | def create_file(name, content): |
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39 | file = open(name, 'w') |
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40 | file.write(content) |
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41 | file.close() |
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42 | |
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43 | def is_numeric(s): |
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44 | try: |
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45 | float(s) |
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46 | return True |
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47 | except ValueError: |
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48 | return False |
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49 | |
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50 | def get_x_y(nb_procs): |
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51 | x = 1 |
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52 | y = 1 |
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53 | to_x = True |
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54 | while (x * y * 4 < nb_procs): |
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55 | if to_x: |
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56 | x = x * 2 |
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57 | else: |
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58 | y = y * 2 |
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59 | to_x = not to_x |
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60 | return x, y |
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61 | |
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62 | |
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63 | |
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64 | # We first fill the m_metric_id table |
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65 | for metric in all_metrics: |
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66 | for tag in all_tags: |
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67 | if m_metric_tag[metric] == tag: |
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68 | m_metric_id[tag] = metric |
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69 | break |
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70 | |
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71 | |
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72 | # We start by processing all the log files |
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73 | # Term files are processed for exec time only |
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74 | # Init files are processed for all metrics |
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75 | exec_time = {} |
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76 | metrics_val = {} |
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77 | for prot in single_protocols: |
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78 | metrics_val[prot] = {} |
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79 | exec_time[prot] = {} |
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80 | for app in apps: |
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81 | exec_time[prot][app] = {} |
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82 | metrics_val[prot][app] = {} |
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83 | for i in nb_procs: |
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84 | metrics_val[prot][app][i] = {} |
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85 | log_stdo_file = os.path.join(scripts_path, data_dir, app + '_' + prot + log_stdo_name + str(i)) |
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86 | log_term_file = os.path.join(scripts_path, data_dir, app + '_' + prot + log_term_name + str(i)) |
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87 | |
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88 | # Term |
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89 | lines = open(log_term_file, 'r') |
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90 | for line in lines: |
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91 | tokens = line[:-1].split() |
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92 | if len(tokens) > 0 and tokens[0] == "[PARALLEL_COMPUTE]": |
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93 | exec_time[prot][app][i] = int(tokens[len(tokens) - 1]) |
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94 | |
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95 | # Init files |
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96 | lines = open(log_stdo_file, 'r') |
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97 | for line in lines: |
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98 | tokens = line[:-1].split() |
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99 | if len(tokens) == 0: |
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100 | continue |
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101 | tag = tokens[0] |
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102 | value = tokens[len(tokens) - 1] |
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103 | pattern = re.compile('\[0[0-9][0-9]\]') |
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104 | if pattern.match(tag): |
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105 | metric = m_metric_id[tag] |
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106 | if (not metrics_val[prot][app][i].has_key(metric) or tag == "[000]" or tag == "[001]"): |
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107 | # We don't add cycles of all Memcaches (they must be the same for all) |
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108 | metrics_val[prot][app][i][metric] = int(value) |
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109 | else: |
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110 | metrics_val[prot][app][i][metric] += int(value) |
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111 | |
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112 | # Completing unset metrics (i.e. they are not present in the data file) with 0 |
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113 | for prot in single_protocols: |
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114 | for app in apps: |
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115 | for i in nb_procs: |
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116 | for metric in all_metrics: |
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117 | if metric not in metrics_val[prot][app][i]: |
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118 | metrics_val[prot][app][i][metric] = 0 |
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119 | |
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120 | # We make a 2nd pass to fill the derived fields, e.g. nb_total_updates |
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121 | for prot in single_protocols: |
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122 | for app in apps: |
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123 | for i in nb_procs: |
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124 | x, y = get_x_y(i) |
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125 | metrics_val[prot][app][i]['total_read'] = metrics_val[prot][app][i]['local_read'] + metrics_val[prot][app][i]['remote_read'] |
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126 | metrics_val[prot][app][i]['total_write'] = metrics_val[prot][app][i]['local_write'] + metrics_val[prot][app][i]['remote_write'] |
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127 | metrics_val[prot][app][i]['total_ll'] = metrics_val[prot][app][i]['local_ll'] + metrics_val[prot][app][i]['remote_ll'] |
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128 | metrics_val[prot][app][i]['total_sc'] = metrics_val[prot][app][i]['local_sc'] + metrics_val[prot][app][i]['remote_sc'] |
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129 | metrics_val[prot][app][i]['total_cas'] = metrics_val[prot][app][i]['local_cas'] + metrics_val[prot][app][i]['remote_cas'] |
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130 | metrics_val[prot][app][i]['total_update'] = metrics_val[prot][app][i]['local_update'] + metrics_val[prot][app][i]['remote_update'] |
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131 | metrics_val[prot][app][i]['total_m_inv'] = metrics_val[prot][app][i]['local_m_inv'] + metrics_val[prot][app][i]['remote_m_inv'] |
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132 | metrics_val[prot][app][i]['total_cleanup'] = metrics_val[prot][app][i]['local_cleanup'] + metrics_val[prot][app][i]['remote_cleanup'] |
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133 | metrics_val[prot][app][i]['total_direct'] = metrics_val[prot][app][i]['total_read'] + metrics_val[prot][app][i]['total_write'] |
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134 | metrics_val[prot][app][i]['total_ncc_to_cc'] = metrics_val[prot][app][i]['ncc_to_cc_read'] + metrics_val[prot][app][i]['ncc_to_cc_write'] |
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135 | metrics_val[prot][app][i]['direct_cost'] = metrics_val[prot][app][i]['read_cost'] + metrics_val[prot][app][i]['write_cost'] |
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136 | metrics_val[prot][app][i]['broadcast_cost'] = metrics_val[prot][app][i]['broadcast'] * (x * y - 1) |
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137 | if metrics_val[prot][app][i]['broadcast'] < metrics_val[prot][app][i]['write_broadcast']: |
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138 | # test to patch a bug in mem_cache |
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139 | metrics_val[prot][app][i]['nonwrite_broadcast'] = 0 |
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140 | else: |
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141 | metrics_val[prot][app][i]['nonwrite_broadcast'] = metrics_val[prot][app][i]['broadcast'] - metrics_val[prot][app][i]['write_broadcast'] |
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142 | |
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143 | metrics_val[prot][app][i]['total_stacked'] = 0 |
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144 | for stacked_metric in stacked_metrics: |
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145 | metrics_val[prot][app][i]['total_stacked'] += metrics_val[prot][app][i][stacked_metric] |
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146 | |
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147 | |
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148 | print "mkdir -p", os.path.join(scripts_path, gen_dir) |
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149 | subprocess.call([ 'mkdir', '-p', os.path.join(scripts_path, gen_dir) ]) |
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150 | |
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151 | print "mkdir -p", os.path.join(scripts_path, graph_dir) |
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152 | subprocess.call([ 'mkdir', '-p', os.path.join(scripts_path, graph_dir) ]) |
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153 | |
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154 | ############################################################ |
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155 | ### Graph 1 : Coherence traffic Cost per application ### |
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156 | ############################################################ |
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157 | |
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158 | for prot in single_protocols: |
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159 | for app in apps: |
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160 | data_coherence_name = os.path.join(scripts_path, gen_dir, prot + '_' + app + '_coherence.dat') |
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161 | gp_coherence_name = os.path.join(scripts_path, gen_dir, prot + '_' + app + '_coherence.gp') |
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162 | |
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163 | # Creating the data file |
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164 | width = 15 |
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165 | content = "" |
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166 | |
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167 | for metric in [ '#nb_procs' ] + grouped_metrics: |
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168 | content += metric + " " |
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169 | nb_spaces = width - len(metric) |
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170 | content += nb_spaces * ' ' |
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171 | content += "\n" |
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172 | |
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173 | for i in nb_procs: |
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174 | content += "%-15d " % i |
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175 | for metric in grouped_metrics: |
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176 | val = float(metrics_val[prot][app][i][metric]) / exec_time[prot][app][i] * 1000 |
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177 | content += "%-15f " % val |
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178 | content += "\n" |
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179 | |
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180 | create_file(data_coherence_name, content) |
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181 | |
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182 | # Creating the gp file |
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183 | template_file = open(coherence_tmpl, 'r') |
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184 | template = template_file.read() |
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185 | |
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186 | plot_str = "" |
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187 | col = 2 |
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188 | for metric in grouped_metrics: |
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189 | if metric != grouped_metrics[0]: |
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190 | plot_str += ", \\\n " |
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191 | plot_str += "\"" + data_coherence_name + "\" using ($1):($" + str(col) + ") lc rgb " + colors[col - 2] + " title \"" + m_metric_name[metric] + "\" with linespoint" |
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192 | col += 1 |
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193 | gp_commands = template % dict(app_name = m_app_name[app], nb_procs = nb_procs[-1] + 1, plot_str = plot_str, svg_name = os.path.join(graph_dir, prot + '_' + app + '_coherence')) |
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194 | |
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195 | create_file(gp_coherence_name, gp_commands) |
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196 | |
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197 | # Calling gnuplot |
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198 | print "gnuplot", gp_coherence_name |
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199 | subprocess.call([ 'gnuplot', gp_coherence_name ]) |
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200 | |
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201 | |
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202 | ############################################################ |
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203 | ### Graph 2 : Speedup per Application ### |
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204 | ############################################################ |
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205 | |
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206 | for prot in single_protocols: |
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207 | for app in apps: |
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208 | |
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209 | data_speedup_name = os.path.join(scripts_path, gen_dir, prot + '_' + app + '_speedup.dat') |
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210 | gp_speedup_name = os.path.join(scripts_path, gen_dir, prot + '_' + app + '_speedup.gp') |
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211 | |
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212 | # Creating data file |
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213 | width = 15 |
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214 | content = "#nb_procs" |
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215 | nb_spaces = width - len(content) |
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216 | content += nb_spaces * ' ' |
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217 | content += "speedup\n" |
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218 | |
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219 | for i in nb_procs: |
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220 | content += "%-15d " % i |
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221 | val = exec_time[prot][app][i] |
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222 | content += "%-15f\n" % (exec_time[prot][app][1] / float(val)) |
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223 | |
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224 | plot_str = "\"" + data_speedup_name + "\" using ($1):($2) lc rgb \"#654387\" title \"Speedup\" with linespoint" |
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225 | |
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226 | create_file(data_speedup_name, content) |
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227 | |
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228 | # Creating the gp file |
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229 | template_file = open(speedup_tmpl, 'r') |
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230 | template = template_file.read() |
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231 | |
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232 | gp_commands = template % dict(appli = m_app_name[app], nb_procs = nb_procs[-1] + 1, plot_str = plot_str, svg_name = os.path.join(graph_dir, prot + '_' + app + '_speedup')) |
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233 | |
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234 | create_file(gp_speedup_name, gp_commands) |
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235 | |
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236 | # Calling gnuplot |
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237 | print "gnuplot", gp_speedup_name |
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238 | subprocess.call([ 'gnuplot', gp_speedup_name ]) |
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239 | |
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240 | |
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241 | ############################################################ |
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242 | ### Graph 3 : All speedups on the same Graph ### |
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243 | ############################################################ |
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244 | |
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245 | for prot in single_protocols: |
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246 | # This graph uses the same template as the graph 2 |
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247 | data_speedup_name = os.path.join(scripts_path, gen_dir, prot + '_all_speedup.dat') |
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248 | gp_speedup_name = os.path.join(scripts_path, gen_dir, prot + '_all_speedup.gp') |
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249 | |
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250 | # Creating data file |
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251 | width = 15 |
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252 | content = "#nb_procs" |
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253 | nb_spaces = width - len(content) |
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254 | content += (nb_spaces + 1) * ' ' |
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255 | for app in apps: |
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256 | content += app + " " |
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257 | content += (width - len(app)) * " " |
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258 | content += "\n" |
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259 | |
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260 | for i in nb_procs: |
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261 | content += "%-15d " % i |
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262 | for app in apps: |
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263 | val = exec_time[prot][app][i] |
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264 | content += "%-15f " % (exec_time[prot][app][1] / float(val)) |
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265 | content += "\n" |
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266 | |
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267 | create_file(data_speedup_name, content) |
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268 | |
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269 | # Creating gp file |
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270 | template_file = open(speedup_tmpl, 'r') |
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271 | template = template_file.read() |
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272 | |
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273 | plot_str = "" |
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274 | col = 2 |
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275 | for app in apps: |
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276 | if app != apps[0]: |
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277 | plot_str += ", \\\n " |
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278 | plot_str += "\"" + data_speedup_name + "\" using ($1):($" + str(col) + ") lc rgb %s title \"" % (colors[col - 2]) + m_app_name[app] + "\" with linespoint" |
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279 | col += 1 |
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280 | |
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281 | gp_commands = template % dict(appli = "All Applications", nb_procs = nb_procs[-1] + 1, plot_str = plot_str, svg_name = os.path.join(graph_dir, prot + '_all_speedup')) |
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282 | |
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283 | create_file(gp_speedup_name, gp_commands) |
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284 | |
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285 | # Calling gnuplot |
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286 | print "gnuplot", gp_speedup_name |
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287 | subprocess.call([ 'gnuplot', gp_speedup_name ]) |
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288 | |
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289 | |
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290 | ############################################################ |
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291 | ### Graph 4 : Graph per metric ### |
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292 | ############################################################ |
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293 | |
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294 | # The following section creates the graphs grouped by measure (e.g. #broadcasts) |
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295 | # The template file cannot be easily created otherwise it would not be generic |
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296 | # in many ways. This is why it is mainly created here. |
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297 | # Graphs are created for metric in the "individual_metrics" list |
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298 | |
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299 | for prot in single_protocols: |
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300 | for metric in individual_metrics: |
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301 | data_metric_name = os.path.join(scripts_path, gen_dir, prot + '_' + metric + '.dat') |
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302 | gp_metric_name = os.path.join(scripts_path, gen_dir, prot + '_' + metric + '.gp') |
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303 | |
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304 | # Creating the gp file |
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305 | # Setting xtics, i.e. number of procs for each application |
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306 | xtics_str = "(" |
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307 | first = True |
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308 | xpos = 1 |
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309 | app_labels = "" |
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310 | for num_appli in range(0, len(apps)): |
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311 | for i in nb_procs: |
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312 | if not first: |
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313 | xtics_str += ", " |
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314 | first = False |
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315 | if i == nb_procs[0]: |
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316 | xpos_first = xpos |
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317 | xtics_str += "\"%d\" %.1f" % (i, xpos) |
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318 | xpos_last = xpos |
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319 | xpos += 1.5 |
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320 | xpos += 0.5 |
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321 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
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322 | app_labels += "set label \"%s\" at first %f,character 1 center font \"Times,12\"\n" % (m_app_name[apps[num_appli]], app_name_xpos) |
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323 | xtics_str += ")" |
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324 | |
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325 | xmax_val = float(xpos - 1) |
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326 | |
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327 | # Writing the lines of "plot" |
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328 | plot_str = "" |
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329 | xpos = 0 |
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330 | first = True |
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331 | column = 2 |
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332 | for i in range(0, len(nb_procs)): |
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333 | if not first: |
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334 | plot_str += ", \\\n " |
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335 | first = False |
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336 | plot_str += "\"%s\" using ($1+%.1f):($%d) lc rgb %s notitle with boxes" % (data_metric_name, xpos, column, colors[i]) |
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337 | column += 1 |
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338 | xpos += 1.5 |
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339 | |
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340 | template_file = open(metric_tmpl, 'r') |
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341 | template = template_file.read() |
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342 | |
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343 | gp_commands = template % dict(xtics_str = xtics_str, app_labels = app_labels, ylabel_str = m_metric_name[metric], norm_factor_str = m_norm_factor_name[m_metric_norm[metric]], xmax_val = xmax_val, plot_str = plot_str, svg_name = os.path.join(graph_dir, prot + '_' + metric)) |
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344 | |
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345 | create_file(gp_metric_name, gp_commands) |
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346 | |
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347 | # Creating the data file |
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348 | width = 15 |
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349 | content = "#x_pos" |
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350 | nb_spaces = width - len(content) |
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351 | content += nb_spaces * ' ' |
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352 | for i in nb_procs: |
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353 | content += "%-15d" % i |
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354 | content += "\n" |
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355 | |
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356 | x_pos = 1 |
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357 | for app in apps: |
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358 | # Computation of x_pos |
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359 | content += "%-15f" % x_pos |
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360 | x_pos += len(nb_procs) * 1.5 + 0.5 |
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361 | for i in nb_procs: |
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362 | if m_metric_norm[metric] == "N": |
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363 | content += "%-15d" % (metrics_val[prot][app][i][metric]) |
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364 | elif m_metric_norm[metric] == "P": |
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365 | content += "%-15f" % (float(metrics_val[prot][app][i][metric]) / i) |
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366 | elif m_metric_norm[metric] == "C": |
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367 | content += "%-15f" % (float(metrics_val[prot][app][i][metric]) / exec_time[prot][app][i] * 1000) |
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368 | elif m_metric_norm[metric] == "W": |
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369 | content += "%-15f" % (float(metrics_val[prot][app][i][metric]) / float(metrics_val[prot][app][i]['total_write'])) # Number of writes |
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370 | elif m_metric_norm[metric] == "R": |
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371 | content += "%-15f" % (float(metrics_val[prot][app][i][metric]) / float(metrics_val[prot][app][i]['total_read'])) # Number of reads |
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372 | elif m_metric_norm[metric] == "D": |
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373 | content += "%-15f" % (float(metrics_val[prot][app][i][metric]) / float(metrics_val[prot][app][i]['total_direct'])) # Number of req. |
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374 | elif is_numeric(m_metric_norm[metric]): |
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375 | content += "%-15f" % (float(metrics_val[prot][app][i][metric]) / float(metrics_val[prot][app][int(m_metric_norm[metric])][metric])) |
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376 | else: |
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377 | assert(False) |
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378 | |
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379 | app_name = m_app_name[app] |
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380 | content += "#" + app_name + "\n" |
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381 | |
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382 | create_file(data_metric_name, content) |
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383 | |
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384 | # Calling gnuplot |
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385 | print "gnuplot", gp_metric_name |
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386 | subprocess.call([ 'gnuplot', gp_metric_name ]) |
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387 | |
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388 | |
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389 | ############################################################ |
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390 | ### Graph 5 : Stacked histogram with counters ### |
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391 | ############################################################ |
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392 | |
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393 | # The following section creates a stacked histogram containing |
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394 | # the metrics in the "stacked_metric" list |
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395 | # It is normalized per application w.r.t the values on 256 procs |
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396 | |
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397 | for prot in single_protocols: |
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398 | data_stacked_name = os.path.join(scripts_path, gen_dir, prot + '_stacked.dat') |
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399 | gp_stacked_name = os.path.join(scripts_path, gen_dir, prot + '_stacked.gp') |
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400 | |
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401 | norm_factor_value = 256 |
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402 | |
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403 | # Creating the gp file |
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404 | template_file = open(stacked_tmpl, 'r') |
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405 | template = template_file.read() |
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406 | |
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407 | xtics_str = "(" |
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408 | first = True |
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409 | xpos = 1 |
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410 | app_labels = "" |
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411 | for num_appli in range(0, len(apps)): |
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412 | for i in nb_procs: |
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413 | if not first: |
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414 | xtics_str += ", " |
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415 | first = False |
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416 | if i == nb_procs[0]: |
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417 | xpos_first = xpos |
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418 | xtics_str += "\"%d\" %d -1" % (i, xpos) |
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419 | xpos_last = xpos |
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420 | xpos += 1 |
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421 | xpos += 1 |
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422 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
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423 | app_labels += "set label \"%s\" at first %f,character 1 center font \"Times,12\"\n" % (m_app_name[apps[num_appli]], app_name_xpos) |
---|
424 | xtics_str += ")" |
---|
425 | |
---|
426 | plot_str = "newhistogram \"\"" |
---|
427 | n = 1 |
---|
428 | for stacked_metric in stacked_metrics: |
---|
429 | plot_str += ", \\\n " + "'" + data_stacked_name + "'" + " using " + str(n) + " lc rgb " + colors[n] + " title \"" + m_metric_name[stacked_metric] + "\"" |
---|
430 | n += 1 |
---|
431 | |
---|
432 | ylabel_str = "Breakdown of Coherence Traffic Normalized w.r.t. \\nthe Values on %d Processors" % norm_factor_value |
---|
433 | content = template % dict(svg_name = os.path.join(graph_dir, prot + '_stacked'), xtics_str = xtics_str, plot_str = plot_str, ylabel_str = ylabel_str, app_labels = app_labels, prot_labels = "") |
---|
434 | |
---|
435 | create_file(gp_stacked_name, content) |
---|
436 | |
---|
437 | # Creating the data file |
---|
438 | # Values are normalized by application, w.r.t. the number of requests for a given number of procs |
---|
439 | content = "#" |
---|
440 | for stacked_metric in stacked_metrics: |
---|
441 | content += stacked_metric |
---|
442 | content += ' ' + ' ' * (15 - len(stacked_metric)) |
---|
443 | content += "\n" |
---|
444 | for app in apps: |
---|
445 | if app != apps[0]: |
---|
446 | for i in range(0, len(stacked_metrics)): |
---|
447 | content += "%-15f" % 0.0 |
---|
448 | content += "\n" |
---|
449 | for i in nb_procs: |
---|
450 | for stacked_metric in stacked_metrics: |
---|
451 | content += "%-15f" % (float(metrics_val[prot][app][i][stacked_metric]) / metrics_val[prot][app][norm_factor_value]['total_stacked']) |
---|
452 | content += "\n" |
---|
453 | |
---|
454 | create_file(data_stacked_name, content) |
---|
455 | # Calling gnuplot |
---|
456 | print "gnuplot", gp_stacked_name |
---|
457 | subprocess.call([ 'gnuplot', gp_stacked_name ]) |
---|
458 | |
---|
459 | |
---|
460 | |
---|
461 | ################################################################################# |
---|
462 | ### Graph 6 : Stacked histogram with coherence cost compared to r/w cost ### |
---|
463 | ################################################################################# |
---|
464 | |
---|
465 | # The following section creates pairs of stacked histograms, normalized w.r.t. the first one. |
---|
466 | # The first one contains the cost of reads and writes, the second contains the cost |
---|
467 | # of m_inv, m_up and broadcasts (extrapolated) |
---|
468 | |
---|
469 | for prot in single_protocols: |
---|
470 | data_cost_filename = os.path.join(scripts_path, gen_dir, prot + '_relative_cost.dat') |
---|
471 | gp_cost_filename = os.path.join(scripts_path, gen_dir, prot + '_relative_cost.gp') |
---|
472 | |
---|
473 | direct_cost_metrics = [ 'read_cost', 'write_cost' ] |
---|
474 | coherence_cost_metrics = ['update_cost', 'm_inv_cost', 'broadcast_cost' ] |
---|
475 | |
---|
476 | # Creating the gp file |
---|
477 | template_file = open(stacked_tmpl, 'r') |
---|
478 | template = template_file.read() |
---|
479 | |
---|
480 | xtics_str = "(" |
---|
481 | first = True |
---|
482 | xpos = 1 |
---|
483 | app_labels = "" |
---|
484 | for num_appli in range(0, len(apps)): |
---|
485 | first_proc = True |
---|
486 | for i in nb_procs: |
---|
487 | if i > 4: |
---|
488 | if not first: |
---|
489 | xtics_str += ", " |
---|
490 | first = False |
---|
491 | if first_proc: |
---|
492 | first_proc = False |
---|
493 | xpos_first = xpos |
---|
494 | xtics_str += "\"%d\" %f -1" % (i, float(xpos + 0.5)) |
---|
495 | xpos_last = xpos |
---|
496 | xpos += 3 |
---|
497 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
---|
498 | app_labels += "set label \"%s\" at first %f,character 1 center font \"Times,12\"\n" % (m_app_name[apps[num_appli]], app_name_xpos) |
---|
499 | #xpos += 1 |
---|
500 | xtics_str += ")" |
---|
501 | |
---|
502 | plot_str = "newhistogram \"\"" |
---|
503 | n = 1 |
---|
504 | for cost_metric in direct_cost_metrics + coherence_cost_metrics: |
---|
505 | plot_str += ", \\\n " + "'" + data_cost_filename + "'" + " using " + str(n) + " lc rgb " + colors[n] + " title \"" + m_metric_name[cost_metric] + "\"" |
---|
506 | n += 1 |
---|
507 | |
---|
508 | ylabel_str = "Coherence Cost Compared to Direct Requests Cost,\\nNormalized per Application for each Number of Processors" |
---|
509 | content = template % dict(svg_name = os.path.join(graph_dir, prot + '_rel_cost'), xtics_str = xtics_str, plot_str = plot_str, ylabel_str = ylabel_str, app_labels = app_labels, prot_labels = "") |
---|
510 | |
---|
511 | create_file(gp_cost_filename, content) |
---|
512 | |
---|
513 | # Creating the data file |
---|
514 | # Values are normalized by application, w.r.t. the number of requests for a given number of procs |
---|
515 | content = "#" |
---|
516 | for cost_metric in direct_cost_metrics: |
---|
517 | content += cost_metric |
---|
518 | content += ' ' + ' ' * (15 - len(cost_metric)) |
---|
519 | for cost_metric in coherence_cost_metrics: |
---|
520 | content += cost_metric |
---|
521 | content += ' ' + ' ' * (15 - len(cost_metric)) |
---|
522 | content += "\n" |
---|
523 | for app in apps: |
---|
524 | if app != apps[0]: |
---|
525 | for i in range(0, len(direct_cost_metrics) + len(coherence_cost_metrics)): |
---|
526 | content += "%-15f" % 0.0 |
---|
527 | content += "\n" |
---|
528 | for i in nb_procs: |
---|
529 | if i > 4: |
---|
530 | for cost_metric in direct_cost_metrics: |
---|
531 | content += "%-15f" % (float(metrics_val[prot][app][i][cost_metric]) / metrics_val[prot][app][i]['direct_cost']) |
---|
532 | for cost_metric in coherence_cost_metrics: |
---|
533 | content += "%-15f" % 0.0 |
---|
534 | content += "\n" |
---|
535 | for cost_metric in direct_cost_metrics: |
---|
536 | content += "%-15f" % 0.0 |
---|
537 | for cost_metric in coherence_cost_metrics: |
---|
538 | content += "%-15f" % (float(metrics_val[prot][app][i][cost_metric]) / metrics_val[prot][app][i]['direct_cost']) |
---|
539 | content += "\n" |
---|
540 | if i != nb_procs[-1]: |
---|
541 | for j in range(0, len(direct_cost_metrics) + len(coherence_cost_metrics)): |
---|
542 | content += "%-15f" % 0.0 |
---|
543 | content += "\n" |
---|
544 | |
---|
545 | create_file(data_cost_filename, content) |
---|
546 | # Calling gnuplot |
---|
547 | print "gnuplot", gp_cost_filename |
---|
548 | subprocess.call([ 'gnuplot', gp_cost_filename ]) |
---|
549 | |
---|
550 | |
---|
551 | ################################################################################# |
---|
552 | ### Joint Graphs to several architectures ### |
---|
553 | ################################################################################# |
---|
554 | |
---|
555 | if len(joint_protocols) == 0: |
---|
556 | sys.exit() |
---|
557 | |
---|
558 | ################################################################################# |
---|
559 | ### Graph 7: Comparison of Speedups (normalized w.r.t. 1 proc on first arch) ### |
---|
560 | ################################################################################# |
---|
561 | |
---|
562 | |
---|
563 | for app in apps: |
---|
564 | |
---|
565 | data_speedup_name = os.path.join(scripts_path, gen_dir, 'joint_' + app + '_speedup.dat') |
---|
566 | gp_speedup_name = os.path.join(scripts_path, gen_dir, 'joint_' + app + '_speedup.gp') |
---|
567 | |
---|
568 | # Creating data file |
---|
569 | width = 15 |
---|
570 | content = "#nb_procs" |
---|
571 | nb_spaces = width - len(content) |
---|
572 | content += nb_spaces * ' ' |
---|
573 | content += "speedup\n" |
---|
574 | |
---|
575 | for i in nb_procs: |
---|
576 | content += "%-15d " % i |
---|
577 | for prot in joint_protocols: |
---|
578 | val = exec_time[prot][app][i] |
---|
579 | content += "%-15f " % (exec_time[joint_protocols[0]][app][1] / float(val)) |
---|
580 | content += "\n" |
---|
581 | |
---|
582 | create_file(data_speedup_name, content) |
---|
583 | |
---|
584 | # Creating the gp file |
---|
585 | template_file = open(speedup_tmpl, 'r') |
---|
586 | template = template_file.read() |
---|
587 | |
---|
588 | plot_str = "" |
---|
589 | col = 2 |
---|
590 | for prot in joint_protocols: |
---|
591 | if prot != joint_protocols[0]: |
---|
592 | plot_str += ", \\\n " |
---|
593 | plot_str += "\"" + data_speedup_name + "\" using ($1):($" + str(col) + ") lc rgb %s title \"" % (colors[col - 2]) + m_prot_name[prot] + "\" with linespoint" |
---|
594 | col += 1 |
---|
595 | |
---|
596 | gp_commands = template % dict(appli = m_app_name[app] + " Normalized w.r.t. " + m_prot_name[joint_protocols[0]] + " on 1 Processor", nb_procs = nb_procs[-1] + 1, plot_str = plot_str, svg_name = os.path.join(graph_dir, 'joint_' + app + '_speedup')) |
---|
597 | |
---|
598 | create_file(gp_speedup_name, gp_commands) |
---|
599 | |
---|
600 | # Calling gnuplot |
---|
601 | print "gnuplot", gp_speedup_name |
---|
602 | subprocess.call([ 'gnuplot', gp_speedup_name ]) |
---|
603 | |
---|
604 | |
---|
605 | ################################################################################# |
---|
606 | ### Graph 8 : Joint Stacked histogram with coherence cost and r/w cost ### |
---|
607 | ################################################################################# |
---|
608 | |
---|
609 | # The following section creates pairs of stacked histograms for each arch for each number of proc for each app, normalized by (app x number of procs) (with first arch, R/W cost, first of the 2*num_arch histo). It is close to Graph 6 |
---|
610 | |
---|
611 | data_cost_filename = os.path.join(scripts_path, gen_dir, 'joint_relative_cost.dat') |
---|
612 | gp_cost_filename = os.path.join(scripts_path, gen_dir, 'joint_relative_cost.gp') |
---|
613 | |
---|
614 | direct_cost_metrics = [ 'read_cost', 'write_cost' ] |
---|
615 | coherence_cost_metrics = ['update_cost', 'm_inv_cost', 'broadcast_cost' ] |
---|
616 | |
---|
617 | # Creating the gp file |
---|
618 | template_file = open(stacked_tmpl, 'r') |
---|
619 | template = template_file.read() |
---|
620 | |
---|
621 | xtics_str = "(" |
---|
622 | first = True |
---|
623 | xpos = 1 # successive x position of the center of the first bar in a application |
---|
624 | app_labels = "" |
---|
625 | prot_labels = "" |
---|
626 | for num_appli in range(0, len(apps)): |
---|
627 | first_proc = True |
---|
628 | for i in nb_procs: |
---|
629 | if i > 4: |
---|
630 | x = 0 # local var for computing position of protocol names |
---|
631 | for prot in joint_protocols: |
---|
632 | prot_labels += "set label \"%s\" at first %f, character 2 center font \"Times,10\"\n" % (m_prot_name[prot], float((xpos - 0.5)) + x) # -0.5 instead of +0.5, don't know why... (bug gnuplot?) |
---|
633 | x += 2 |
---|
634 | |
---|
635 | if not first: |
---|
636 | xtics_str += ", " |
---|
637 | first = False |
---|
638 | if first_proc: |
---|
639 | first_proc = False |
---|
640 | xpos_first = xpos |
---|
641 | xtics_str += "\"%d\" %f -1" % (i, float(xpos - 0.5 + len(joint_protocols))) |
---|
642 | xpos_last = xpos |
---|
643 | xpos += 1 + len(joint_protocols) * 2 |
---|
644 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
---|
645 | app_labels += "set label \"%s\" at first %f,character 1 center font \"Times,12\"\n" % (m_app_name[apps[num_appli]], app_name_xpos) |
---|
646 | xpos += 1 |
---|
647 | xtics_str += ")" |
---|
648 | |
---|
649 | plot_str = "newhistogram \"\"" |
---|
650 | n = 1 |
---|
651 | for cost_metric in direct_cost_metrics + coherence_cost_metrics: |
---|
652 | plot_str += ", \\\n " + "'" + data_cost_filename + "'" + " using " + str(n) + " lc rgb " + colors[n] + " title \"" + m_metric_name[cost_metric] + "\"" |
---|
653 | n += 1 |
---|
654 | |
---|
655 | ylabel_str = "Coherence Cost and Direct Requests Cost,\\nNormalized per Application for each Number of Processors" |
---|
656 | content = template % dict(svg_name = os.path.join(graph_dir, 'joint_rel_cost'), xtics_str = xtics_str, plot_str = plot_str, ylabel_str = ylabel_str, app_labels = app_labels, prot_labels = prot_labels) |
---|
657 | |
---|
658 | create_file(gp_cost_filename, content) |
---|
659 | |
---|
660 | # Creating the data file |
---|
661 | # Values are normalized by application, w.r.t. the number of requests for a given number of procs |
---|
662 | content = "#" |
---|
663 | for cost_metric in direct_cost_metrics: |
---|
664 | content += cost_metric |
---|
665 | content += ' ' + ' ' * (15 - len(cost_metric)) |
---|
666 | for cost_metric in coherence_cost_metrics: |
---|
667 | content += cost_metric |
---|
668 | content += ' ' + ' ' * (15 - len(cost_metric)) |
---|
669 | content += "\n" |
---|
670 | for app in apps: |
---|
671 | if app != apps[0]: |
---|
672 | for j in range(0, len(direct_cost_metrics) + len(coherence_cost_metrics)): |
---|
673 | content += "%-15f" % 0.0 |
---|
674 | content += "\n" |
---|
675 | for i in nb_procs: |
---|
676 | if i > 4: |
---|
677 | for prot in joint_protocols: |
---|
678 | for cost_metric in direct_cost_metrics: |
---|
679 | content += "%-15f" % (float(metrics_val[prot][app][i][cost_metric]) / metrics_val[joint_protocols[0]][app][i]['direct_cost']) |
---|
680 | for cost_metric in coherence_cost_metrics: |
---|
681 | content += "%-15f" % 0.0 |
---|
682 | content += "\n" |
---|
683 | for cost_metric in direct_cost_metrics: |
---|
684 | content += "%-15f" % 0.0 |
---|
685 | for cost_metric in coherence_cost_metrics: |
---|
686 | content += "%-15f" % (float(metrics_val[prot][app][i][cost_metric]) / metrics_val[joint_protocols[0]][app][i]['direct_cost']) |
---|
687 | content += "\n" |
---|
688 | if i != nb_procs[-1]: |
---|
689 | for j in range(0, len(direct_cost_metrics) + len(coherence_cost_metrics)): |
---|
690 | content += "%-15f" % 0.0 |
---|
691 | content += "\n" |
---|
692 | |
---|
693 | create_file(data_cost_filename, content) |
---|
694 | # Calling gnuplot |
---|
695 | print "gnuplot", gp_cost_filename |
---|
696 | subprocess.call([ 'gnuplot', gp_cost_filename ]) |
---|
697 | |
---|
698 | |
---|
699 | |
---|
700 | |
---|
701 | |
---|