| 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 | |
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| 7 | |
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| 8 | |
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| 9 | apps = [ 'histogram', 'mandel', 'filter', 'radix', 'radix_ga', 'fft', 'fft_ga', 'filt_ga', 'kmeans', 'pca', 'lu' ] |
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| 10 | nb_procs = [ 1, 4, 8, 16, 32, 64, 128, 256 ] |
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| 11 | |
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| 12 | top_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..") |
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| 13 | scripts_path = os.path.join(top_path, 'scripts') |
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| 14 | counter_defs_name = os.path.join(scripts_path, "counter_defs.py") |
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| 15 | |
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| 16 | exec(file(counter_defs_name)) |
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| 17 | |
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| 18 | gen_dir = 'generated' |
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| 19 | graph_dir = 'graph' |
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| 20 | template_dir = 'templates' |
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| 21 | data_dir = 'data' |
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| 22 | |
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| 23 | log_init_name = 'log_init_' |
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| 24 | log_term_name = 'log_term_' |
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| 25 | |
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| 26 | coherence_tmpl = os.path.join(scripts_path, template_dir, 'coherence_template.gp') # 1 graph per appli |
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| 27 | speedup_tmpl = os.path.join(scripts_path, template_dir, 'speedup_template.gp') |
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| 28 | metric_tmpl = os.path.join(scripts_path, template_dir, 'metric_template.gp') # 1 graph per metric |
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| 29 | stacked_tmpl = os.path.join(scripts_path, template_dir, 'stacked_template.gp') |
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| 30 | |
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| 31 | |
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| 32 | |
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| 33 | def create_file(name, content): |
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| 34 | file = open(name, 'w') |
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| 35 | file.write(content) |
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| 36 | file.close() |
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| 37 | |
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| 38 | def is_numeric(s): |
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| 39 | try: |
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| 40 | float(s) |
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| 41 | return True |
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| 42 | except ValueError: |
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| 43 | return False |
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| 44 | |
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| 45 | def get_x_y(nb_procs): |
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| 46 | x = 1 |
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| 47 | y = 1 |
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| 48 | to_x = True |
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| 49 | while (x * y * 4 < nb_procs): |
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| 50 | if to_x: |
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| 51 | x = x * 2 |
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| 52 | else: |
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| 53 | y = y * 2 |
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| 54 | to_x = not to_x |
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| 55 | return x, y |
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| 56 | |
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| 57 | |
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| 58 | |
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| 59 | # We first fill the m_metric_id table |
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| 60 | for metric in all_metrics: |
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| 61 | for tag in all_tags: |
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| 62 | if m_metric_tag[metric] == tag: |
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| 63 | m_metric_id[tag] = metric |
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| 64 | break |
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| 65 | |
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| 66 | |
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| 67 | # We start by processing all the log files |
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| 68 | # Term files are processed for exec time only |
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| 69 | # Init files are processed for all metrics |
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| 70 | exec_time = {} |
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| 71 | metrics_val = {} |
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| 72 | for app in apps: |
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| 73 | exec_time[app] = {} |
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| 74 | metrics_val[app] = {} |
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| 75 | for i in nb_procs: |
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| 76 | metrics_val[app][i] = {} |
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| 77 | log_init_file = os.path.join(scripts_path, data_dir, app + '_' + log_init_name + str(i)) |
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| 78 | log_term_file = os.path.join(scripts_path, data_dir, app + '_' + log_term_name + str(i)) |
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| 79 | |
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| 80 | # Term |
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| 81 | lines = open(log_term_file, 'r') |
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| 82 | for line in lines: |
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| 83 | tokens = line[:-1].split() |
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| 84 | if len(tokens) > 0 and tokens[0] == "[PARALLEL_COMPUTE]": |
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| 85 | exec_time[app][i] = int(tokens[len(tokens) - 1]) |
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| 86 | |
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| 87 | # Init files |
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| 88 | lines = open(log_init_file, 'r') |
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| 89 | for line in lines: |
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| 90 | tokens = line[:-1].split() |
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| 91 | if len(tokens) == 0: |
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| 92 | continue |
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| 93 | tag = tokens[0] |
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| 94 | value = tokens[len(tokens) - 1] |
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| 95 | pattern = re.compile('\[0[0-9][0-9]\]') |
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| 96 | if pattern.match(tag): |
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| 97 | metric = m_metric_id[tag] |
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| 98 | if (not metrics_val[app][i].has_key(metric) or tag == "[000]" or tag == "[001]"): |
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| 99 | # We don't add cycles of all Memcaches (they must be the same for all) |
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| 100 | metrics_val[app][i][metric] = int(value) |
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| 101 | else: |
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| 102 | metrics_val[app][i][metric] += int(value) |
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| 103 | |
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| 104 | # We make a 2nd pass to fill the derived fields, e.g. nb_total_updates |
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| 105 | for app in apps: |
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| 106 | for i in nb_procs: |
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| 107 | x, y = get_x_y(i) |
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| 108 | metrics_val[app][i]['total_read'] = metrics_val[app][i]['local_read'] + metrics_val[app][i]['remote_read'] |
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| 109 | metrics_val[app][i]['total_write'] = metrics_val[app][i]['local_write'] + metrics_val[app][i]['remote_write'] |
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| 110 | metrics_val[app][i]['total_ll'] = metrics_val[app][i]['local_ll'] + metrics_val[app][i]['remote_ll'] |
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| 111 | metrics_val[app][i]['total_sc'] = metrics_val[app][i]['local_sc'] + metrics_val[app][i]['remote_sc'] |
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| 112 | metrics_val[app][i]['total_cas'] = metrics_val[app][i]['local_cas'] + metrics_val[app][i]['remote_cas'] |
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| 113 | metrics_val[app][i]['total_update'] = metrics_val[app][i]['local_update'] + metrics_val[app][i]['remote_update'] |
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| 114 | metrics_val[app][i]['total_m_inv'] = metrics_val[app][i]['local_m_inv'] + metrics_val[app][i]['remote_m_inv'] |
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| 115 | metrics_val[app][i]['total_cleanup'] = metrics_val[app][i]['local_cleanup'] + metrics_val[app][i]['remote_cleanup'] |
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| 116 | metrics_val[app][i]['total_direct'] = metrics_val[app][i]['total_read'] + metrics_val[app][i]['total_write'] |
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| 117 | metrics_val[app][i]['direct_cost'] = metrics_val[app][i]['read_cost'] + metrics_val[app][i]['write_cost'] |
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| 118 | metrics_val[app][i]['broadcast_cost'] = metrics_val[app][i]['broadcast'] * (x * y - 1) |
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| 119 | if metrics_val[app][i]['broadcast'] < metrics_val[app][i]['write_broadcast']: |
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| 120 | # test to patch a bug in mem_cache |
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| 121 | metrics_val[app][i]['nonwrite_broadcast'] = 0 |
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| 122 | else: |
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| 123 | metrics_val[app][i]['nonwrite_broadcast'] = metrics_val[app][i]['broadcast'] - metrics_val[app][i]['write_broadcast'] |
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| 124 | |
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| 125 | metrics_val[app][i]['total_stacked'] = 0 |
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| 126 | for stacked_metric in stacked_metrics: |
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| 127 | metrics_val[app][i]['total_stacked'] += metrics_val[app][i][stacked_metric] |
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| 128 | |
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| 129 | |
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| 130 | print "mkdir -p", os.path.join(scripts_path, gen_dir) |
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| 131 | subprocess.call([ 'mkdir', '-p', os.path.join(scripts_path, gen_dir) ]) |
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| 132 | |
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| 133 | print "mkdir -p", os.path.join(scripts_path, graph_dir) |
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| 134 | subprocess.call([ 'mkdir', '-p', os.path.join(scripts_path, graph_dir) ]) |
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| 135 | |
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| 136 | ############################################################ |
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| 137 | ### Graph 1 : Coherence traffic Cost per application ### |
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| 138 | ############################################################ |
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| 139 | |
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| 140 | for app in apps: |
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| 141 | data_coherence_name = os.path.join(scripts_path, gen_dir, app + '_coherence.dat') |
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| 142 | gp_coherence_name = os.path.join(scripts_path, gen_dir, app + '_coherence.gp') |
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| 143 | |
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| 144 | # Creating the data file |
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| 145 | width = 15 |
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| 146 | content = "" |
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| 147 | |
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| 148 | for metric in [ '#nb_procs' ] + grouped_metrics: |
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| 149 | content += metric + " " |
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| 150 | nb_spaces = width - len(metric) |
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| 151 | content += nb_spaces * ' ' |
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| 152 | content += "\n" |
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| 153 | |
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| 154 | for i in nb_procs: |
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| 155 | content += "%-15d " % i |
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| 156 | for metric in grouped_metrics: |
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| 157 | val = float(metrics_val[app][i][metric]) / exec_time[app][i] * 1000 |
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| 158 | content += "%-15f " % val |
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| 159 | content += "\n" |
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| 160 | |
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| 161 | create_file(data_coherence_name, content) |
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| 162 | |
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| 163 | # Creating the gp file |
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| 164 | template_file = open(coherence_tmpl, 'r') |
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| 165 | template = template_file.read() |
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| 166 | |
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| 167 | plot_str = "" |
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| 168 | col = 2 |
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| 169 | for metric in grouped_metrics: |
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| 170 | if metric != grouped_metrics[0]: |
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| 171 | plot_str += ", \\\n " |
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| 172 | 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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| 173 | col += 1 |
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| 174 | 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, app + '_coherence')) |
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| 175 | |
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| 176 | create_file(gp_coherence_name, gp_commands) |
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| 177 | |
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| 178 | # Calling gnuplot |
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| 179 | print "gnuplot", gp_coherence_name |
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| 180 | subprocess.call([ 'gnuplot', gp_coherence_name ]) |
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| 181 | |
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| 182 | |
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| 183 | ############################################################ |
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| 184 | ### Graph 2 : Speedup per Application ### |
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| 185 | ############################################################ |
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| 186 | |
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| 187 | for app in apps: |
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| 188 | |
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| 189 | data_speedup_name = os.path.join(scripts_path, gen_dir, app + '_speedup.dat') |
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| 190 | gp_speedup_name = os.path.join(scripts_path, gen_dir, app + '_speedup.gp') |
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| 191 | |
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| 192 | # Creating data file |
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| 193 | width = 15 |
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| 194 | content = "#nb_procs" |
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| 195 | nb_spaces = width - len(content) |
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| 196 | content += nb_spaces * ' ' |
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| 197 | content += "speedup\n" |
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| 198 | |
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| 199 | for i in nb_procs: |
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| 200 | content += "%-15d " % i |
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| 201 | val = exec_time[app][i] |
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| 202 | content += "%-15f\n" % (exec_time[app][1] / float(val)) |
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| 203 | |
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| 204 | plot_str = "\"" + data_speedup_name + "\" using ($1):($2) lc rgb \"#654387\" title \"Speedup\" with linespoint" |
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| 205 | |
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| 206 | create_file(data_speedup_name, content) |
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| 207 | |
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| 208 | # Creating the gp file |
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| 209 | template_file = open(speedup_tmpl, 'r') |
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| 210 | template = template_file.read() |
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| 211 | |
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| 212 | 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, app + '_speedup')) |
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| 213 | |
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| 214 | create_file(gp_speedup_name, gp_commands) |
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| 215 | |
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| 216 | # Calling gnuplot |
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| 217 | print "gnuplot", gp_speedup_name |
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| 218 | subprocess.call([ 'gnuplot', gp_speedup_name ]) |
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| 219 | |
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| 220 | |
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| 221 | ############################################################ |
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| 222 | ### Graph 3 : All speedups on the same Graph ### |
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| 223 | ############################################################ |
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| 224 | |
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| 225 | # This graph uses the same template as the graph 2 |
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| 226 | |
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| 227 | data_speedup_name = os.path.join(scripts_path, gen_dir, 'all_speedup.dat') |
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| 228 | gp_speedup_name = os.path.join(scripts_path, gen_dir, 'all_speedup.gp') |
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| 229 | |
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| 230 | # Creating data file |
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| 231 | width = 15 |
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| 232 | content = "#nb_procs" |
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| 233 | nb_spaces = width - len(content) |
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| 234 | content += (nb_spaces + 1) * ' ' |
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| 235 | for app in apps: |
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| 236 | content += app + " " |
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| 237 | content += (width - len(app)) * " " |
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| 238 | content += "\n" |
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| 239 | |
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| 240 | for i in nb_procs: |
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| 241 | content += "%-15d " % i |
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| 242 | for app in apps: |
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| 243 | val = exec_time[app][i] |
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| 244 | content += "%-15f " % (exec_time[app][1] / float(val)) |
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| 245 | content += "\n" |
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| 246 | |
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| 247 | create_file(data_speedup_name, content) |
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| 248 | |
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| 249 | # Creating gp file |
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| 250 | template_file = open(speedup_tmpl, 'r') |
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| 251 | template = template_file.read() |
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| 252 | |
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| 253 | plot_str = "" |
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| 254 | col = 2 |
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| 255 | for app in apps: |
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| 256 | if app != apps[0]: |
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| 257 | plot_str += ", \\\n " |
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| 258 | 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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| 259 | col += 1 |
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| 260 | |
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| 261 | gp_commands = template % dict(appli = "All Applications", nb_procs = nb_procs[-1] + 1, plot_str = plot_str, svg_name = os.path.join(graph_dir, 'all_speedup')) |
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| 262 | |
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| 263 | create_file(gp_speedup_name, gp_commands) |
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| 264 | |
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| 265 | # Calling gnuplot |
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| 266 | print "gnuplot", gp_speedup_name |
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| 267 | subprocess.call([ 'gnuplot', gp_speedup_name ]) |
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| 268 | |
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| 269 | |
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| 270 | ############################################################ |
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| 271 | ### Graph 4 : Graph per metric ### |
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| 272 | ############################################################ |
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| 273 | |
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| 274 | # The following section creates the graphs grouped by measure (e.g. #broadcasts) |
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| 275 | # The template file cannot be easily created otherwise it would not be generic |
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| 276 | # in many ways. This is why it is mainly created here. |
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| 277 | # Graphs are created for metric in the "individual_metrics" list |
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| 278 | |
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| 279 | for metric in individual_metrics: |
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| 280 | data_metric_name = os.path.join(scripts_path, gen_dir, metric + '.dat') |
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| 281 | gp_metric_name = os.path.join(scripts_path, gen_dir, metric + '.gp') |
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| 282 | |
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| 283 | # Creating the gp file |
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| 284 | # Setting xtics, i.e. number of procs for each application |
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| 285 | xtics_str = "(" |
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| 286 | first = True |
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| 287 | xpos = 1 |
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| 288 | app_labels = "" |
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| 289 | for num_appli in range(0, len(apps)): |
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| 290 | for i in nb_procs: |
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| 291 | if not first: |
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| 292 | xtics_str += ", " |
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| 293 | first = False |
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| 294 | if i == nb_procs[0]: |
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| 295 | xpos_first = xpos |
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| 296 | xtics_str += "\"%d\" %.1f" % (i, xpos) |
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| 297 | xpos_last = xpos |
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| 298 | xpos += 1.5 |
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| 299 | xpos += 0.5 |
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| 300 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
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| 301 | 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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| 302 | xtics_str += ")" |
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| 303 | |
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| 304 | xmax_val = xpos + 0.5 |
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| 305 | |
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| 306 | # Writing the lines of "plot" |
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| 307 | plot_str = "" |
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| 308 | xpos = 0 |
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| 309 | first = True |
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| 310 | column = 2 |
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| 311 | for i in range(0, len(nb_procs)): |
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| 312 | if not first: |
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| 313 | plot_str += ", \\\n " |
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| 314 | first = False |
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| 315 | plot_str += "\"%s\" using ($1+%.1f):($%d) lc rgb %s notitle with boxes" % (data_metric_name, xpos, column, colors[i]) |
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| 316 | column += 1 |
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| 317 | xpos += 1.5 |
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| 318 | |
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| 319 | template_file = open(metric_tmpl, 'r') |
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| 320 | template = template_file.read() |
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| 321 | |
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| 322 | 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, metric)) |
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| 323 | |
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| 324 | create_file(gp_metric_name, gp_commands) |
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| 325 | |
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| 326 | # Creating the data file |
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| 327 | width = 15 |
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| 328 | content = "#x_pos" |
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| 329 | nb_spaces = width - len(content) |
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| 330 | content += nb_spaces * ' ' |
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| 331 | for i in nb_procs: |
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| 332 | content += "%-15d" % i |
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| 333 | content += "\n" |
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| 334 | |
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| 335 | x_pos = 1 |
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| 336 | for app in apps: |
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| 337 | # Computation of x_pos |
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| 338 | content += "%-15f" % x_pos |
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| 339 | x_pos += len(nb_procs) * 1.5 + 0.5 |
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| 340 | for i in nb_procs: |
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| 341 | if m_metric_norm[metric] == "N": |
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| 342 | content += "%-15d" % (metrics_val[app][i][metric]) |
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| 343 | elif m_metric_norm[metric] == "P": |
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| 344 | content += "%-15f" % (float(metrics_val[app][i][metric]) / i) |
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| 345 | elif m_metric_norm[metric] == "C": |
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| 346 | content += "%-15f" % (float(metrics_val[app][i][metric]) / exec_time[app][i] * 1000) |
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| 347 | elif m_metric_norm[metric] == "W": |
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| 348 | content += "%-15f" % (float(metrics_val[app][i][metric]) / float(metrics_val[app][i]['total_write'])) # Number of writes |
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| 349 | elif m_metric_norm[metric] == "R": |
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| 350 | content += "%-15f" % (float(metrics_val[app][i][metric]) / float(metrics_val[app][i]['total_read'])) # Number of reads |
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| 351 | elif m_metric_norm[metric] == "D": |
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| 352 | content += "%-15f" % (float(metrics_val[app][i][metric]) / float(metrics_val[app][i]['total_direct'])) # Number of req. |
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| 353 | elif is_numeric(m_metric_norm[metric]): |
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| 354 | content += "%-15f" % (float(metrics_val[app][i][metric]) / float(metrics_val[app][int(m_metric_norm[metric])][metric])) |
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| 355 | else: |
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| 356 | assert(False) |
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| 357 | |
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| 358 | app_name = m_app_name[app] |
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| 359 | content += "#" + app_name + "\n" |
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| 360 | |
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| 361 | create_file(data_metric_name, content) |
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| 362 | |
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| 363 | # Calling gnuplot |
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| 364 | print "gnuplot", gp_metric_name |
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| 365 | subprocess.call([ 'gnuplot', gp_metric_name ]) |
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| 366 | |
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| 367 | |
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| 368 | ############################################################ |
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| 369 | ### Graph 5 : Stacked histogram with counters ### |
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| 370 | ############################################################ |
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| 371 | |
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| 372 | # The following section creates a stacked histogram containing |
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| 373 | # the metrics in the "stacked_metric" list |
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| 374 | # It is normalized per application w.r.t the values on 256 procs |
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| 375 | |
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| 376 | data_stacked_name = os.path.join(scripts_path, gen_dir, 'stacked.dat') |
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| 377 | gp_stacked_name = os.path.join(scripts_path, gen_dir, 'stacked.gp') |
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| 378 | |
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| 379 | norm_factor_value = 256 |
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| 380 | |
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| 381 | # Creating the gp file |
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| 382 | template_file = open(stacked_tmpl, 'r') |
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| 383 | template = template_file.read() |
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| 384 | |
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| 385 | xtics_str = "(" |
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| 386 | first = True |
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| 387 | xpos = 1 |
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| 388 | app_labels = "" |
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| 389 | for num_appli in range(0, len(apps)): |
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| 390 | for i in nb_procs: |
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| 391 | if not first: |
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| 392 | xtics_str += ", " |
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| 393 | first = False |
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| 394 | if i == nb_procs[0]: |
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| 395 | xpos_first = xpos |
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| 396 | xtics_str += "\"%d\" %d -1" % (i, xpos) |
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| 397 | xpos_last = xpos |
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| 398 | xpos += 1 |
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| 399 | xpos += 1 |
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| 400 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
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| 401 | 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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| 402 | xtics_str += ")" |
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| 403 | |
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| 404 | plot_str = "newhistogram \"\"" |
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| 405 | n = 1 |
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| 406 | for stacked_metric in stacked_metrics: |
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| 407 | plot_str += ", \\\n " + "'" + data_stacked_name + "'" + " using " + str(n) + " lc rgb " + colors[n] + " title \"" + m_metric_name[stacked_metric] + "\"" |
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| 408 | n += 1 |
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| 409 | |
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| 410 | ylabel_str = "Breakdown of Coherence Traffic Normalized w.r.t. \\nthe Values on %d Processors" % norm_factor_value |
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| 411 | content = template % dict(svg_name = os.path.join(graph_dir, 'stacked'), xtics_str = xtics_str, plot_str = plot_str, ylabel_str = ylabel_str, app_labels = app_labels) |
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| 412 | |
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| 413 | create_file(gp_stacked_name, content) |
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| 414 | |
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| 415 | # Creating the data file |
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| 416 | # Values are normalized by application, w.r.t. the number of requests for a given number of procs |
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| 417 | content = "#" |
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| 418 | for stacked_metric in stacked_metrics: |
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| 419 | content += stacked_metric |
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| 420 | content += ' ' + ' ' * (15 - len(stacked_metric)) |
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| 421 | content += "\n" |
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| 422 | for app in apps: |
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| 423 | if app != apps[0]: |
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| 424 | for i in range(0, len(stacked_metrics)): |
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| 425 | content += "%-15f" % 0.0 |
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| 426 | content += "\n" |
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| 427 | for i in nb_procs: |
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| 428 | for stacked_metric in stacked_metrics: |
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| 429 | content += "%-15f" % (float(metrics_val[app][i][stacked_metric]) / metrics_val[app][norm_factor_value]['total_stacked']) |
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| 430 | content += "\n" |
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| 431 | |
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| 432 | create_file(data_stacked_name, content) |
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| 433 | # Calling gnuplot |
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| 434 | print "gnuplot", gp_stacked_name |
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| 435 | subprocess.call([ 'gnuplot', gp_stacked_name ]) |
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| 436 | |
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| 437 | |
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| 438 | |
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| 439 | ################################################################################# |
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| 440 | ### Graph 6 : Stacked histogram with coherence cost compared to r/w cost ### |
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| 441 | ################################################################################# |
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| 442 | |
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| 443 | # The following section creates pairs of stacked histograms, normalized w.r.t. the first one. |
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| 444 | # The first one contains the cost of reads and writes, the second contains the cost |
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| 445 | # of m_inv, m_up and broadcasts (extrapolated) |
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| 446 | |
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| 447 | data_cost_filename = os.path.join(scripts_path, gen_dir, 'relative_cost.dat') |
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| 448 | gp_cost_filename = os.path.join(scripts_path, gen_dir, 'relative_cost.gp') |
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| 449 | |
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| 450 | direct_cost_metrics = [ 'read_cost', 'write_cost' ] |
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| 451 | coherence_cost_metrics = ['update_cost', 'm_inv_cost', 'broadcast_cost' ] |
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| 452 | |
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| 453 | # Creating the gp file |
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| 454 | template_file = open(stacked_tmpl, 'r') |
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| 455 | template = template_file.read() |
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| 456 | |
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| 457 | xtics_str = "(" |
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| 458 | first = True |
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| 459 | xpos = 1.5 |
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| 460 | app_labels = "" |
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| 461 | for num_appli in range(0, len(apps)): |
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| 462 | first_proc = True |
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| 463 | for i in nb_procs: |
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| 464 | if i > 4: |
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| 465 | if not first: |
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| 466 | xtics_str += ", " |
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| 467 | first = False |
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| 468 | if first_proc: |
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| 469 | first_proc = False |
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| 470 | xpos_first = xpos |
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| 471 | xtics_str += "\"%d\" %f -1" % (i, xpos) |
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| 472 | xpos_last = xpos |
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| 473 | xpos += 3 |
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| 474 | app_name_xpos = float((xpos_first + xpos_last)) / 2 |
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| 475 | 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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| 476 | xpos += 1 |
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| 477 | xtics_str += ")" |
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| 478 | |
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| 479 | plot_str = "newhistogram \"\"" |
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| 480 | n = 1 |
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| 481 | for cost_metric in direct_cost_metrics + coherence_cost_metrics: |
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| 482 | plot_str += ", \\\n " + "'" + data_cost_filename + "'" + " using " + str(n) + " lc rgb " + colors[n] + " title \"" + m_metric_name[cost_metric] + "\"" |
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| 483 | n += 1 |
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| 484 | |
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| 485 | ylabel_str = "Coherence Cost Compared to Direct Requests Cost,\\nNormalized per Application for each Number of Processors" |
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| 486 | content = template % dict(svg_name = os.path.join(graph_dir, 'rel_cost'), xtics_str = xtics_str, plot_str = plot_str, ylabel_str = ylabel_str, app_labels = app_labels) |
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| 487 | |
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| 488 | create_file(gp_cost_filename, content) |
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| 489 | |
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| 490 | # Creating the data file |
|---|
| 491 | # Values are normalized by application, w.r.t. the number of requests for a given number of procs |
|---|
| 492 | content = "#" |
|---|
| 493 | for cost_metric in direct_cost_metrics: |
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| 494 | content += cost_metric |
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| 495 | content += ' ' + ' ' * (15 - len(cost_metric)) |
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| 496 | for cost_metric in coherence_cost_metrics: |
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| 497 | content += cost_metric |
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| 498 | content += ' ' + ' ' * (15 - len(cost_metric)) |
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| 499 | content += "\n" |
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| 500 | for app in apps: |
|---|
| 501 | if app != apps[0]: |
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| 502 | for i in range(0, len(direct_cost_metrics) + len(coherence_cost_metrics)): |
|---|
| 503 | content += "%-15f" % 0.0 |
|---|
| 504 | content += "\n" |
|---|
| 505 | for i in nb_procs: |
|---|
| 506 | if i > 4: |
|---|
| 507 | for cost_metric in direct_cost_metrics: |
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| 508 | content += "%-15f" % (float(metrics_val[app][i][cost_metric]) / metrics_val[app][i]['direct_cost']) |
|---|
| 509 | for cost_metric in coherence_cost_metrics: |
|---|
| 510 | content += "%-15f" % 0.0 |
|---|
| 511 | content += "\n" |
|---|
| 512 | for cost_metric in direct_cost_metrics: |
|---|
| 513 | content += "%-15f" % 0.0 |
|---|
| 514 | for cost_metric in coherence_cost_metrics: |
|---|
| 515 | content += "%-15f" % (float(metrics_val[app][i][cost_metric]) / metrics_val[app][i]['direct_cost']) |
|---|
| 516 | content += "\n" |
|---|
| 517 | for i in range(0, len(direct_cost_metrics) + len(coherence_cost_metrics)): |
|---|
| 518 | content += "%-15f" % 0.0 |
|---|
| 519 | content += "\n" |
|---|
| 520 | |
|---|
| 521 | create_file(data_cost_filename, content) |
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| 522 | # Calling gnuplot |
|---|
| 523 | print "gnuplot", gp_cost_filename |
|---|
| 524 | subprocess.call([ 'gnuplot', gp_cost_filename ]) |
|---|
| 525 | |
|---|
| 526 | |
|---|
