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1 #! /usr/bin/env python
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2 # -*- coding: utf-8 -*-
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3
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4 import sys
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5 from re import search
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6 import datetime
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7 from os.path import basename
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8
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9 """
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10 This script generates a graph that represents the overhead
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11 involved in synchronisation operations
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12 """
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13
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14 usage="""
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15 This generates an output file for each value of number-of-threads that give the number of all runs.
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16 As an input it expects a result file which is generated by the overhead_result_calc.py script.
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17 The file extension of the result file has to be ".meas".
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18 It is expected that the output directory's path is meaningful, such as machine-name, date, and so on
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19 Usage:
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20 %s [result files] [path to output dir]
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21 """ % sys.argv[0]
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22
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23 if len(sys.argv) < 3:
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24 print usage
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25 sys.exit(0)
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26
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27 result_filenames = sys.argv[1:-1]
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28 output_dir_path = sys.argv[-1]
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29
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30 for result_filename in result_filenames:
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31 #open input file
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32 try:
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33 result_file = open(result_filename,"r")
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34 except:
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35 print "Cannot open result file %s" % result_filename
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36 sys.exit(1)
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37
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38 #parse(evaluate) result file
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39 try:
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40 exec(result_file.read())
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41 except:
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42 print "Cannot parse result file: %s" % result_filename
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43 result_file.close()
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44 sys.exit(1)
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45 result_file.close()
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46
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47 #check for file extension
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48 result_filename = basename(result_filename)
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49 if search("\.meas$",result_filename) == None:
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50 print "Wrong file extension! Has to be '.meas'"
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51 sys.exit(1)
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52
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53 output = output_dir_path + "/" + result_filename.replace(".meas",".result")
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54 #open gnuplot output
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55 try:
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56 gnuplot_output = open(output,"w")
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57 except IOError:
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58 print "Cannot open output file %s" % output
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59 result_file.close()
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60 sys.exit(1)
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61
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62 table_header = "# %20s\t%20s\t%20s\t%20s\t%20s\t%20s\t%20s\t%20s\n" % (
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63 "<iters per task>",
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64 "<total exe cycles>",
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65 "<total work cyc>",
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66 "<one task cyc>",
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67 "<total overhead cyc>",
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68 "<num syncs>",
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69 "<overhead per Sync cyc>",
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70 "<Exe/Work ratio>")
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71
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72 #write header to file
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73 gnuplot_output.writelines(["# Output file name: %s\n" % data_filename,
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74 "# Date of Run: %s\n" % date_of_run,
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75 "# Number of Cores: %d\n" % NUM_CORES,
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76 "# Number of Threads: %f per Core, %d total\n" % (threads_per_core, totalThreads),
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77 table_header,
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78 "# " + (len(table_header)-3)*"-" + "\n"])
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79
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80 #Now print the results out
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81 for workload_iterations_in_task in ITERS_PER_TASK_TABLE:
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82 results = array_of_results[workload_iterations_in_task]
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83
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84 #take shortest run
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85 results.sort(lambda x,y: cmp(x["total_exe_cycles"],y["total_exe_cycles"]))
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86 total_workcycles = results[0]["total_workcycles"]
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87 total_exe_cycles = results[0]["total_exe_cycles"]
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88 #exeCycles_workCycles_ratio = results[0]["exeCycles_workCycles_ratio"]
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89
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90 #Calculate numbers
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91 overhead = total_exe_cycles - total_workcycles
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92 total_syncs = totalThreads * TASKS_PER_THREAD * 2
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93 overhead_per_sync = float(overhead) / float(total_syncs)
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94 cycles_of_task = float(total_workcycles) / float(TASKS_PER_THREAD * totalThreads)
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95 overhead_per_core = float(overhead) / NUM_CORES
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96 workcycles_per_core = total_workcycles / NUM_CORES
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97
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98 # The 2 is in there because we have two sync operations in one per outer iteration
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99 exeCycles_workCycles_ratio = float(total_workcycles+float(overhead)/2)/float(total_workcycles)
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100
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101 gnuplot_output.write("%20d\t%20d\t%20d\t%20f\t%20d\t%20d\t%20f\t%20f\n" % (
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102 workload_iterations_in_task,
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103 total_exe_cycles,
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104 total_workcycles,
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105 cycles_of_task,
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106 overhead,
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107 total_syncs,
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108 overhead_per_sync,
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109 exeCycles_workCycles_ratio))
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110
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111 gnuplot_output.close();
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