1e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein''' 2e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinCreated on May 19, 2011 3e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 4e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein@author: bungeman 5e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein''' 6e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 7e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinimport os 8e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinimport re 9e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinimport math 10e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 11e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# bench representation algorithm constant names 12e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinALGORITHM_AVERAGE = 'avg' 13e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinALGORITHM_MEDIAN = 'med' 14e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinALGORITHM_MINIMUM = 'min' 15e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinALGORITHM_25TH_PERCENTILE = '25th' 16e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 17e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# Regular expressions used throughout. 18e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinPER_SETTING_RE = '([^\s=]+)(?:=(\S+))?' 19e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinSETTINGS_RE = 'skia bench:((?:\s+' + PER_SETTING_RE + ')*)' 20e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinBENCH_RE = 'running bench (?:\[\d+ \d+\] )?\s*(\S+)' 21e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinTIME_RE = '(?:(\w*)msecs = )?\s*((?:\d+\.\d+)(?:,\s*\d+\.\d+)*)' 22e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# non-per-tile benches have configs that don't end with ']' or '>' 23e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinCONFIG_RE = '(\S+[^\]>]):\s+((?:' + TIME_RE + '\s+)+)' 24e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# per-tile bench lines are in the following format. Note that there are 25e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# non-averaged bench numbers in separate lines, which we ignore now due to 26e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# their inaccuracy. 27e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinTILE_RE = (' tile_(\S+): tile \[\d+,\d+\] out of \[\d+,\d+\] <averaged>:' 28e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein ' ((?:' + TIME_RE + '\s+)+)') 29e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# for extracting tile layout 30e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinTILE_LAYOUT_RE = ' out of \[(\d+),(\d+)\] <averaged>: ' 31e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 32e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinPER_SETTING_RE_COMPILED = re.compile(PER_SETTING_RE) 33e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinSETTINGS_RE_COMPILED = re.compile(SETTINGS_RE) 34e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinBENCH_RE_COMPILED = re.compile(BENCH_RE) 35e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinTIME_RE_COMPILED = re.compile(TIME_RE) 36e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinCONFIG_RE_COMPILED = re.compile(CONFIG_RE) 37e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinTILE_RE_COMPILED = re.compile(TILE_RE) 38e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinTILE_LAYOUT_RE_COMPILED = re.compile(TILE_LAYOUT_RE) 39e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 40e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinclass BenchDataPoint: 41e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """A single data point produced by bench. 42e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """ 43e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __init__(self, bench, config, time_type, time, settings, 44e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein tile_layout='', per_tile_values=[], per_iter_time=[]): 45e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # string name of the benchmark to measure 46e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.bench = bench 47e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # string name of the configurations to run 48e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.config = config 49e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # type of the timer in string: '' (walltime), 'c' (cpu) or 'g' (gpu) 50e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.time_type = time_type 51e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # float number of the bench time value 52e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.time = time 53e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # dictionary of the run settings 54e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.settings = settings 55e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # how tiles cover the whole picture: '5x3' means 5 columns and 3 rows 56e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.tile_layout = tile_layout 57e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # list of float for per_tile bench values, if applicable 58e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.per_tile_values = per_tile_values 59e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # list of float for per-iteration bench time, if applicable 60e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.per_iter_time = per_iter_time 61e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 62e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __repr__(self): 63e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return "BenchDataPoint(%s, %s, %s, %s, %s)" % ( 64e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.bench), 65e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.config), 66e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.time_type), 67e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.time), 68e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.settings), 69e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein ) 70e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 71e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinclass _ExtremeType(object): 72e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Instances of this class compare greater or less than other objects.""" 73e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __init__(self, cmpr, rep): 74e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein object.__init__(self) 75e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._cmpr = cmpr 76e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._rep = rep 77e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 78e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __cmp__(self, other): 79e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if isinstance(other, self.__class__) and other._cmpr == self._cmpr: 80e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return 0 81e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return self._cmpr 82e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 83e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __repr__(self): 84e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return self._rep 85e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 86e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinMax = _ExtremeType(1, "Max") 87e530eb370c084336b584a6dff5a9e6974d932dfaMike KleinMin = _ExtremeType(-1, "Min") 88e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 89e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinclass _ListAlgorithm(object): 90e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Algorithm for selecting the representation value from a given list. 91e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein representation is one of the ALGORITHM_XXX representation types.""" 92e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __init__(self, data, representation=None): 93e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if not representation: 94e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein representation = ALGORITHM_AVERAGE # default algorithm 95e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._data = data 96e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._len = len(data) 97e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if representation == ALGORITHM_AVERAGE: 98e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._rep = sum(self._data) / self._len 99e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 100e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._data.sort() 101e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if representation == ALGORITHM_MINIMUM: 102e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._rep = self._data[0] 103e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 104e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # for percentiles, we use the value below which x% of values are 105e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # found, which allows for better detection of quantum behaviors. 106e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if representation == ALGORITHM_MEDIAN: 107e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein x = int(round(0.5 * self._len + 0.5)) 108e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein elif representation == ALGORITHM_25TH_PERCENTILE: 109e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein x = int(round(0.25 * self._len + 0.5)) 110e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 111e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein raise Exception("invalid representation algorithm %s!" % 112e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein representation) 113e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self._rep = self._data[x - 1] 114e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 115e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def compute(self): 116e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return self._rep 117e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 118e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleindef _ParseAndStoreTimes(config_re_compiled, is_per_tile, line, bench, 119e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein value_dic, layout_dic): 120e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Parses given bench time line with regex and adds data to value_dic. 121e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 122e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein config_re_compiled: precompiled regular expression for parsing the config 123e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein line. 124e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein is_per_tile: boolean indicating whether this is a per-tile bench. 125e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein If so, we add tile layout into layout_dic as well. 126e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein line: input string line to parse. 127e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench: name of bench for the time values. 128e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein value_dic: dictionary to store bench values. See bench_dic in parse() below. 129e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein layout_dic: dictionary to store tile layouts. See parse() for descriptions. 130e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """ 131e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 132e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for config in config_re_compiled.finditer(line): 133e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_config = config.group(1) 134e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein tile_layout = '' 135e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if is_per_tile: # per-tile bench, add name prefix 136e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_config = 'tile_' + current_config 137e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein layouts = TILE_LAYOUT_RE_COMPILED.search(line) 138e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if layouts and len(layouts.groups()) == 2: 139e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein tile_layout = '%sx%s' % layouts.groups() 140e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein times = config.group(2) 141e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for new_time in TIME_RE_COMPILED.finditer(times): 142e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_time_type = new_time.group(1) 143e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein iters = [float(i) for i in 144e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein new_time.group(2).strip().split(',')] 145e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein value_dic.setdefault(bench, {}).setdefault( 146e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_config, {}).setdefault(current_time_type, []).append( 147e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein iters) 148e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein layout_dic.setdefault(bench, {}).setdefault( 149e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_config, {}).setdefault(current_time_type, tile_layout) 150e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 151e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleindef parse_skp_bench_data(directory, revision, rep, default_settings=None): 152e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Parses all the skp bench data in the given directory. 153e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 154e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Args: 155e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein directory: string of path to input data directory. 156e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein revision: git hash revision that matches the data to process. 157e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein rep: bench representation algorithm, see bench_util.py. 158e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein default_settings: dictionary of other run settings. See writer.option() in 159e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench/benchmain.cpp. 160e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 161e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Returns: 162e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein A list of BenchDataPoint objects. 163e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """ 164e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein revision_data_points = [] 165e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein file_list = os.listdir(directory) 166e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein file_list.sort() 167e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for bench_file in file_list: 168e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein scalar_type = None 169e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # Scalar type, if any, is in the bench filename after 'scalar_'. 170e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if (bench_file.startswith('bench_' + revision + '_data_')): 171e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if bench_file.find('scalar_') > 0: 172e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein components = bench_file.split('_') 173e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein scalar_type = components[components.index('scalar') + 1] 174e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: # Skips non skp bench files. 175e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein continue 176e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 177e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein with open('/'.join([directory, bench_file]), 'r') as file_handle: 178e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settings = dict(default_settings or {}) 179e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settings['scalar'] = scalar_type 180e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein revision_data_points.extend(parse(settings, file_handle, rep)) 181e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 182e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return revision_data_points 183e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 184e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# TODO(bensong): switch to reading JSON output when available. This way we don't 185e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein# need the RE complexities. 186e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleindef parse(settings, lines, representation=None): 187e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Parses bench output into a useful data structure. 188e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 189e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein ({str:str}, __iter__ -> str) -> [BenchDataPoint] 190e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein representation is one of the ALGORITHM_XXX types.""" 191e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 192e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein benches = [] 193e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_bench = None 194e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # [bench][config][time_type] -> [[per-iter values]] where per-tile config 195e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # has per-iter value list for each tile [[<tile1_iter1>,<tile1_iter2>,...], 196e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # [<tile2_iter1>,<tile2_iter2>,...],...], while non-per-tile config only 197e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # contains one list of iterations [[iter1, iter2, ...]]. 198e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench_dic = {} 199e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # [bench][config][time_type] -> tile_layout 200e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein layout_dic = {} 201e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 202e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for line in lines: 203e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 204e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # see if this line is a settings line 205e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settingsMatch = SETTINGS_RE_COMPILED.search(line) 206e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if (settingsMatch): 207e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settings = dict(settings) 208e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for settingMatch in PER_SETTING_RE_COMPILED.finditer(settingsMatch.group(1)): 209e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if (settingMatch.group(2)): 210e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settings[settingMatch.group(1)] = settingMatch.group(2) 211e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 212e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settings[settingMatch.group(1)] = True 213e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 214e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # see if this line starts a new bench 215e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein new_bench = BENCH_RE_COMPILED.search(line) 216e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if new_bench: 217e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_bench = new_bench.group(1) 218e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 219e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # add configs on this line to the bench_dic 220e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if current_bench: 221e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if line.startswith(' tile_') : 222e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein _ParseAndStoreTimes(TILE_RE_COMPILED, True, line, current_bench, 223e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench_dic, layout_dic) 224e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 225e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein _ParseAndStoreTimes(CONFIG_RE_COMPILED, False, line, 226e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein current_bench, bench_dic, layout_dic) 227e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 228e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # append benches to list 229e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for bench in bench_dic: 230e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for config in bench_dic[bench]: 231e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for time_type in bench_dic[bench][config]: 232e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein tile_layout = '' 233e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_tile_values = [] # empty for non-per-tile configs 234e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_iter_time = [] # empty for per-tile configs 235e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench_summary = None # a single final bench value 236e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if len(bench_dic[bench][config][time_type]) > 1: 237e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # per-tile config; compute representation for each tile 238e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_tile_values = [ 239e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein _ListAlgorithm(iters, representation).compute() 240e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for iters in bench_dic[bench][config][time_type]] 241e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # use sum of each tile representation for total bench value 242e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench_summary = sum(per_tile_values) 243e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # extract tile layout 244e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein tile_layout = layout_dic[bench][config][time_type] 245e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 246e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein # get the list of per-iteration values 247e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_iter_time = bench_dic[bench][config][time_type][0] 248e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench_summary = _ListAlgorithm( 249e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_iter_time, representation).compute() 250e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein benches.append(BenchDataPoint( 251e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench, 252e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein config, 253e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein time_type, 254e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein bench_summary, 255e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein settings, 256e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein tile_layout, 257e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_tile_values, 258e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein per_iter_time)) 259e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 260e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return benches 261e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 262e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinclass LinearRegression: 263e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Linear regression data based on a set of data points. 264e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 265e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein ([(Number,Number)]) 266e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein There must be at least two points for this to make sense.""" 267e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __init__(self, points): 268e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein n = len(points) 269e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein max_x = Min 270e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein min_x = Max 271e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 272e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sx = 0.0 273e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sy = 0.0 274e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sxx = 0.0 275e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sxy = 0.0 276e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Syy = 0.0 277e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein for point in points: 278e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein x = point[0] 279e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein y = point[1] 280e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein max_x = max(max_x, x) 281e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein min_x = min(min_x, x) 282e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 283e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sx += x 284e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sy += y 285e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sxx += x*x 286e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Sxy += x*y 287e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein Syy += y*y 288e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 289e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein denom = n*Sxx - Sx*Sx 290e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if (denom != 0.0): 291e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein B = (n*Sxy - Sx*Sy) / denom 292e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein else: 293e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein B = 0.0 294e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein a = (1.0/n)*(Sy - B*Sx) 295e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 296e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein se2 = 0 297e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein sB2 = 0 298e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein sa2 = 0 299e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if (n >= 3 and denom != 0.0): 300e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein se2 = (1.0/(n*(n-2)) * (n*Syy - Sy*Sy - B*B*denom)) 301e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein sB2 = (n*se2) / denom 302e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein sa2 = sB2 * (1.0/n) * Sxx 303e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 304e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 305e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.slope = B 306e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.intercept = a 307e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.serror = math.sqrt(max(0, se2)) 308e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.serror_slope = math.sqrt(max(0, sB2)) 309e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.serror_intercept = math.sqrt(max(0, sa2)) 310e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.max_x = max_x 311e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein self.min_x = min_x 312e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 313e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def __repr__(self): 314e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return "LinearRegression(%s, %s, %s, %s, %s)" % ( 315e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.slope), 316e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.intercept), 317e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.serror), 318e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.serror_slope), 319e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein str(self.serror_intercept), 320e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein ) 321e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 322e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein def find_min_slope(self): 323e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Finds the minimal slope given one standard deviation.""" 324e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein slope = self.slope 325e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein intercept = self.intercept 326e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein error = self.serror 327e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein regr_start = self.min_x 328e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein regr_end = self.max_x 329e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein regr_width = regr_end - regr_start 330e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 331e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein if slope < 0: 332e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein lower_left_y = slope*regr_start + intercept - error 333e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein upper_right_y = slope*regr_end + intercept + error 334e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return min(0, (upper_right_y - lower_left_y) / regr_width) 335e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 336e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein elif slope > 0: 337e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein upper_left_y = slope*regr_start + intercept + error 338e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein lower_right_y = slope*regr_end + intercept - error 339e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return max(0, (lower_right_y - upper_left_y) / regr_width) 340e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 341e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return 0 342e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 343e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleindef CreateRevisionLink(revision_number): 344e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """Returns HTML displaying the given revision number and linking to 345e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein that revision's change page at code.google.com, e.g. 346e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein http://code.google.com/p/skia/source/detail?r=2056 347e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein """ 348e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein return '<a href="http://code.google.com/p/skia/source/detail?r=%s">%s</a>'%( 349e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein revision_number, revision_number) 350e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 351e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleindef main(): 352e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein foo = [[0.0, 0.0], [0.0, 1.0], [0.0, 2.0], [0.0, 3.0]] 353e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein LinearRegression(foo) 354e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein 355e530eb370c084336b584a6dff5a9e6974d932dfaMike Kleinif __name__ == "__main__": 356e530eb370c084336b584a6dff5a9e6974d932dfaMike Klein main() 357