def loadDataframe(mean_file):
mean_ps = PerformanceStatistics(mean_file, index_col='layout')
mean_df = mean_ps.getDataFrame()
mean_df['walk_cycles'] = mean_ps.getWalkDuration()
mean_df['cpu-cycles'] = mean_ps.getRuntime()
df = mean_df[['walk_cycles', 'cpu-cycles']]
return df
def loadDataframe(mean_file):
mean_ps = PerformanceStatistics(mean_file)
mean_df = mean_ps.getDataFrame()
mean_df['walk_cycles'] = mean_ps.getWalkDuration()
mean_df['cpu-cycles'] = mean_ps.getRuntime()
mean_df['stlb_misses'] = mean_ps.getStlbMisses()
mean_df['stlb_hits'] = mean_ps.getStlbHits()
df = mean_df[['walk_cycles', 'stlb_misses', 'stlb_hits', 'cpu-cycles']]
return df
def testSimpleInputFile(self):
test_file = os.path.dirname(sys.argv[0]) + '/performance_statistics_test_data.csv'
test_benchmark = 'my_gups/1GB'
ps = PerformanceStatistics(test_file, 'benchmark')
self.assertListEqual(ps.getIndexColumn().tolist(), ['my_gups/16GB', 'my_gups/1GB', 'my_gups/4GB'])
self.assertEqual(ps.getWalkDuration(test_benchmark), 42850778964.0)
self.assertEqual(ps.getStlbMisses(test_benchmark), 554443700.0)
self.assertEqual(ps.getRuntime(test_benchmark), 31148327321.0)
def loadDataframe(mean_file, output):
mean_ps = PerformanceStatistics(mean_file)
mean_df = mean_ps.getDataFrame()
mean_df['cpu-cycles'] = mean_ps.getRuntime()
mean_df['walk_cycles'] = mean_ps.getWalkDuration()
mean_df['stlb_hits'] = mean_ps.getStlbHits()
mean_df['stlb_misses'] = mean_ps.getStlbMisses()
df = mean_df[['layout', 'walk_cycles', 'stlb_hits', 'stlb_misses', 'cpu-cycles']]
# drop duplicated rows
important_columns = list(df.columns)
important_columns.remove('layout')
#df.drop_duplicates(inplace=True, subset=important_columns)
df = df.drop_duplicates(subset=important_columns)
df.to_csv(output)
return df
def readSingle(mean_file, std_file, y_metric, x_metric):
if x_metric == 'tlb_misses':
metric_func = PerformanceStatistics.getStlbMisses
elif x_metric == 'walk_cycles':
metric_func = PerformanceStatistics.getWalkDuration
else:
raise Exception('Unknown x-metric: ' + x_metric)
mean_ps = PerformanceStatistics(mean_file, 'layout')
mean_df = mean_ps.getDataFrame()
mean_df[x_metric] = metric_func(mean_ps)
mean_df = mean_df[[x_metric, y_metric]]
std_df = pd.DataFrame()
if std_file:
std_ps = PerformanceStatistics(std_file, 'layout')
std_df = std_ps.getDataFrame()
std_df[y_metric + '_std'] = std_df[y_metric]
std_df[x_metric + '_std'] = metric_func(std_ps)
std_df = std_df[[x_metric + '_std', y_metric + '_std']]
output_df = pd.concat([mean_df, std_df], axis='columns')
output_df.sort_values(x_metric, inplace=True)
return output_df