<jupyter_start><jupyter_text>Scaling Analysis Examples TTbar70Authors: <NAME> and <NAME>All this is using the taucmdr python libraries from paratoolshttp://taucommander.paratools.com/topLoading DataMetricsCorrelationsStore Buffer StallsFetch Stallsl3 misses  ImportsThis section imports necessary libraies, the metrics.py and utilities.py files and sets up the window.<jupyter_code># A couple of scripts to set the environent and import data from a .tau set of results
from utilities import *
from metrics import *
# Plotting, notebook settings:
%matplotlib inline  
#plt.rcParams.update({'font.size': 16})
import numbers
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
pd.set_option('display.float_format', lambda x: '%.2e' % x)
pd.set_option('display.max_columns',100)
pd.set_option('max_colwidth', 70)

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

import copy, shelve
application = "tau_mictest"
exp1 = "TAU__SKL-SP_CMSSW_TTbar_PU70_NVU16_NTH1_HS100"
exp2 = "TAU__SKL-SP_CMSSW_TTbar_PU70_NVU16_NTH1_HS9"

HS100 = load_perf_data(application,exp1,nolibs=False,scaling=False,callpaths=True)
#original_metric_data = remove_erroneous_threads(original_metric_data,  [4, 8, 16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256])

HS9   = load_perf_data(application,exp2,nolibs=False,scaling=False,callpaths=True)

print HS100.keys()
print HS9.keys()

hs100_sorted = HS100['PAPI_SP_OPS'][['Exclusive']].sort_values(by='Exclusive',ascending=False)
hs100_sorted.head(5)
hs9_sorted = HS9['PAPI_SP_OPS'][['Exclusive']].sort_values(by='Exclusive',ascending=False)
hs9_sorted.head(5)
METRIC='PAPI_SP_OPS'
func = 'KHC'

diff = hs100_sorted - hs9_sorted
diff_sorted = diff.sort_values(by='Exclusive',ascending=False)
diff_sorted.head(20)
diff_searched = get_func_level_metric(diff, func=func, avg=True)
diff_searched.head(20)
hs9_searched = get_func_level_metric(hs9_sorted, func=func, avg=True)
hs9_searched.head(20)
hs100_searched = get_func_level_metric(hs100_sorted, func=func, avg=True)
hs100_searched.head(20)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11584249084249085, 'fraction_numerical': 0.055860805860805864, 'mean_word_length': 8.458874458874458, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '123730', 'license': 'MIT', 'n_tokens_mistral': 910, 'n_tokens_neox': 808, 'n_words': 158, 'path': 'mictest/Simple_FLOP_comparison.ipynb', 'repo_name': 'HPCL/hep-performance', 'size': '2199'}