def main():
parser = argparse.ArgumentParser()
parser.add_argument("n_values", type=int, help="Number of values to generate")
parser.add_argument('-u', '--unroll', action='store_true', help='Generate parameters for unroll speedup prediction')
args = parser.parse_args()
n_values = args.n_values
unroll = args.unroll
var_name = 'LORE_PROC_CLANG_PATH' if unroll else 'LORE_PROC_PATH'
check_config([var_name])
proc_dir = os.path.abspath(os.environ[var_name])
dirs = os.listdir(proc_dir)
n_dirs = len(dirs)
parsed = 0
failed = 0
df_meta = get_df_meta()
for i, file_name in enumerate(dirs):
if not os.path.isdir(os.path.join(proc_dir, file_name)):
failed += 1
continue
print('[' + str(i) + '/' + str(n_dirs) + '] Generating params for ' + file_name)
try:
with open(os.path.join(proc_dir, file_name, file_name + '_params.txt'), 'w') as fout, \
open(os.path.join(proc_dir, file_name, file_name + '_params_names.txt'), 'r') as fin_names, \
open(os.path.join(proc_dir, file_name, file_name + '_max_param.txt'), 'r') as fin_max:
max_param = int(fin_max.read())
param_names = fin_names.read().strip().split(',')
try:
loop_depth = df_meta.loc[file_name, 'loop_depth']
except KeyError:
print('\tCannot generate params - unknown loop depth')
failed += 1
continue
if len(param_names) > 0 and len(param_names[0]) > 0:
for k in range(1, n_values + 1):
defines = ['-D ' + p + '=' + str(intermediate_value(k, n_values, loop_depth, max_param))
for p in param_names]
fout.write(' '.join(defines) + '\n')
else:
fout.write('\n')
parsed += 1
except FileNotFoundError:
failed += 1
print('\tFile (...)_params_names.txt or (...)_max_param.txt is missing.')
print('========')
print(str(parsed) + ' parsed, ' + str(failed) + ' skipped')
def main():
check_config(['LORE_ORIG_PATH', 'LORE_PROC_PATH'])
argparser = argparse.ArgumentParser()
argparser.add_argument('-v',
'--verbose',
action='store_true',
help='Verbose')
argparser.add_argument('-u',
'--unroll',
action='store_true',
help='If enabled prepare code for loop unrolling')
args = argparser.parse_args()
verbose = args.verbose
unroll = args.unroll
orig_path = os.path.abspath(os.environ['LORE_ORIG_PATH'])
proc_path = os.path.abspath(os.environ['LORE_PROC_PATH'])
df_meta = pd.DataFrame(columns=('alg', 'max_arr_dim', 'loop_depth'))
print(df_meta)
if not os.path.isdir(proc_path):
os.makedirs(proc_path)
dirs = os.listdir(orig_path)
n_dirs = len(dirs)
parsed = 0
failed = 0
for i, file_name in enumerate(dirs):
try:
if not file_name.endswith(".c"):
continue
print('[' + str(i + 1) + '/' + str(n_dirs) +
'] Parsing %s' % file_name)
file_path = os.path.join(orig_path, file_name)
file_name = str(file_name[:-2])
out_dir = os.path.join(proc_path, file_name)
with open(file_path, 'r') as fin:
code = fin.read()
includes, code = split_code(code)
ct = CodeTransformer(includes=includes,
code=code,
papi_scope='pragma',
verbose=verbose,
main_name='loop',
modifiers_to_remove=['extern'],
gen_mallocs=True,
rename_bounds=(not unroll),
add_pragma_unroll=unroll)
code = ct.transform()
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
with open(os.path.join(out_dir, file_name + '.c'),
'w') as fout:
fout.write(code)
with open(os.path.join(out_dir, file_name + '_max_param.txt'),
'w') as fout:
fout.write(str(ct.max_param))
with open(
os.path.join(out_dir, file_name + '_params_names.txt'),
'w') as fout:
fout.write(','.join(
['PARAM_' + b.upper() for b in ct.pp.bounds]))
df_meta = df_meta.append(pd.DataFrame(
[[file_name, ct.max_arr_dim, ct.loop_depth]],
columns=df_meta.columns),
ignore_index=True)
parsed += 1
except Exception as e:
failed += 1
print('\t', e)
df_meta.to_csv(os.path.join(proc_path, 'metadata.csv'),
index_label=False,
index=False)
print('========')
print(str(parsed) + ' parsed, ' + str(failed) + ' skipped')
from __future__ import print_function
import argparse
import os
from sklearn.externals import joblib
from popsicle.ml_utils.file_loader import FileLoader
from popsicle.utils import check_config
check_config(['OUT_DIR', 'MODELS_DIR'])
out_dir = os.path.abspath(os.environ['OUT_DIR'])
model_dir = os.path.abspath(os.environ['MODELS_DIR'])
def load_models():
scaler = joblib.load(os.path.join(model_dir, 'scaler.pkl'))
dim_reducer = joblib.load(os.path.join(model_dir, 'dim_reducer.pkl'))
regr = joblib.load(os.path.join(model_dir, 'regr.pkl'))
return scaler, dim_reducer, regr
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', action='append', required=True,
help='<Required> input files in CSV format (names without extensions). You can provide multiple'
' files (-i file1 -i file2...).')
args = parser.parse_args()
files = args.input
scaler, dim_reducer, regr = load_models()
fl = FileLoader(files, mode='p', scaler=scaler)
import os
from typing import Tuple, List
from random import shuffle
import pandas as pd
from popsicle.ml_utils.data_set import DataSet
from popsicle.utils import check_config
check_config(['LORE_PROC_PATH'])
proc_dir = os.path.abspath(os.environ['LORE_PROC_PATH'])
def df_aggregate(df: pd.DataFrame) -> pd.DataFrame:
"""
Of all measurements of the same program with same parameters, take the minimum.
Minimum should be always the best approximation of the actual program characteristics without any overhead.
"""
return df.groupby(['alg', 'run']).min()
def df_get_index_col(df: pd.DataFrame, col: str) -> List:
"""
Finds a column with given name in DataFrame's index.
"""
col_level = df.index.names.index(col)
return df.index.get_level_values(col_level)
def df_scale_by_tot_ins(df: pd.DataFrame) -> pd.DataFrame:
"""
Divides values of all PAPI values outputs by PAPI_TOT_INS in order to normalise them
"""
import argparse
import pandas as pd
# noinspection PyCompatibility
import urllib.parse
# noinspection PyCompatibility
import urllib.request
import os
from popsicle.utils import check_config
check_config(['LORE_ORIG_PATH'])
lore_url = 'https://vectorization.computer/AJAX/get_src.php'
out_dir = os.path.abspath(os.environ["LORE_ORIG_PATH"])
def main():
"""
The input file can be obtained from LORE repository online by running the query:
SELECT id, application, benchmark, file, line, function, version FROM loops
at https://vectorization.computer/query.html
(valid as of Aug 2018)
"""
parser = argparse.ArgumentParser()
parser.add_argument("input_file",
help="Path to CSV file obtained from LORE query.")
args = parser.parse_args()
input_file = args.input_file
if not os.path.isdir(out_dir):
import os
import pandas as pd
from typing import List
from popsicle.ml_utils.data import Data
from popsicle.ml_utils.df_utils import df_aggregate, df_sort_cols, df_scale_by_tot_ins, get_df_meta
from popsicle.utils import check_config
check_config(['OUT_DIR'])
out_dir = os.path.abspath(os.environ['OUT_DIR'])
min_time = 100
class FileLoader:
def __init__(self, files, mode='gcc', dim=None, scaler=None):
if mode in ('predict', 'p') and scaler is None:
raise ValueError('Scaler must be provided to make predictions')
self.data = None
self.files = []
for file in files:
if file.endswith('.csv'):
print('Warning: ' + file.split('/')[-1] + ' interpreted as ' +
file.split('/')[-1][:-4] +
' (file names should be provided without extensions).')
self.files.append(file[:-4])
else:
self.files.append(file)
self.dim = dim
self.scaler = scaler
import os
import re
from popsicle.utils import check_config
check_config('PAPI_UTILS_PATH')
papi_utils_path = os.path.abspath(os.environ['PAPI_UTILS_PATH'])
class CodeTransformerStr:
def __init__(self, includes, code):
self.includes = includes
self.code = code
def add_includes(self, other_includes=None):
"""
Adds all necessary #include instructions to the code.
"""
if other_includes is None:
other_includes = []
self.includes += '\n'
self.includes += '#include <papi.h>\n'
self.includes += '#include <time.h>\n'
for path in other_includes:
self.includes += '#include <' + path + '>\n'
self.includes += '#include "' + os.path.abspath(
os.path.join(papi_utils_path, 'papi_utils.h')) + '"\n'
def add_max_macro(self):