def execute(argv):
"""Generate random sequences for each benchmark"""
del argv
FLAGS = flags.FLAGS
# The benchmarks
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if not benchmarks:
logging.fatal('There are no benchmarks to process')
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
# Process each benchmark
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[index + 1:]
bench_in_dir = os.path.join(FLAGS.benchmarks_directory, bench_dir,
bench_name)
if not os.path.isdir(bench_in_dir):
continue
bench_out_dir = os.path.join(FLAGS.results_directory, bench_dir)
# Create the results directory for the suite
try:
os.makedirs(bench_out_dir)
except FileExistsError:
pass
filename = '{}/{}.yaml'.format(bench_out_dir, bench_name)
if FLAGS.verify_report and os.path.isfile(filename):
continue
results = {}
for compiler in ['opt', 'llvm']:
for level in FLAGS.levels:
goal_value = Engine.evaluate(
Goals.prepare_goals(FLAGS.goals,
FLAGS.weights), '-{}'.format(level),
compiler, bench_in_dir, FLAGS.working_set, FLAGS.times,
FLAGS.tool, FLAGS.verify_output)
compiler_name = 'clang' if compiler == 'llvm' else 'opt'
if compiler_name not in results:
results[compiler_name] = {}
results[compiler_name][level] = {
'goal': goal_value,
'seq': ['-{}'.format(level)]
}
IO.dump_yaml(results, filename)
def execute(argv):
"""Generate random sequences for each benchmark"""
del argv
FLAGS = flags.FLAGS
# The benchmarks
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if not benchmarks:
logging.error('There are no benchmarks to process')
sys.exit(1)
# Verify benchmark directory
if not os.path.isdir(FLAGS.benchmarks_directory):
logging.error('Benchmarks directory {} does not exist.'.format(
FLAGS.benchmarks_directory))
sys.exit(1)
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
# Initialize a Random object
rnd = Random(FLAGS.nof_sequences, FLAGS.minimum, FLAGS.maximum,
FLAGS.factor, FLAGS.ssa, FLAGS.shuffle, FLAGS.update,
FLAGS.repetition, FLAGS.original, FLAGS.passes_filename,
Goals.prepare_goals(FLAGS.goals, FLAGS.weights), 'opt',
FLAGS.benchmarks_directory, FLAGS.working_set, FLAGS.times,
FLAGS.tool, FLAGS.verify_output)
# Process each benchmark
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[index + 1:]
bench_dir = os.path.join(FLAGS.results_directory, bench_dir)
# Create the results directory for the suite
try:
os.makedirs(bench_dir)
except FileExistsError:
pass
filename = '{}/{}.yaml'.format(bench_dir, bench_name)
if FLAGS.verify_report and os.path.isfile(filename):
continue
rnd.run(benchmark)
if rnd.results:
IO.dump_yaml(rnd.results, filename, FLAGS.report_only_the_best)
def execute(argv):
"""Generate the report"""
del argv
FLAGS = flags.FLAGS
results_files = glob.glob('{}/*.yaml'.format(FLAGS.data_directory))
report = {}
strategies = []
for results in tqdm(results_files, desc='Processing'):
index = results.rfind('_')
benchmark = results[:index]
benchmark = benchmark.replace('{}/'.format(FLAGS.data_directory), '')
strategy = results[index+1:]
strategy = strategy.replace('.yaml', '')
if strategy not in strategies:
strategies.append(strategy)
if benchmark not in report:
report[benchmark] = {}
data = IO.load_yaml(results)
values = [str_data['goal'] for _, str_data in data.items()]
report[benchmark][strategy] = sum(values)
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
filename = '{}/{}'.format(FLAGS.results_directory, FLAGS.report_filename)
IO.dump_yaml(report, filename)
strategies.sort()
csv_filename = filename.replace('.yaml', '.csv')
with open(csv_filename, 'w') as f:
w = csv.DictWriter(f, ['bench']+strategies)
w.writeheader()
line = {}
for bench_name, data in report.items():
line['bench'] = bench_name
for strategy_name, strategy_value in data.items():
line[strategy_name] = strategy_value
w.writerow(line)
def find_sequences(test_benchmark, training_benchmaks, distance_directory,
training_data_directory, nof_sequences):
"""Get N sequences from the most similar benchmark"""
# Test suite and benchmark
index = test_benchmark.find('.')
test_suite_name = test_benchmark[:index]
test_bench_name = test_benchmark[index + 1:]
# Find the training suites
training_suites = []
for training_benchmark in training_benchmaks:
index = training_benchmark.find('.')
training_suite_name = training_benchmark[:index]
if training_suite_name not in training_suites:
training_suites.append(training_suite_name)
# Find the closer benchmark
closer = []
for training_suite in training_suites:
d_directory = os.path.join(distance_directory, test_suite_name,
training_suite)
filename = '{}/{}.yaml'.format(d_directory, test_bench_name)
distance_data = IO.load_yaml(filename)
closer += [(distance, training_suite, training_bench)
for training_bench, distance in distance_data.items()]
closer.sort()
closer_suite_name = closer[0][1]
closer_bench_name = closer[0][2]
# Load closer benchmark data
d_directory = os.path.join(training_data_directory, closer_suite_name)
filename = '{}/{}.yaml'.format(d_directory, closer_bench_name)
training_data = IO.load_yaml_or_fail(filename)
# Rank sequences
rank = [(seq_data['goal'], seq_key)
for seq_key, seq_data in training_data.items()]
rank.sort()
# Extract N sequences
best = {}
for i, (_, seq_key) in enumerate(rank):
best[seq_key] = training_data[seq_key].copy()
if i + 1 == nof_sequences:
break
return closer_suite_name, best
def execute(argv):
"""Create a small sequence"""
del argv
FLAGS = flags.FLAGS
# The benchmarks
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if not benchmarks:
logging.fatal('There are no benchmarks to process')
# Verify directory
if not os.path.isdir(FLAGS.benchmarks_directory):
logging.error('Benchmarks directory {} does not exist.'.format(
FLAGS.benchmarks_directory))
sys.exit(1)
if not os.path.isdir(FLAGS.training_directory):
logging.error('Training directory {} does not exist.'.format(
FLAGS.training_directory))
sys.exit(1)
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
# Initialize a BenchmarkReduction object
bred = BenchmarkReduction(FLAGS.baseline, FLAGS.benchmarks_directory,
FLAGS.results_directory)
# Reduce
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[index + 1:]
bench_dir = os.path.join(FLAGS.training_directory, bench_dir)
sequences = IO.load_yaml_or_fail('{}/{}.yaml'.format(
bench_dir, bench_name))
sequence = Sequence.get_the_best(sequences)
for _, seq_data in sequence.items():
sequence = seq_data['seq']
bred.run(benchmark, sequence)
def execute(argv):
"""Generate the report"""
del argv
FLAGS = flags.FLAGS
results_files = glob.glob('{}/*.yaml'.format(FLAGS.data_directory))
report = {}
strategies = []
for results in tqdm(results_files, desc='Processing'):
benchmark = results.replace('{}/'.format(FLAGS.data_directory), '')
benchmark = benchmark.replace('.yaml', '')
if benchmark not in report:
report[benchmark] = {}
data = IO.load_yaml(results)
for compiler_name, compiler_data in data.items():
for level_name, level_data in compiler_data.items():
strategy = '{}_{}'.format(compiler_name, level_name)
if strategy not in strategies:
strategies.append(strategy)
report[benchmark][strategy] = level_data['goal']
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
csv_filename = '{}/{}'.format(FLAGS.results_directory,
FLAGS.report_filename)
with open(csv_filename, 'w') as f:
w = csv.DictWriter(f, ['bench'] + strategies)
w.writeheader()
line = {}
for bench_name, bench_data in report.items():
line['bench'] = bench_name
for level, level_value in bench_data.items():
line[level] = level_value
w.writerow(line)
def execute(argv):
"""Generate a CSV from YAML reports"""
del argv
FLAGS = flags.FLAGS
results_files = glob.glob('{}/*.yaml'.format(FLAGS.data_directory))
report = {}
strategies = []
for results in tqdm(results_files, desc='Report'):
benchmark = results.replace('{}/'.format(FLAGS.data_directory), '')
benchmark = benchmark.replace('.yaml', '')
if benchmark not in report:
report[benchmark] = {}
data = IO.load_yaml(results)
for seq_name, seq_data in data.items():
if seq_name not in strategies:
strategies.append(seq_name)
report[benchmark][seq_name] = seq_data['goal']
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
csv_filename = '{}/{}'.format(FLAGS.results_directory,
FLAGS.report_filename)
with open(csv_filename, 'w') as f:
w = csv.DictWriter(f, ['bench'] + strategies)
w.writeheader()
line = {}
for bench_name, data in report.items():
line['bench'] = bench_name
for seq_name, goal_value in data.items():
line[seq_name] = goal_value
w.writerow(line)
def execute(argv):
"""Create a small sequence"""
del argv
FLAGS = flags.FLAGS
# Verify directories
if not os.path.isdir(FLAGS.benchmarks_directory):
logging.error('Benchmarks directory {} does not exist.'.format(
FLAGS.benchmarks_directory))
sys.exit(1)
if not os.path.isdir(FLAGS.training_directory):
logging.error('Training directory {} does not exist.'.format(
FLAGS.training_directory))
sys.exit(1)
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
# The benchmarks
if FLAGS.benchmarks_filename:
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if not benchmarks:
logging.exit('There are no benchmarks to process')
sys.exit(1)
else:
benchmarks = glob.glob('{}/*.yaml'.format(FLAGS.training_directory))
benchmarks = [
b.replace('{}/'.format(FLAGS.training_directory),
'').replace('.yaml', '') for b in benchmarks
]
# Initialize a SequenceReduction object
seqred = SequenceReduction(Goals.prepare_goals(FLAGS.goals,
FLAGS.weights), 'opt',
FLAGS.benchmarks_directory, FLAGS.working_set,
FLAGS.times, FLAGS.tool, FLAGS.verify_output)
# Reduce
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[index + 1:]
bench_dir_result = os.path.join(FLAGS.results_directory, bench_dir)
filename_result = '{}/{}.yaml'.format(bench_dir_result, bench_name)
# Create the results directory for the suite
try:
os.makedirs(bench_dir_result)
except FileExistsError:
pass
if FLAGS.verify_report and os.path.isfile(filename_result):
continue
bench_dir_training = os.path.join(FLAGS.training_directory, bench_dir)
filename_training = '{}/{}.yaml'.format(bench_dir_training, bench_name)
sequences = IO.load_yaml(filename_training)
sequences = Sequence.get_the_best(sequences, FLAGS.nof_sequences)
results = {}
counter = 0
for _, data in sequences.items():
seqred.run(data['seq'], benchmark)
if not FLAGS.report_only_the_small:
results[counter] = {
'seq': seqred.results[0]['seq'],
'goal': seqred.results[0]['goal']
}
counter += 1
results[counter] = {
'seq': seqred.results[1]['seq'],
'goal': seqred.results[1]['goal']
}
counter += 1
IO.dump_yaml(results, filename_result)
def execute(argv):
"""Generate genetic sequences for each benchmark"""
del argv
FLAGS = flags.FLAGS
# The benchmarks
benchmarks = IO.load_yaml(FLAGS.benchmarks_filename)
if not benchmarks:
logging.error('There are no benchmarks to process')
sys.exit(1)
# Verify benchmark directory
if not os.path.isdir(FLAGS.benchmarks_directory):
logging.error('Benchmarks directory {} does not exist.'.format(
FLAGS.benchmarks_directory)
)
sys.exit(1)
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
# Initialize a SGA object
sga = SGA(FLAGS.generations,
FLAGS.population,
FLAGS.cr,
FLAGS.m,
FLAGS.param_m,
FLAGS.param_s,
FLAGS.crossover,
FLAGS.mutation,
FLAGS.selection,
FLAGS.seed,
FLAGS.dimension,
FLAGS.passes_filename,
Goals.prepare_goals(FLAGS.goals, FLAGS.weights),
'opt',
FLAGS.benchmarks_directory,
FLAGS.working_set,
FLAGS.times,
FLAGS.tool,
FLAGS.verify_output)
# Process each benchmark
for benchmark in tqdm(benchmarks, desc='Processing'):
index = benchmark.find('.')
bench_dir = benchmark[:index]
bench_name = benchmark[index+1:]
bench_dir = os.path.join(FLAGS.results_directory,
bench_dir)
# Create the results directory for the suite
try:
os.makedirs(bench_dir)
except FileExistsError:
pass
filename = '{}/{}.yaml'.format(bench_dir, bench_name)
if FLAGS.verify_report and os.path.isfile(filename):
continue
sga.run(benchmark)
if sga.results:
IO.dump_yaml(sga.results,
filename,
FLAGS.report_only_the_best)
def execute(argv):
"""Generate the report"""
del argv
FLAGS = flags.FLAGS
from_files = glob.glob('{}/*.yaml'.format(FLAGS.from_directory))
report = {}
strategies = []
statistics = {}
for from_file in tqdm(from_files, desc='Processing'):
index = from_file.rfind('_')
benchmark = from_file[:index]
benchmark = benchmark.replace('{}/'.format(FLAGS.from_directory), '')
strategy = from_file[index + 1:]
strategy = strategy.replace('.yaml', '')
if strategy not in strategies:
strategies.append(strategy)
statistics[strategy] = []
data = IO.load_yaml(from_file)
the_best = Sequence.get_the_best(data)
the_best_key = list(the_best.keys()).pop()
filename = '{}*{}.yaml'.format(benchmark, strategy)
to_files = glob.glob('{}/{}'.format(FLAGS.to_directory, filename))
for to_file in to_files:
benchmark = to_file.replace('{}/'.format(FLAGS.to_directory), '')
benchmark = benchmark.replace('_{}.yaml'.format(strategy), '')
if benchmark not in report:
report[benchmark] = {}
data = IO.load_yaml(to_file)
report[benchmark][strategy] = the_best.copy()
statistics[strategy].append(data[the_best_key]['goal'])
# Create the results directory
try:
os.makedirs(FLAGS.results_directory)
except FileExistsError:
pass
# YAML Report
filename = '{}/{}'.format(FLAGS.results_directory, FLAGS.report_filename)
IO.dump_yaml(report, filename)
# CSV Report
strategies.sort()
filename = filename.replace('.yaml', '.csv')
with open(filename, 'w') as f:
w = csv.DictWriter(f, ['bench'] + strategies)
w.writeheader()
line = {}
for bench_name, data in report.items():
line['bench'] = bench_name
for strategy_name, strategy_value in data.items():
line[strategy_name] = strategy_value
w.writerow(line)
# Statistics
filename = filename.replace('.csv', '.stat')
stats = {}
for strategy, values in statistics.items():
stats[strategy] = {
'min': float(np.min(values)),
'max': float(np.max(values)),
'mean': float(np.mean(values)),
'median': float(np.median(values)),
'1quantile': float(np.quantile(values, 0.25)),
'2quantile': float(np.quantile(values, 0.50)),
'3quantile': float(np.quantile(values, 0.75))
}
IO.dump_yaml(stats, filename)
def run(self, training_benchmarks, compiler, baseline, k):
"""
Best-k
Fast and effective orchestration of compiler optimizations
for automatic performance tuning
Z. Pan and R. Eigenmann
International Symposium on Code Generation and Optimization
2006
10.1109/CGO.2006.38
Argument
--------
training_benchmarks : list
compiler : str
baseline : str
k : int
Number of sequences
"""
# Create the dictionary
dictionary = {}
best_sequences = {}
for training_benchmark in training_benchmarks:
index = training_benchmark.find('.')
bench_dir = training_benchmark[:index]
bench_name = training_benchmark[index + 1:]
training_dir = os.path.join(self.__flags.training_directory,
bench_dir)
baseline_dir = os.path.join(self.__flags.baseline_directory,
bench_dir)
training_sequences = IO.load_yaml('{}/{}.yaml'.format(
training_dir, bench_name))
if not training_sequences:
continue
baseline_goal_value = IO.load_yaml_or_fail('{}/{}.yaml'.format(
baseline_dir, bench_name))
baseline_goal_value = baseline_goal_value[compiler][baseline][
'goal']
# For each sequence
for seq in training_sequences.keys():
if seq not in dictionary.keys():
dictionary[seq] = []
best_sequences[seq] = training_sequences[seq]['seq']
goal_value = training_sequences[seq]['goal']
# Store the fitness
if goal_value < baseline_goal_value:
improvement = ((baseline_goal_value - goal_value) /
baseline_goal_value) * 100
dictionary[seq].append((training_benchmark, improvement))
# Find the best dictionary entries
if dictionary:
bestk = []
self.__covering = {}
for _ in range(k):
progs = []
for _, data in dictionary.items():
progs += [p for p, _ in data if p not in progs]
if len(progs) == 0:
break
key = self.__get_maximum(dictionary)
dictionary_entry = dictionary[key].copy()
self.__covering[key] = len(dictionary_entry)
bestk.append(key)
for key, data in dictionary.items():
for program, improvement in dictionary_entry:
index = self.__program_in_dictionary(program, data)
if index > -1:
del dictionary[key][index]
# Store the best k sequences
self.__results = {}
for best in bestk:
self.__results[best] = {'x': best_sequences[best]}
