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choelzl
2022-04-07 18:46:57 +02:00
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#!/usr/bin/env python
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from tbb import *
from tbb import __all__, __doc__
if __name__ == "__main__":
from tbb import _main
import sys
sys.exit(_main())

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<HTML>
<BODY>
<H2>Python* API for Intel&reg; Threading Building Blocks (Intel&reg; TBB).
</H2>
<H2>Overview</H2>
It is a preview Python* module which unlocks opportunities for additional performance in multi-threaded and multiprocess Python programs by enabling threading composability
between two or more thread-enabled libraries like Numpy, Scipy, Sklearn, Dask, Joblib, and etc.
<p></p>
The biggest improvement can be achieved when a task pool like the ThreadPool or Pool from the Python standard library or libraries like Dask or Joblib (used either in multi-threading or multi-processing mode)
execute tasks calling compute-intensive functions of Numpy/Scipy/Sklearn/PyDAAL which in turn are parallelized using Intel&reg; Math Kernel Library or/and Intel&reg; TBB.
<p></p>
The module implements Pool class with the standard interface using Intel&reg; TBB which can be used to replace Python's ThreadPool.
Thanks to the monkey-patching technique implemented in class Monkey, no source code change is needed in order to enable threading composability in Python programs.
<p></p>
For more information and examples, please refer to <A HREF="http://software.intel.com/en-us/blogs/2016/04/04/unleash-parallel-performance-of-python-programs">online blog</A>.
<H2>Directories</H2>
<DL>
<DT><A HREF="rml">rml</A>
<DD>The folder contains sources for building the plugin with cross-process dynamic thread scheduler implementation.
<DT><A HREF="tbb">tbb</A>
<DD>The folder contains Python module sources.
</DL>
<H2>Files</H2>
<DL>
<DT><A HREF="setup.py">setup.py</A>
<DD>Standard Python setup script.
<DT><A HREF="Makefile">Makefile</A>
<DD>Internal Makefile for building, installing, and testing. See below.
<DT><A HREF="TBB.py">TBB.py</A>
<DD>Alternative entry point for Python module.
</DL>
<A NAME=build><H2>Build and install (source package only)</H2></A>
For accessing targets defined in python/Makefile, please use
<A HREF="../src/index.html">src/Makefile</A>
instead and build runtime libraries before working with Python.
<DL>
<DT><TT>make -C ../src python_all</TT>
<DD>Install and test as described below.
<DT><TT>make -C ../src python_install</TT>
<DD>Install module into Python environment.
<DT><TT>make -C ../src python_test</TT>
<DD>Test installed Intel&reg; TBB module for Python.
<DT><TT>make -C ../src python_release</TT>
<DD>Recompile Python module. Result is located in Intel&reg; TBB build directory.
<DT><TT>make -C ../src python_clean</TT>
<DD>Remove any intermediate files produced by the commands above. Does not remove installed module.
</DL>
<H2>Command-line interface</H2>
<DL>
<DT><TT>python -m tbb -h</TT>
<DD>Print documentation on command-line interface</DD>
<DT><TT>pydoc tbb</TT>
<DD>Read built-in documentation for Python interfaces.</DD>
<DT><TT>python-tbb your_script.py</TT>
<DT><TT>python -m tbb your_script.py</TT>
<DD>Run your_script.py in context of `with tbb.Monkey():` when Intel&reg; TBB is enabled. By default only multi-threading will be covered.</DD>
<DT><TT>python -m tbb --ipc your_script.py</TT>
<DD>Run your_script.py in context of `with tbb.Monkey():` when Intel&reg; TBB enabled in both multi-threading and multi-processing modes.</DD>
</DL>
<H2>System Requirements</H2>
The Python module was not tested on older versions of Python thus we require at least Python versions 2.7 and 3.5 or higher.<BR>
SWIG must be of version 3.0.6 or higher<BR>
OS versions:
Microsoft* Windows* Server 2012,
Microsoft* Windows* 10,
Ubuntu* 14.04 LTS,
Red Hat* Enterprise Linux* 7.
<HR>
<A href="../index.html">Up to parent directory</A>
<p></p>
Copyright &copy; 2016-2020 Intel Corporation. All Rights Reserved.
<P></P>
Intel is a registered trademark or trademark of Intel Corporation
or its subsidiaries in the United States and other countries.
<p></p>
* Other names and brands may be claimed as the property of others.
</BODY>
</HTML>

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/*
Copyright (c) 2017-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "ipc_utils.h"
#include <stdlib.h>
#include <stdio.h>
#include <limits.h>
#include <string.h>
#include <unistd.h>
namespace tbb {
namespace internal {
namespace rml {
#define MAX_STR_LEN 255
#define STARTTIME_ITEM_ID 21
static char* get_stat_item(char* line, int item_id) {
int id = 0, i = 0;
while( id!=item_id ) {
while( line[i]!='(' && line[i]!=' ' && line[i]!='\0' ) {
++i;
}
if( line[i]==' ' ) {
++id;
++i;
} else if( line[i]=='(' ) {
while( line[i]!=')' && line[i]!='\0' ) {
++i;
}
if( line[i]==')' ) {
++i;
} else {
return NULL;
}
} else {
return NULL;
}
}
return line + i;
}
unsigned long long get_start_time(int pid) {
const char* stat_file_path_template = "/proc/%d/stat";
char stat_file_path[MAX_STR_LEN + 1];
sprintf( stat_file_path, stat_file_path_template, pid );
FILE* stat_file = fopen( stat_file_path, "rt" );
if( stat_file==NULL ) {
return 0;
}
char stat_line[MAX_STR_LEN + 1];
char* line = fgets( stat_line, MAX_STR_LEN, stat_file );
if( line==NULL ) {
return 0;
}
char* starttime_str = get_stat_item( stat_line, STARTTIME_ITEM_ID );
if( starttime_str==NULL ) {
return 0;
}
unsigned long long starttime = strtoull( starttime_str, NULL, 10 );
if( starttime==ULLONG_MAX ) {
return 0;
}
return starttime;
}
char* get_shared_name(const char* prefix, int pid, unsigned long long time) {
const char* name_template = "%s_%d_%llu";
const int digits_in_int = 10;
const int digits_in_long = 20;
int len = strlen( name_template ) + strlen( prefix ) + digits_in_int + digits_in_long + 1;
char* name = new char[len];
sprintf( name, name_template, prefix, pid, time );
return name;
}
char* get_shared_name(const char* prefix) {
int pid = getpgrp();
unsigned long long time = get_start_time( pid );
return get_shared_name( prefix, pid, time );
}
int get_num_threads(const char* env_var) {
if( env_var==NULL ) {
return 0;
}
char* value = getenv( env_var );
if( value==NULL ) {
return 0;
}
int num_threads = (int)strtol( value, NULL, 10 );
return num_threads;
}
bool get_enable_flag(const char* env_var) {
if( env_var==NULL ) {
return false;
}
char* value = getenv( env_var );
if( value==NULL ) {
return false;
}
if( strcmp( value, "0" ) == 0 ||
strcmp( value, "false" ) == 0 ||
strcmp( value, "False" ) == 0 ||
strcmp( value, "FALSE" ) == 0 ) {
return false;
}
return true;
}
}}} //tbb::internal::rml

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/*
Copyright (c) 2017-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#ifndef __IPC_UTILS_H
#define __IPC_UTILS_H
namespace tbb {
namespace internal {
namespace rml {
char* get_shared_name(const char* prefix);
int get_num_threads(const char* env_var);
bool get_enable_flag(const char* env_var);
}}} //tbb::internal::rml
#endif

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#!/usr/bin/env python
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# System imports
from __future__ import print_function
from glob import glob
import platform
import os
from distutils.core import *
from distutils.command.build import build
rundir = os.getcwd()
os.chdir(os.path.abspath(os.path.dirname(__file__)))
if any(i in os.environ for i in ["CC", "CXX"]):
if "CC" not in os.environ:
os.environ['CC'] = os.environ['CXX']
if "CXX" not in os.environ:
os.environ['CXX'] = os.environ['CC']
if platform.system() == 'Linux':
os.environ['LDSHARED'] = os.environ['CXX'] + " -shared"
print("Environment specifies CC=%s CXX=%s"%(os.environ['CC'], os.environ['CXX']))
intel_compiler = os.getenv('CC', '') in ['icl', 'icpc', 'icc']
try:
tbb_root = os.environ['TBBROOT']
print("Using TBBROOT=", tbb_root)
except:
tbb_root = '..'
if not intel_compiler:
print("Warning: TBBROOT env var is not set and Intel's compiler is not used. It might lead\n"
" !!!: to compile/link problems. Source tbbvars.sh/.csh file to set environment")
use_compiler_tbb = intel_compiler and tbb_root == '..'
if use_compiler_tbb:
print("Using Intel TBB from Intel's compiler")
if platform.system() == 'Windows':
if intel_compiler:
os.environ['DISTUTILS_USE_SDK'] = '1' # Enable environment settings in distutils
os.environ['MSSdk'] = '1'
print("Using compiler settings from environment")
tbb_flag = ['/Qtbb'] if use_compiler_tbb else []
tbb_flag += ['/EHsc'] # for Python 2
compile_flags = ['/Qstd=c++11'] if intel_compiler else []
else:
tbb_flag = ['-tbb'] if use_compiler_tbb else []
compile_flags = ['-std=c++11', '-Wno-unused-variable']
_tbb = Extension("tbb._api", ["tbb/api.i"],
include_dirs=[os.path.join(tbb_root, 'include')] if not use_compiler_tbb else [],
swig_opts =['-c++', '-O', '-threads'] + ( # add '-builtin' later
['-I' + os.path.join(tbb_root, 'include')] if not use_compiler_tbb else []),
extra_compile_args=compile_flags + tbb_flag,
extra_link_args=tbb_flag,
libraries =(['tbb'] if not use_compiler_tbb else []) +
(['irml'] if platform.system() == "Linux" else []), # TODO: why do we need this?
library_dirs=[ rundir, # for custom-builds
os.path.join(tbb_root, 'lib', 'intel64', 'gcc4.8'), # for Linux
os.path.join(tbb_root, 'lib'), # for MacOS
os.path.join(tbb_root, 'lib', 'intel64', 'vc_mt'), # for Windows
] if not use_compiler_tbb else [],
language ='c++',
)
class TBBBuild(build):
sub_commands = [ # define build order
('build_ext', build.has_ext_modules),
('build_py', build.has_pure_modules),
]
setup( name ="TBB",
description ="Python API for Intel TBB",
long_description="Python API to Intel(R) Threading Building Blocks library (Intel(R) TBB) "
"extended with standard Pool implementation and monkey-patching",
url ="https://software.intel.com/en-us/intel-tbb",
author ="Intel Corporation",
author_email="inteltbbdevelopers@intel.com",
license ="Dual license: Apache or Proprietary",
version ="0.1",
classifiers =[
'Development Status :: 4 - Beta',
'Environment :: Console',
'Environment :: Plugins',
'Intended Audience :: Developers',
'Intended Audience :: System Administrators',
'Intended Audience :: Other Audience',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: Apache Software License',
'Operating System :: MacOS :: MacOS X',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX :: Linux',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Programming Language :: C++',
'Topic :: System :: Hardware :: Symmetric Multi-processing',
'Topic :: Software Development :: Libraries',
],
keywords='TBB multiprocessing multithreading composable parallelism',
ext_modules=[_tbb],
packages=['tbb'],
py_modules=['TBB'],
cmdclass={'build': TBBBuild}
)

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#!/usr/bin/env python
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import multiprocessing.pool
import ctypes
import atexit
import sys
import os
from .api import *
from .api import __all__ as api__all
from .pool import *
from .pool import __all__ as pool__all
__all__ = ["Monkey", "is_active"] + api__all + pool__all
__doc__ = """
Python API for Intel(R) Threading Building Blocks library (Intel(R) TBB)
extended with standard Python's pools implementation and monkey-patching.
Command-line interface example:
$ python -m tbb $your_script.py
Runs your_script.py in context of tbb.Monkey
"""
is_active = False
""" Indicates whether TBB context is activated """
ipc_enabled = False
""" Indicates whether IPC mode is enabled """
libirml = "libirml.so.1"
def _test(arg=None):
"""Some tests"""
import platform
if platform.system() == "Linux":
ctypes.CDLL(libirml)
assert 256 == os.system("ldd "+_api.__file__+"| grep -E 'libimf|libsvml|libintlc'")
from .test import test
test(arg)
print("done")
def tbb_process_pool_worker27(inqueue, outqueue, initializer=None, initargs=(),
maxtasks=None):
from multiprocessing.pool import worker
worker(inqueue, outqueue, initializer, initargs, maxtasks)
if ipc_enabled:
try:
librml = ctypes.CDLL(libirml)
librml.release_resources()
except:
print("Warning: Can not load ", libirml, file=sys.stderr)
class TBBProcessPool27(multiprocessing.pool.Pool):
def _repopulate_pool(self):
"""Bring the number of pool processes up to the specified number,
for use after reaping workers which have exited.
"""
from multiprocessing.util import debug
for i in range(self._processes - len(self._pool)):
w = self.Process(target=tbb_process_pool_worker27,
args=(self._inqueue, self._outqueue,
self._initializer,
self._initargs, self._maxtasksperchild)
)
self._pool.append(w)
w.name = w.name.replace('Process', 'PoolWorker')
w.daemon = True
w.start()
debug('added worker')
def __del__(self):
self.close()
for p in self._pool:
p.join()
def __exit__(self, *args):
self.close()
for p in self._pool:
p.join()
def tbb_process_pool_worker3(inqueue, outqueue, initializer=None, initargs=(),
maxtasks=None, wrap_exception=False):
from multiprocessing.pool import worker
worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
if ipc_enabled:
try:
librml = ctypes.CDLL(libirml)
librml.release_resources()
except:
print("Warning: Can not load ", libirml, file=sys.stderr)
class TBBProcessPool3(multiprocessing.pool.Pool):
def _repopulate_pool(self):
"""Bring the number of pool processes up to the specified number,
for use after reaping workers which have exited.
"""
from multiprocessing.util import debug
for i in range(self._processes - len(self._pool)):
w = self.Process(target=tbb_process_pool_worker3,
args=(self._inqueue, self._outqueue,
self._initializer,
self._initargs, self._maxtasksperchild,
self._wrap_exception)
)
self._pool.append(w)
w.name = w.name.replace('Process', 'PoolWorker')
w.daemon = True
w.start()
debug('added worker')
def __del__(self):
self.close()
for p in self._pool:
p.join()
def __exit__(self, *args):
self.close()
for p in self._pool:
p.join()
class Monkey:
"""
Context manager which replaces standard multiprocessing.pool
implementations with tbb.pool using monkey-patching. It also enables TBB
threading for Intel(R) Math Kernel Library (Intel(R) MKL). For example:
with tbb.Monkey():
run_my_numpy_code()
It allows multiple parallel tasks to be executed on the same thread pool
and coordinate number of threads across multiple processes thus avoiding
overheads from oversubscription.
"""
_items = {}
_modules = {}
def __init__(self, max_num_threads=None, benchmark=False):
"""
Create context manager for running under TBB scheduler.
:param max_num_threads: if specified, limits maximal number of threads
:param benchmark: if specified, blocks in initialization until requested number of threads are ready
"""
if max_num_threads:
self.ctl = global_control(global_control.max_allowed_parallelism, int(max_num_threads))
if benchmark:
if not max_num_threads:
max_num_threads = default_num_threads()
from .api import _concurrency_barrier
_concurrency_barrier(int(max_num_threads))
def _patch(self, class_name, module_name, obj):
m = self._modules[class_name] = __import__(module_name, globals(),
locals(), [class_name])
if m == None:
return
oldattr = getattr(m, class_name, None)
if oldattr == None:
self._modules[class_name] = None
return
self._items[class_name] = oldattr
setattr(m, class_name, obj)
def __enter__(self):
global is_active
assert is_active == False, "tbb.Monkey does not support nesting yet"
is_active = True
self.env_mkl = os.getenv('MKL_THREADING_LAYER')
os.environ['MKL_THREADING_LAYER'] = 'TBB'
self.env_numba = os.getenv('NUMBA_THREADING_LAYER')
os.environ['NUMBA_THREADING_LAYER'] = 'TBB'
if ipc_enabled:
if sys.version_info.major == 2 and sys.version_info.minor >= 7:
self._patch("Pool", "multiprocessing.pool", TBBProcessPool27)
elif sys.version_info.major == 3 and sys.version_info.minor >= 5:
self._patch("Pool", "multiprocessing.pool", TBBProcessPool3)
self._patch("ThreadPool", "multiprocessing.pool", Pool)
return self
def __exit__(self, exc_type, exc_value, traceback):
global is_active
assert is_active == True, "modified?"
is_active = False
if self.env_mkl is None:
del os.environ['MKL_THREADING_LAYER']
else:
os.environ['MKL_THREADING_LAYER'] = self.env_mkl
if self.env_numba is None:
del os.environ['NUMBA_THREADING_LAYER']
else:
os.environ['NUMBA_THREADING_LAYER'] = self.env_numba
for name in self._items.keys():
setattr(self._modules[name], name, self._items[name])
def init_sem_name():
try:
librml = ctypes.CDLL(libirml)
librml.set_active_sem_name()
librml.set_stop_sem_name()
except Exception as e:
print("Warning: Can not initialize name of shared semaphores:", e,
file=sys.stderr)
def tbb_atexit():
if ipc_enabled:
try:
librml = ctypes.CDLL(libirml)
librml.release_semaphores()
except:
print("Warning: Can not release shared semaphores",
file=sys.stderr)
def _main():
# Run the module specified as the next command line argument
# python -m TBB user_app.py
global ipc_enabled
import platform
import argparse
parser = argparse.ArgumentParser(prog="python -m tbb", description="""
Run your Python script in context of tbb.Monkey, which
replaces standard Python pools and threading layer of
Intel(R) Math Kernel Library by implementation based on
Intel(R) Threading Building Blocks. It enables multiple parallel
tasks to be executed on the same thread pool and coordinate
number of threads across multiple processes thus avoiding
overheads from oversubscription.
""", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
if platform.system() == "Linux":
parser.add_argument('--ipc', action='store_true',
help="Enable inter-process (IPC) coordination between Intel TBB schedulers")
parser.add_argument('-a', '--allocator', action='store_true',
help="Enable Intel TBB scalable allocator as a replacement for standard memory allocator")
parser.add_argument('--allocator-huge-pages', action='store_true',
help="Enable huge pages for Intel TBB allocator (implies: -a)")
parser.add_argument('-p', '--max-num-threads', default=default_num_threads(), type=int,
help="Initialize Intel TBB with P max number of threads per process", metavar='P')
parser.add_argument('-b', '--benchmark', action='store_true',
help="Block Intel TBB initialization until all the threads are created before continue the script. "
"This is necessary for performance benchmarks that want to exclude lazy scheduler initialization effects from the measurements")
parser.add_argument('-v', '--verbose', action='store_true',
help="Request verbose and version information")
parser.add_argument('-m', action='store_true', dest='module',
help="Executes following as a module")
parser.add_argument('name', help="Script or module name")
parser.add_argument('args', nargs=argparse.REMAINDER,
help="Command line arguments")
args = parser.parse_args()
if args.verbose:
os.environ["TBB_VERSION"] = "1"
if platform.system() == "Linux":
if args.allocator_huge_pages:
args.allocator = True
if args.allocator and not os.environ.get("_TBB_MALLOC_PRELOAD"):
libtbbmalloc_lib = 'libtbbmalloc_proxy.so.2'
ld_preload = 'LD_PRELOAD'
os.environ["_TBB_MALLOC_PRELOAD"] = "1"
preload_list = filter(None, os.environ.get(ld_preload, "").split(':'))
if libtbbmalloc_lib in preload_list:
print('Info:', ld_preload, "contains", libtbbmalloc_lib, "already\n")
else:
os.environ[ld_preload] = ':'.join([libtbbmalloc_lib] + list(preload_list))
if args.allocator_huge_pages:
assert platform.system() == "Linux"
try:
with open('/proc/sys/vm/nr_hugepages', 'r') as f:
pages = int(f.read())
if pages == 0:
print("TBB: Pre-allocated huge pages are not currently reserved in the system. To reserve, run e.g.:\n"
"\tsudo sh -c 'echo 2000 > /proc/sys/vm/nr_hugepages'")
os.environ["TBB_MALLOC_USE_HUGE_PAGES"] = "1"
except:
print("TBB: Failed to read number of pages from /proc/sys/vm/nr_hugepages\n"
"\tIs the Linux kernel configured with the huge pages feature?")
sys.exit(1)
os.execl(sys.executable, sys.executable, '-m', 'tbb', *sys.argv[1:])
assert False, "Re-execution failed"
sys.argv = [args.name] + args.args
ipc_enabled = platform.system() == "Linux" and args.ipc
os.environ["IPC_ENABLE"] = "1" if ipc_enabled else "0"
if ipc_enabled:
atexit.register(tbb_atexit)
init_sem_name()
if not os.environ.get("KMP_BLOCKTIME"): # TODO move
os.environ["KMP_BLOCKTIME"] = "0"
if '_' + args.name in globals():
return globals()['_' + args.name](*args.args)
else:
import runpy
runf = runpy.run_module if args.module else runpy.run_path
with Monkey(max_num_threads=args.max_num_threads, benchmark=args.benchmark):
runf(args.name, run_name='__main__')

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#!/usr/bin/env python
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import _main
from sys import exit
exit(_main())

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%pythonbegin %{
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["task_arena", "task_group", "task_scheduler_init", "global_control", "default_num_threads"]
%}
%begin %{
/* Defines Python wrappers for Intel(R) Threading Building Blocks (Intel TBB).*/
%}
%module api
#if SWIG_VERSION < 0x030001
#error SWIG version 3.0.6 or newer is required for correct functioning
#endif
%{
#define TBB_PREVIEW_WAITING_FOR_WORKERS 1
#include <tbb/tbb.h>
#include <tbb/compat/condition_variable>
#if TBB_IMPLEMENT_CPP0X
namespace std { using tbb::mutex; }
#define unique_ptr auto_ptr
#else
#include <condition_variable>
#include <mutex>
#include <memory>
#endif
using namespace tbb;
class PyCaller : public swig::SwigPtr_PyObject {
public:
// icpc 2013 does not support simple using SwigPtr_PyObject::SwigPtr_PyObject;
PyCaller(const PyCaller& s) : SwigPtr_PyObject(s) {}
PyCaller(PyObject *p, bool initial = true) : SwigPtr_PyObject(p, initial) {}
void operator()() const {
SWIG_PYTHON_THREAD_BEGIN_BLOCK;
PyObject* r = PyObject_CallFunctionObjArgs((PyObject*)*this, NULL);
if(r) Py_DECREF(r);
SWIG_PYTHON_THREAD_END_BLOCK;
}
};
struct ArenaPyCaller {
task_arena *my_arena;
PyObject *my_callable;
ArenaPyCaller(task_arena *a, PyObject *c) : my_arena(a), my_callable(c) {
SWIG_PYTHON_THREAD_BEGIN_BLOCK;
Py_XINCREF(c);
SWIG_PYTHON_THREAD_END_BLOCK;
}
void operator()() const {
my_arena->execute(PyCaller(my_callable, false));
}
};
struct barrier_data {
std::condition_variable event;
std::mutex m;
int worker_threads, full_threads;
};
class barrier_task : public tbb::task {
barrier_data &b;
public:
barrier_task(barrier_data &d) : b(d) {}
/*override*/ tbb::task *execute() {
std::unique_lock<std::mutex> lock(b.m);
if(++b.worker_threads >= b.full_threads)
b.event.notify_all();
else while(b.worker_threads < b.full_threads)
b.event.wait(lock);
return NULL;
}
};
void _concurrency_barrier(int threads = tbb::task_scheduler_init::automatic) {
if(threads == task_scheduler_init::automatic)
threads = task_scheduler_init::default_num_threads();
if(threads < 2)
return;
std::unique_ptr<global_control> g(
(global_control::active_value(global_control::max_allowed_parallelism) < unsigned(threads))?
new global_control(global_control::max_allowed_parallelism, threads) : NULL);
barrier_data b;
b.worker_threads = 0;
b.full_threads = threads-1;
for(int i = 0; i < b.full_threads; i++)
tbb::task::enqueue( *new( tbb::task::allocate_root() ) barrier_task(b) );
std::unique_lock<std::mutex> lock(b.m);
b.event.wait(lock);
};
%}
void _concurrency_barrier(int threads = tbb::task_scheduler_init::automatic);
namespace tbb {
class task_scheduler_init {
public:
//! Typedef for number of threads that is automatic.
static const int automatic = -1;
//! Argument to initialize() or constructor that causes initialization to be deferred.
static const int deferred = -2;
task_scheduler_init( int max_threads=automatic,
size_t thread_stack_size=0 );
~task_scheduler_init();
void initialize( int max_threads=automatic );
void terminate();
static int default_num_threads();
bool is_active() const;
void blocking_terminate();
};
class task_arena {
public:
static const int automatic = -1;
static int current_thread_index();
task_arena(int max_concurrency = automatic, unsigned reserved_for_masters = 1);
task_arena(const task_arena &s);
~task_arena();
void initialize();
void initialize(int max_concurrency, unsigned reserved_for_masters = 1);
void terminate();
bool is_active();
%extend {
void enqueue( PyObject *c ) { $self->enqueue(PyCaller(c)); }
void execute( PyObject *c ) { $self->execute(PyCaller(c)); }
};
};
class task_group {
public:
task_group();
~task_group();
void wait();
bool is_canceling();
void cancel();
%extend {
void run( PyObject *c ) { $self->run(PyCaller(c)); }
void run( PyObject *c, task_arena *a ) { $self->run(ArenaPyCaller(a, c)); }
};
};
class global_control {
public:
enum parameter {
max_allowed_parallelism,
thread_stack_size,
parameter_max // insert new parameters above this point
};
global_control(parameter param, size_t value);
~global_control();
static size_t active_value(parameter param);
};
} // tbb
// Additional definitions for Python part of the module
%pythoncode %{
default_num_threads = task_scheduler_init_default_num_threads
%}

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#!/usr/bin/env python
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Based on the software developed by:
# Copyright (c) 2008,2016 david decotigny (Pool of threads)
# Copyright (c) 2006-2008, R Oudkerk (multiprocessing.Pool)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of author nor the names of any contributors may be
# used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGE.
#
# @brief Python Pool implementation based on TBB with monkey-patching
#
# See http://docs.python.org/dev/library/multiprocessing.html
# Differences: added imap_async and imap_unordered_async, and terminate()
# has to be called explicitly (it's not registered by atexit).
#
# The general idea is that we submit works to a workqueue, either as
# single Jobs (one function to call), or JobSequences (batch of
# Jobs). Each Job is associated with an ApplyResult object which has 2
# states: waiting for the Job to complete, or Ready. Instead of
# waiting for the jobs to finish, we wait for their ApplyResult object
# to become ready: an event mechanism is used for that.
# When we apply a function to several arguments in "parallel", we need
# a way to wait for all/part of the Jobs to be processed: that's what
# "collectors" are for; they group and wait for a set of ApplyResult
# objects. Once a collector is ready to be used, we can use a
# CollectorIterator to iterate over the result values it's collecting.
#
# The methods of a Pool object use all these concepts and expose
# them to their caller in a very simple way.
import sys
import threading
import traceback
from .api import *
__all__ = ["Pool", "TimeoutError"]
__doc__ = """
Standard Python Pool implementation based on Python API
for Intel(R) Threading Building Blocks library (Intel(R) TBB)
"""
class TimeoutError(Exception):
"""Raised when a result is not available within the given timeout"""
pass
class Pool(object):
"""
The Pool class provides standard multiprocessing.Pool interface
which is mapped onto Intel(R) TBB tasks executing in its thread pool
"""
def __init__(self, nworkers=0, name="Pool"):
"""
\param nworkers (integer) number of worker threads to start
\param name (string) prefix for the worker threads' name
"""
self._closed = False
self._tasks = task_group()
self._pool = [None,]*default_num_threads() # Dask asks for len(_pool)
def apply(self, func, args=(), kwds=dict()):
"""Equivalent of the apply() builtin function. It blocks till
the result is ready."""
return self.apply_async(func, args, kwds).get()
def map(self, func, iterable, chunksize=None):
"""A parallel equivalent of the map() builtin function. It
blocks till the result is ready.
This method chops the iterable into a number of chunks which
it submits to the process pool as separate tasks. The
(approximate) size of these chunks can be specified by setting
chunksize to a positive integer."""
return self.map_async(func, iterable, chunksize).get()
def imap(self, func, iterable, chunksize=1):
"""
An equivalent of itertools.imap().
The chunksize argument is the same as the one used by the
map() method. For very long iterables using a large value for
chunksize can make the job complete much faster than
using the default value of 1.
Also if chunksize is 1 then the next() method of the iterator
returned by the imap() method has an optional timeout
parameter: next(timeout) will raise processing.TimeoutError if
the result cannot be returned within timeout seconds.
"""
collector = OrderedResultCollector(as_iterator=True)
self._create_sequences(func, iterable, chunksize, collector)
return iter(collector)
def imap_unordered(self, func, iterable, chunksize=1):
"""The same as imap() except that the ordering of the results
from the returned iterator should be considered
arbitrary. (Only when there is only one worker process is the
order guaranteed to be "correct".)"""
collector = UnorderedResultCollector()
self._create_sequences(func, iterable, chunksize, collector)
return iter(collector)
def apply_async(self, func, args=(), kwds=dict(), callback=None):
"""A variant of the apply() method which returns an
ApplyResult object.
If callback is specified then it should be a callable which
accepts a single argument. When the result becomes ready,
callback is applied to it (unless the call failed). callback
should complete immediately since otherwise the thread which
handles the results will get blocked."""
assert not self._closed # No lock here. We assume it's atomic...
apply_result = ApplyResult(callback=callback)
job = Job(func, args, kwds, apply_result)
self._tasks.run(job)
return apply_result
def map_async(self, func, iterable, chunksize=None, callback=None):
"""A variant of the map() method which returns a ApplyResult
object.
If callback is specified then it should be a callable which
accepts a single argument. When the result becomes ready
callback is applied to it (unless the call failed). callback
should complete immediately since otherwise the thread which
handles the results will get blocked."""
apply_result = ApplyResult(callback=callback)
collector = OrderedResultCollector(apply_result, as_iterator=False)
if not self._create_sequences(func, iterable, chunksize, collector):
apply_result._set_value([])
return apply_result
def imap_async(self, func, iterable, chunksize=None, callback=None):
"""A variant of the imap() method which returns an ApplyResult
object that provides an iterator (next method(timeout)
available).
If callback is specified then it should be a callable which
accepts a single argument. When the resulting iterator becomes
ready, callback is applied to it (unless the call
failed). callback should complete immediately since otherwise
the thread which handles the results will get blocked."""
apply_result = ApplyResult(callback=callback)
collector = OrderedResultCollector(apply_result, as_iterator=True)
if not self._create_sequences(func, iterable, chunksize, collector):
apply_result._set_value(iter([]))
return apply_result
def imap_unordered_async(self, func, iterable, chunksize=None,
callback=None):
"""A variant of the imap_unordered() method which returns an
ApplyResult object that provides an iterator (next
method(timeout) available).
If callback is specified then it should be a callable which
accepts a single argument. When the resulting iterator becomes
ready, callback is applied to it (unless the call
failed). callback should complete immediately since otherwise
the thread which handles the results will get blocked."""
apply_result = ApplyResult(callback=callback)
collector = UnorderedResultCollector(apply_result)
if not self._create_sequences(func, iterable, chunksize, collector):
apply_result._set_value(iter([]))
return apply_result
def close(self):
"""Prevents any more tasks from being submitted to the
pool. Once all the tasks have been completed the worker
processes will exit."""
# No lock here. We assume it's sufficiently atomic...
self._closed = True
def terminate(self):
"""Stops the worker processes immediately without completing
outstanding work. When the pool object is garbage collected
terminate() will be called immediately."""
self.close()
self._tasks.cancel()
def join(self):
"""Wait for the worker processes to exit. One must call
close() or terminate() before using join()."""
self._tasks.wait()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.join()
def __del__(self):
self.terminate()
self.join()
def _create_sequences(self, func, iterable, chunksize, collector):
"""
Create callable objects to process and pushes them on the
work queue. Each work unit is meant to process a slice of
iterable of size chunksize. If collector is specified, then
the ApplyResult objects associated with the jobs will notify
collector when their result becomes ready.
\return the list callable objects (basically: JobSequences)
pushed onto the work queue
"""
assert not self._closed # No lock here. We assume it's atomic...
it_ = iter(iterable)
exit_loop = False
sequences = []
while not exit_loop:
seq = []
for _ in range(chunksize or 1):
try:
arg = next(it_)
except StopIteration:
exit_loop = True
break
apply_result = ApplyResult(collector)
job = Job(func, (arg,), {}, apply_result)
seq.append(job)
if seq:
sequences.append(JobSequence(seq))
for t in sequences:
self._tasks.run(t)
return sequences
class Job:
"""A work unit that corresponds to the execution of a single function"""
def __init__(self, func, args, kwds, apply_result):
"""
\param func/args/kwds used to call the function
\param apply_result ApplyResult object that holds the result
of the function call
"""
self._func = func
self._args = args
self._kwds = kwds
self._result = apply_result
def __call__(self):
"""
Call the function with the args/kwds and tell the ApplyResult
that its result is ready. Correctly handles the exceptions
happening during the execution of the function
"""
try:
result = self._func(*self._args, **self._kwds)
except:
self._result._set_exception()
else:
self._result._set_value(result)
class JobSequence:
"""A work unit that corresponds to the processing of a continuous
sequence of Job objects"""
def __init__(self, jobs):
self._jobs = jobs
def __call__(self):
"""
Call all the Job objects that have been specified
"""
for job in self._jobs:
job()
class ApplyResult(object):
"""An object associated with a Job object that holds its result:
it's available during the whole life the Job and after, even when
the Job didn't process yet. It's possible to use this object to
wait for the result/exception of the job to be available.
The result objects returns by the Pool::*_async() methods are of
this type"""
def __init__(self, collector=None, callback=None):
"""
\param collector when not None, the notify_ready() method of
the collector will be called when the result from the Job is
ready
\param callback when not None, function to call when the
result becomes available (this is the parameter passed to the
Pool::*_async() methods.
"""
self._success = False
self._event = threading.Event()
self._data = None
self._collector = None
self._callback = callback
if collector is not None:
collector.register_result(self)
self._collector = collector
def get(self, timeout=None):
"""
Returns the result when it arrives. If timeout is not None and
the result does not arrive within timeout seconds then
TimeoutError is raised. If the remote call raised an exception
then that exception will be reraised by get().
"""
if not self.wait(timeout):
raise TimeoutError("Result not available within %fs" % timeout)
if self._success:
return self._data
if sys.version_info[0] == 3:
raise self._data[0](self._data[1]).with_traceback(self._data[2])
else:
exec("raise self._data[0], self._data[1], self._data[2]")
def wait(self, timeout=None):
"""Waits until the result is available or until timeout
seconds pass."""
self._event.wait(timeout)
return self._event.isSet()
def ready(self):
"""Returns whether the call has completed."""
return self._event.isSet()
def successful(self):
"""Returns whether the call completed without raising an
exception. Will raise AssertionError if the result is not
ready."""
assert self.ready()
return self._success
def _set_value(self, value):
"""Called by a Job object to tell the result is ready, and
provides the value of this result. The object will become
ready and successful. The collector's notify_ready() method
will be called, and the callback method too"""
assert not self.ready()
self._data = value
self._success = True
self._event.set()
if self._collector is not None:
self._collector.notify_ready(self)
if self._callback is not None:
try:
self._callback(value)
except:
traceback.print_exc()
def _set_exception(self):
"""Called by a Job object to tell that an exception occurred
during the processing of the function. The object will become
ready but not successful. The collector's notify_ready()
method will be called, but NOT the callback method"""
# traceback.print_exc()
assert not self.ready()
self._data = sys.exc_info()
self._success = False
self._event.set()
if self._collector is not None:
self._collector.notify_ready(self)
class AbstractResultCollector(object):
"""ABC to define the interface of a ResultCollector object. It is
basically an object which knows whuich results it's waiting for,
and which is able to get notify when they get available. It is
also able to provide an iterator over the results when they are
available"""
def __init__(self, to_notify):
"""
\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
"""
self._to_notify = to_notify
def register_result(self, apply_result):
"""Used to identify which results we're waiting for. Will
always be called BEFORE the Jobs get submitted to the work
queue, and BEFORE the __iter__ and _get_result() methods can
be called
\param apply_result ApplyResult object to add in our collection
"""
raise NotImplementedError("Children classes must implement it")
def notify_ready(self, apply_result):
"""Called by the ApplyResult object (already registered via
register_result()) that it is now ready (ie. the Job's result
is available or an exception has been raised).
\param apply_result ApplyResult object telling us that the job
has been processed
"""
raise NotImplementedError("Children classes must implement it")
def _get_result(self, idx, timeout=None):
"""Called by the CollectorIterator object to retrieve the
result's values one after another (order defined by the
implementation)
\param idx The index of the result we want, wrt collector's order
\param timeout integer telling how long to wait (in seconds)
for the result at index idx to be available, or None (wait
forever)
"""
raise NotImplementedError("Children classes must implement it")
def __iter__(self):
"""Return a new CollectorIterator object for this collector"""
return CollectorIterator(self)
class CollectorIterator(object):
"""An iterator that allows to iterate over the result values
available in the given collector object. Equipped with an extended
next() method accepting a timeout argument. Created by the
AbstractResultCollector::__iter__() method"""
def __init__(self, collector):
"""\param AbstractResultCollector instance"""
self._collector = collector
self._idx = 0
def __iter__(self):
return self
def next(self, timeout=None):
"""Return the next result value in the sequence. Raise
StopIteration at the end. Can raise the exception raised by
the Job"""
try:
apply_result = self._collector._get_result(self._idx, timeout)
except IndexError:
# Reset for next time
self._idx = 0
raise StopIteration
except:
self._idx = 0
raise
self._idx += 1
assert apply_result.ready()
return apply_result.get(0)
def __next__(self):
return self.next()
class UnorderedResultCollector(AbstractResultCollector):
"""An AbstractResultCollector implementation that collects the
values of the ApplyResult objects in the order they become ready. The
CollectorIterator object returned by __iter__() will iterate over
them in the order they become ready"""
def __init__(self, to_notify=None):
"""
\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
"""
AbstractResultCollector.__init__(self, to_notify)
self._cond = threading.Condition()
self._collection = []
self._expected = 0
def register_result(self, apply_result):
"""Used to identify which results we're waiting for. Will
always be called BEFORE the Jobs get submitted to the work
queue, and BEFORE the __iter__ and _get_result() methods can
be called
\param apply_result ApplyResult object to add in our collection
"""
self._expected += 1
def _get_result(self, idx, timeout=None):
"""Called by the CollectorIterator object to retrieve the
result's values one after another, in the order the results have
become available.
\param idx The index of the result we want, wrt collector's order
\param timeout integer telling how long to wait (in seconds)
for the result at index idx to be available, or None (wait
forever)
"""
self._cond.acquire()
try:
if idx >= self._expected:
raise IndexError
elif idx < len(self._collection):
return self._collection[idx]
elif idx != len(self._collection):
# Violation of the sequence protocol
raise IndexError()
else:
self._cond.wait(timeout=timeout)
try:
return self._collection[idx]
except IndexError:
# Still not added !
raise TimeoutError("Timeout while waiting for results")
finally:
self._cond.release()
def notify_ready(self, apply_result=None):
"""Called by the ApplyResult object (already registered via
register_result()) that it is now ready (ie. the Job's result
is available or an exception has been raised).
\param apply_result ApplyResult object telling us that the job
has been processed
"""
first_item = False
self._cond.acquire()
try:
self._collection.append(apply_result)
first_item = (len(self._collection) == 1)
self._cond.notifyAll()
finally:
self._cond.release()
if first_item and self._to_notify is not None:
self._to_notify._set_value(iter(self))
class OrderedResultCollector(AbstractResultCollector):
"""An AbstractResultCollector implementation that collects the
values of the ApplyResult objects in the order they have been
submitted. The CollectorIterator object returned by __iter__()
will iterate over them in the order they have been submitted"""
def __init__(self, to_notify=None, as_iterator=True):
"""
\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
\param as_iterator boolean telling whether the result value
set on to_notify should be an iterator (available as soon as 1
result arrived) or a list (available only after the last
result arrived)
"""
AbstractResultCollector.__init__(self, to_notify)
self._results = []
self._lock = threading.Lock()
self._remaining = 0
self._as_iterator = as_iterator
def register_result(self, apply_result):
"""Used to identify which results we're waiting for. Will
always be called BEFORE the Jobs get submitted to the work
queue, and BEFORE the __iter__ and _get_result() methods can
be called
\param apply_result ApplyResult object to add in our collection
"""
self._results.append(apply_result)
self._remaining += 1
def _get_result(self, idx, timeout=None):
"""Called by the CollectorIterator object to retrieve the
result's values one after another (order defined by the
implementation)
\param idx The index of the result we want, wrt collector's order
\param timeout integer telling how long to wait (in seconds)
for the result at index idx to be available, or None (wait
forever)
"""
res = self._results[idx]
res.wait(timeout)
return res
def notify_ready(self, apply_result):
"""Called by the ApplyResult object (already registered via
register_result()) that it is now ready (ie. the Job's result
is available or an exception has been raised).
\param apply_result ApplyResult object telling us that the job
has been processed
"""
got_first = False
got_last = False
self._lock.acquire()
try:
assert self._remaining > 0
got_first = (len(self._results) == self._remaining)
self._remaining -= 1
got_last = (self._remaining == 0)
finally:
self._lock.release()
if self._to_notify is not None:
if self._as_iterator and got_first:
self._to_notify._set_value(iter(self))
elif not self._as_iterator and got_last:
try:
lst = [r.get(0) for r in self._results]
except:
self._to_notify._set_exception()
else:
self._to_notify._set_value(lst)

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cs440-acg/ext/tbb/python/tbb/test.py Normal file
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#!/usr/bin/env python
#
# Copyright (c) 2016-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Based on the software developed by:
# Copyright (c) 2008,2016 david decotigny (Pool of threads)
# Copyright (c) 2006-2008, R Oudkerk (multiprocessing.Pool)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of author nor the names of any contributors may be
# used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGE.
#
from __future__ import print_function
import time
import threading
from .api import *
from .pool import *
def test(arg=None):
if arg == "-v":
def say(*x):
print(*x)
else:
def say(*x):
pass
say("Start Pool testing")
get_tid = lambda: threading.current_thread().ident
def return42():
return 42
def f(x):
return x * x
def work(mseconds):
res = str(mseconds)
if mseconds < 0:
mseconds = -mseconds
say("[%d] Start to work for %fms..." % (get_tid(), mseconds*10))
time.sleep(mseconds/100.)
say("[%d] Work done (%fms)." % (get_tid(), mseconds*10))
return res
### Test copy/pasted from multiprocessing
pool = Pool(4) # start worker threads
# edge cases
assert pool.map(return42, []) == []
assert pool.apply_async(return42, []).get() == 42
assert pool.apply(return42, []) == 42
assert list(pool.imap(return42, iter([]))) == []
assert list(pool.imap_unordered(return42, iter([]))) == []
assert pool.map_async(return42, []).get() == []
assert list(pool.imap_async(return42, iter([])).get()) == []
assert list(pool.imap_unordered_async(return42, iter([])).get()) == []
# basic tests
result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously
assert result.get(timeout=1) == 100 # ... unless slow computer
assert list(pool.map(f, range(10))) == list(map(f, range(10)))
it = pool.imap(f, range(10))
assert next(it) == 0
assert next(it) == 1
assert next(it) == 4
# Test apply_sync exceptions
result = pool.apply_async(time.sleep, (3,))
try:
say(result.get(timeout=1)) # raises `TimeoutError`
except TimeoutError:
say("Good. Got expected timeout exception.")
else:
assert False, "Expected exception !"
assert result.get() is None # sleep() returns None
def cb(s):
say("Result ready: %s" % s)
# Test imap()
assert list(pool.imap(work, range(10, 3, -1), chunksize=4)) == list(map(
str, range(10, 3, -1)))
# Test imap_unordered()
assert sorted(pool.imap_unordered(work, range(10, 3, -1))) == sorted(map(
str, range(10, 3, -1)))
# Test map_async()
result = pool.map_async(work, range(10), callback=cb)
try:
result.get(timeout=0.01) # raises `TimeoutError`
except TimeoutError:
say("Good. Got expected timeout exception.")
else:
assert False, "Expected exception !"
say(result.get())
# Test imap_async()
result = pool.imap_async(work, range(3, 10), callback=cb)
try:
result.get(timeout=0.01) # raises `TimeoutError`
except TimeoutError:
say("Good. Got expected timeout exception.")
else:
assert False, "Expected exception !"
for i in result.get():
say("Item:", i)
say("### Loop again:")
for i in result.get():
say("Item2:", i)
# Test imap_unordered_async()
result = pool.imap_unordered_async(work, range(10, 3, -1), callback=cb)
try:
say(result.get(timeout=0.01)) # raises `TimeoutError`
except TimeoutError:
say("Good. Got expected timeout exception.")
else:
assert False, "Expected exception !"
for i in result.get():
say("Item1:", i)
for i in result.get():
say("Item2:", i)
r = result.get()
for i in r:
say("Item3:", i)
for i in r:
say("Item4:", i)
for i in r:
say("Item5:", i)
#
# The case for the exceptions
#
# Exceptions in imap_unordered_async()
result = pool.imap_unordered_async(work, range(2, -10, -1), callback=cb)
time.sleep(3)
try:
for i in result.get():
say("Got item:", i)
except (IOError, ValueError):
say("Good. Got expected exception")
# Exceptions in imap_async()
result = pool.imap_async(work, range(2, -10, -1), callback=cb)
time.sleep(3)
try:
for i in result.get():
say("Got item:", i)
except (IOError, ValueError):
say("Good. Got expected exception")
# Stop the test: need to stop the pool !!!
pool.terminate()
pool.join()