Source code for numexpr.utils

###################################################################
#  Numexpr - Fast numerical array expression evaluator for NumPy.
#
#      License: MIT
#      Author:  See AUTHORS.txt
#
#  See LICENSE.txt and LICENSES/*.txt for details about copyright and
#  rights to use.
####################################################################

import os
import subprocess

from numexpr.interpreter import _set_num_threads, MAX_THREADS
from numexpr import use_vml

if use_vml:
    from numexpr.interpreter import (
        _get_vml_version, _set_vml_accuracy_mode, _set_vml_num_threads)


[docs]def get_vml_version(): """Get the VML/MKL library version.""" if use_vml: return _get_vml_version() else: return None
[docs]def set_vml_accuracy_mode(mode): """ Set the accuracy mode for VML operations. The `mode` parameter can take the values: - 'high': high accuracy mode (HA), <1 least significant bit - 'low': low accuracy mode (LA), typically 1-2 least significant bits - 'fast': enhanced performance mode (EP) - None: mode settings are ignored This call is equivalent to the `vmlSetMode()` in the VML library. See: http://www.intel.com/software/products/mkl/docs/webhelp/vml/vml_DataTypesAccuracyModes.html for more info on the accuracy modes. Returns old accuracy settings. """ if use_vml: acc_dict = {None: 0, 'low': 1, 'high': 2, 'fast': 3} acc_reverse_dict = {1: 'low', 2: 'high', 3: 'fast'} if mode not in list(acc_dict.keys()): raise ValueError( "mode argument must be one of: None, 'high', 'low', 'fast'") retval = _set_vml_accuracy_mode(acc_dict.get(mode, 0)) return acc_reverse_dict.get(retval) else: return None
[docs]def set_vml_num_threads(nthreads): """ Suggests a maximum number of threads to be used in VML operations. This function is equivalent to the call `mkl_domain_set_num_threads(nthreads, MKL_DOMAIN_VML)` in the MKL library. See: http://www.intel.com/software/products/mkl/docs/webhelp/support/functn_mkl_domain_set_num_threads.html for more info about it. """ if use_vml: _set_vml_num_threads(nthreads)
[docs]def set_num_threads(nthreads): """ Sets a number of threads to be used in operations. Returns the previous setting for the number of threads. During initialization time Numexpr sets this number to the number of detected cores in the system (see `detect_number_of_cores()`). If you are using Intel's VML, you may want to use `set_vml_num_threads(nthreads)` to perform the parallel job with VML instead. However, you should get very similar performance with VML-optimized functions, and VML's parallelizer cannot deal with common expresions like `(x+1)*(x-2)`, while Numexpr's one can. """ old_nthreads = _set_num_threads(nthreads) return old_nthreads
[docs]def detect_number_of_cores(): """ Detects the number of cores on a system. Cribbed from pp. """ # Linux, Unix and MacOS: if hasattr(os, "sysconf"): if "SC_NPROCESSORS_ONLN" in os.sysconf_names: # Linux & Unix: ncpus = os.sysconf("SC_NPROCESSORS_ONLN") if isinstance(ncpus, int) and ncpus > 0: return ncpus else: # OSX: return int(subprocess.check_output(["sysctl", "-n", "hw.ncpu"])) # Windows: if "NUMBER_OF_PROCESSORS" in os.environ: ncpus = int(os.environ["NUMBER_OF_PROCESSORS"]); if ncpus > 0: return ncpus return 1 # Default
[docs]def detect_number_of_threads(): """ If this is modified, please update the note in: https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide """ try: nthreads = int(os.environ['NUMEXPR_NUM_THREADS']) except KeyError: nthreads = int(os.environ.get('OMP_NUM_THREADS', detect_number_of_cores())) # Check that we don't activate too many threads at the same time. # 8 seems a sensible value. if nthreads > 8: nthreads = 8 # Check that we don't surpass the MAX_THREADS in interpreter.cpp if nthreads > MAX_THREADS: nthreads = MAX_THREADS return nthreads
class CacheDict(dict): """ A dictionary that prevents itself from growing too much. """ def __init__(self, maxentries): self.maxentries = maxentries super(CacheDict, self).__init__(self) def __setitem__(self, key, value): # Protection against growing the cache too much if len(self) > self.maxentries: # Remove a 10% of (arbitrary) elements from the cache entries_to_remove = self.maxentries // 10 for k in list(self.keys())[:entries_to_remove]: super(CacheDict, self).__delitem__(k) super(CacheDict, self).__setitem__(key, value)