pydata--numexpr/numexpr/utils.py

###################################################################
#  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)


def get_vml_version():
    """Get the VML/MKL library version."""
    if use_vml:
        return _get_vml_version()
    else:
        return None


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 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


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)


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


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 os.environ.has_key("NUMBER_OF_PROCESSORS"):
        ncpus = int(os.environ["NUMBER_OF_PROCESSORS"]);
        if ncpus > 0:
            return ncpus
    return 1  # Default


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 self.keys()[:entries_to_remove]:
                super(CacheDict, self).__delitem__(k)
        super(CacheDict, self).__setitem__(key, value)