This section is about the Numba threading layer, this is the library that is
used internally to perform the parallel execution that occurs through the use of
the parallel
targets for CPUs, namely:
- The use of the
parallel=True
kwarg in@jit
and@njit
. - The use of the
target='parallel'
kwarg in@vectorize
and@guvectorize
.
Note
If a code base does not use the threading
or multiprocessing
modules (or any other sort of parallelism) the defaults for the threading
layer that ship with Numba will work well, no further action is required!
There are three threading layers available and they are named as follows:
tbb
- A threading layer backed by Intel TBB.omp
- A threading layer backed by OpenMP.workqueue
-A simple built-in work-sharing task scheduler.
In practice, the only threading layer guaranteed to be present is workqueue
.
The omp
layer requires the presence of a suitable OpenMP runtime library.
The tbb
layer requires the presence of Intel's TBB libraries, these can be
obtained via the conda command:
$ conda install tbb
If you installed Numba with pip
, TBB can be enabled by running:
$ pip install tbb
Note
The default manner in which Numba searches for and loads a threading layer is tolerant of missing libraries, incompatible runtimes etc.
The threading layer is set via the environment variable
NUMBA_THREADING_LAYER
or through assignment to
numba.config.THREADING_LAYER
. If the programmatic approach to setting the
threading layer is used it must occur logically before any Numba based
compilation for a parallel target has occurred. There are two approaches to
choosing a threading layer, the first is by selecting a threading layer that is
safe under various forms of parallel execution, the second is through explicit
selection via the threading layer name (e.g. tbb
).
By default the threading layers are searched in the order of 'tbb'
,
'omp'
, then 'workqueue'
. To change this search order whilst
maintaining the selection of a threading layer based on availability, the
environment variable :envvar:`NUMBA_THREADING_LAYER_PRIORITY` can be used.
Note that it can also be set via :py:data:`numba.config.THREADING_LAYER_PRIORITY`. Similar to :py:data:`numba.config.THREADING_LAYER`, it must occur logically before any Numba based compilation for a parallel target has occurred.
For example, to instruct Numba to choose omp
first if available,
then tbb
and so on, set the environment variable as
NUMBA_THREADING_LAYER_PRIORITY="omp tbb workqueue"
.
Or programmatically,
numba.config.THREADING_LAYER_PRIORITY = ["omp", "tbb", "workqueue"]
.
Parallel execution is fundamentally derived from core Python libraries in four forms (the first three also apply to code using parallel execution via other means!):
threads
from thethreading
module.spawn
ing processes from themultiprocessing
module viaspawn
(default on Windows, only available in Python 3.4+ on Unix)fork
ing processes from themultiprocessing
module viafork
(default on Unix).fork
ing processes from themultiprocessing
module through the use of aforkserver
(only available in Python 3 on Unix). Essentially a new process is spawned and then forks are made from this new process on request.
Any library in use with these forms of parallelism must exhibit safe behaviour under the given paradigm. As a result, the threading layer selection methods are designed to provide a way to choose a threading layer library that is safe for a given paradigm in an easy, cross platform and environment tolerant manner. The options that can be supplied to the :ref:`setting mechanisms <numba-threading-layer-setting-mech>` are as follows:
default
provides no specific safety guarantee and is the default.safe
is both fork and thread safe, this requires thetbb
package (Intel TBB libraries) to be installed.forksafe
provides a fork safe library.threadsafe
provides a thread safe library.
To discover the threading layer that was selected, the function
numba.threading_layer()
may be called after parallel execution. For example,
on a Linux machine with no TBB installed:
from numba import config, njit, threading_layer import numpy as np # set the threading layer before any parallel target compilation config.THREADING_LAYER = 'threadsafe' @njit(parallel=True) def foo(a, b): return a + b x = np.arange(10.) y = x.copy() # this will force the compilation of the function, select a threading layer # and then execute in parallel foo(x, y) # demonstrate the threading layer chosen print("Threading layer chosen: %s" % threading_layer())
which produces:
Threading layer chosen: omp
and this makes sense as GNU OpenMP, as present on Linux, is thread safe.
Advanced users may wish to select a specific threading layer for their use case, this is done by directly supplying the threading layer name to the :ref:`setting mechanisms <numba-threading-layer-setting-mech>`. The options and requirements are as follows:
Threading Layer Name | Platform | Requirements |
---|---|---|
tbb |
All | The tbb package ($ conda install
tbb ) |
omp |
Linux Windows OSX |
GNU OpenMP libraries (very likely this will already exist) MS OpenMP libraries (very likely this will already exist) Either the |
workqueue |
All | None |
Should the threading layer not load correctly Numba will detect this and provide
a hint about how to resolve the problem. It should also be noted that the Numba
diagnostic command numba -s
has a section
__Threading Layer Information__
that reports on the availability of
threading layers in the current environment.
The threading layers have fairly complex interactions with CPython internals and system level libraries, some additional things to note:
- The installation of Intel's TBB libraries vastly widens the options available in the threading layer selection process.
- On Linux, the
omp
threading layer is not fork safe due to the GNU OpenMP runtime library (libgomp
) not being fork safe. If a fork occurs in a program that is using theomp
threading layer, a detection mechanism is present that will try and gracefully terminate the forked child and print an error message toSTDERR
. - On systems with the
fork(2)
system call available, if the TBB backed threading layer is in use and afork
call is made from a thread other than the thread that launched TBB (typically the main thread) then this results in undefined behaviour and a warning will be displayed onSTDERR
. Asspawn
is essentiallyfork
followed byexec
it is safe tospawn
from a non-main thread, but as this cannot be differentiated from just afork
call the warning message will still be displayed. - On OSX, the
intel-openmp
package is required to enable the OpenMP based threading layer.
The number of threads used by numba is based on the number of CPU cores available (see :obj:`numba.config.NUMBA_DEFAULT_NUM_THREADS`), but it can be overridden with the :envvar:`NUMBA_NUM_THREADS` environment variable.
The total number of threads that numba launches is in the variable :obj:`numba.config.NUMBA_NUM_THREADS`.
For some use cases, it may be desirable to set the number of threads to a lower value, so that numba can be used with higher level parallelism.
The number of threads can be set dynamically at runtime using :func:`numba.set_num_threads`. Note that :func:`~.set_num_threads` only allows setting the number of threads to a smaller value than :obj:`~.NUMBA_NUM_THREADS`. Numba always launches :obj:`numba.config.NUMBA_NUM_THREADS` threads, but :func:`~.set_num_threads` causes it to mask out unused threads so they aren't used in computations.
The current number of threads used by numba can be accessed with :func:`numba.get_num_threads`. Both functions work inside of a jitted function.
In this example, suppose the machine we are running on has 8 cores (so
:obj:`numba.config.NUMBA_NUM_THREADS` would be 8
). Suppose we want to run
some code with @njit(parallel=True)
, but we also want to run our code
concurrently in 4 different processes. With the default number of threads,
each Python process would run 8 threads, for a total in 4*8 = 32 threads,
which is oversubscription for our 8 cores. We should rather limit each process
to 2 threads, so that the total will be 4*2 = 8, which matches our number of
physical cores.
There are two ways to do this. One is to set the :envvar:`NUMBA_NUM_THREADS`
environment variable to 2
.
$ NUMBA_NUM_THREADS=2 python ourcode.py
However, there are two downsides to this approach:
- :envvar:`NUMBA_NUM_THREADS` must be set before Numba is imported, and ideally before Python is launched. As soon as Numba is imported the environment variable is read and that number of threads is locked in as the number of threads Numba launches.
- If we want to later increase the number of threads used by the process, we cannot. :envvar:`NUMBA_NUM_THREADS` sets the maximum number of threads that are launched for a process. Calling :func:`~.set_num_threads()` with a value greater than :obj:`numba.config.NUMBA_NUM_THREADS` results in an error.
The advantage of this approach is that we can do it from outside of the process without changing the code.
Another approach is to use the :func:`numba.set_num_threads` function in our code
from numba import njit, set_num_threads
@njit(parallel=True)
def func():
...
set_num_threads(2)
func()
If we call set_num_threads(2)
before executing our parallel code, it has
the same effect as calling the process with NUMBA_NUM_THREADS=2
, in that
the parallel code will only execute on 2 threads. However, we can later call
set_num_threads(8)
to increase the number of threads back to the default
size. And we do not have to worry about setting it before Numba gets imported.
It only needs to be called before the parallel function is run.
In some cases it may be beneficial to have access to a unique identifier for the
current thread that is executing a parallel region in Numba. For that purpose,
Numba provides the :func:`numba.get_thread_id` function. This function is the
corollary of OpenMP's function omp_get_thread_num
and returns an integer
between 0 (inclusive) and the number of configured threads as described above
(exclusive).
.. py:data:: numba.config.NUMBA_NUM_THREADS The total (maximum) number of threads launched by numba. Defaults to :obj:`numba.config.NUMBA_DEFAULT_NUM_THREADS`, but can be overridden with the :envvar:`NUMBA_NUM_THREADS` environment variable.
.. py:data:: numba.config.NUMBA_DEFAULT_NUM_THREADS The number of usable CPU cores on the system (as determined by ``len(os.sched_getaffinity(0))``, if supported by the OS, or ``multiprocessing.cpu_count()`` if not). This is the default value for :obj:`numba.config.NUMBA_NUM_THREADS` unless the :envvar:`NUMBA_NUM_THREADS` environment variable is set.
.. autofunction:: numba.set_num_threads
.. autofunction:: numba.get_num_threads
.. autofunction:: numba.get_thread_id