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The Threading Layers¶

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!

Which threading layers are available?¶

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

Due to compatibility issues with manylinux1 and other portability concerns, the OpenMP threading layer is disabled in the Numba binary wheels on PyPI.

Note

The default manner in which Numba searches for and loads a threading layer is tolerant of missing libraries, incompatible runtimes etc.

Setting the threading layer¶

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).

Selecting a threading layer for safe parallel execution¶

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 the threading module.
spawn ing processes from the multiprocessing module via spawn (default on Windows, only available in Python 3.4+ on Unix)
fork ing processes from the multiprocessing module via fork (default on Unix).
fork ing processes from the multiprocessing module through the use of a forkserver (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 setting mechanisms are as follows:

default provides no specific safety guarantee and is the default.
safe is both fork and thread safe, this requires the tbb 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.

Selecting a named threading layer¶

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 setting mechanisms. 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)

The intel-openmp package ($ conda install intel-openmp)

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.

Extra notes¶

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 the omp threading layer, a detection mechanism is present that will try and gracefully terminate the forked child and print an error message to STDERR.
On OSX, the intel-openmp package is required to enable the OpenMP based threading layer.

Setting the Number of Threads¶

The number of threads used by numba is based on the number of CPU cores available (see numba.config.NUMBA_DEFAULT_NUM_THREADS), but it can be overridden with the NUMBA_NUM_THREADS environment variable.

The total number of threads that numba launches is in the variable 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 numba.set_num_threads(). Note that set_num_threads() only allows setting the number of threads to a smaller value than NUMBA_NUM_THREADS. Numba always launches numba.config.NUMBA_NUM_THREADS threads, but 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 numba.get_num_threads(). Both functions work inside of a jitted function.

Example of Limiting the Number of Threads¶

In this example, suppose the machine we are running on has 8 cores (so 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 NUMBA_NUM_THREADS environment variable to 2.

$ NUMBA_NUM_THREADS=2 python ourcode.py

However, there are two downsides to this approach:

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. NUMBA_NUM_THREADS sets the maximum number of threads that are launched for a process. Calling set_num_threads() with a value greater than 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 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.

API Reference¶

numba.config.NUMBA_NUM_THREADS¶

The total (maximum) number of threads launched by numba.

Defaults to numba.config.NUMBA_DEFAULT_NUM_THREADS, but can be overridden with the NUMBA_NUM_THREADS environment variable.

numba.config.NUMBA_DEFAULT_NUM_THREADS¶: The number of CPU cores on the system (as determined by multiprocessing.cpu_count()). This is the default value for numba.config.NUMBA_NUM_THREADS unless the NUMBA_NUM_THREADS environment variable is set.

numba.set_num_threads(n)¶

Set the number of threads to use for parallel execution.

By default, all numba.config.NUMBA_NUM_THREADS threads are used.

This functionality works by masking out threads that are not used. Therefore, the number of threads n must be less than or equal to NUMBA_NUM_THREADS, the total number of threads that are launched. See its documentation for more details.

This function can be used inside of a jitted function.

Parameters

n: The number of threads. Must be between 1 and NUMBA_NUM_THREADS.