# 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

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`).

## Setting the threading layer selection priority

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 `NUMBA_THREADING_LAYER_PRIORITY` can be used.

Note that it can also be set via `numba.config.THREADING_LAYER_PRIORITY`. Similar to `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"]`.

### 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 (arm64) | GNU OpenMP libraries (very likely this will already exist) MS OpenMP libraries (very likely this will already exist) The `llvm-openmp` package (`$ conda install llvm-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 systems with the `fork(2)` system call available, if the TBB backed threading layer is in use and a `fork` 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 on `STDERR`. As `spawn` is essentially `fork` followed by `exec` it is safe to `spawn` from a non-main thread, but as this cannot be differentiated from just a `fork` call the warning message will still be displayed.

- On OSX (arm64), the `llvm-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:

1. `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.

2. 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.

## Getting a Thread ID

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 `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).

### 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 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 `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.

  See also

  `get_num_threads`, `numba.config.NUMBA_NUM_THREADS`
  `numba.config.NUMBA_DEFAULT_NUM_THREADS`, `NUMBA_NUM_THREADS`

numba.get_num_threads()

  Get the number of threads used for parallel execution.

  By default (if `set_num_threads()` is never called), all `numba.config.NUMBA_NUM_THREADS` threads are used.

  This number is less than or equal to the total number of threads that are launched, `numba.config.NUMBA_NUM_THREADS`.

  This function can be used inside of a jitted function.

  Returns:
    The number of threads.

  See also

  `set_num_threads`, `numba.config.NUMBA_NUM_THREADS`
  `numba.config.NUMBA_DEFAULT_NUM_THREADS`, `NUMBA_NUM_THREADS`

numba.get_thread_id()

  Returns a unique ID for each thread in the range 0 (inclusive) to `get_num_threads()` (exclusive).