SharedArray 3.2.1

Share numpy arrays between processes

This is a simple python extension that lets you share numpy arrays with other processes on the same computer. It uses either shared files or POSIX shared memory as data stores and therefore should work on most operating systems.

Example

Here’s a simple example to give an idea of how it works. This example does everything from a single python interpreter for the sake of clarity, but the real point is to share arrays between python interpreters.

import numpy as np
import SharedArray as sa

# Create an array in shared memory.
a = sa.create("shm://test", 10)

# Attach it as a different array. This can be done from another
# python interpreter as long as it runs on the same computer.
b = sa.attach("shm://test")

# See how they are actually sharing the same memory.
a[0] = 42
print(b[0])

# Destroying a does not affect b.
del a
print(b[0])

# See how "test" is still present in shared memory even though we
# destroyed the array a. This method only works on Linux.
sa.list()

# Now destroy the array "test" from memory.
sa.delete("test")

# The array b is still there, but once you destroy it then the
# data is gone for real.
print(b[0])

Functions

SharedArray.create(name, shape, dtype=float)

This function creates an array in shared memory and returns a numpy array that uses the shared memory as data backend.

The shared memory is identified by name, which can use the file:// prefix to indicate that the data backend will be a file, or shm:// to indicate that the data backend shall be a POSIX shared memory object. For backward compatibility shm:// is assumed when no prefix is given. Most operating systems implement strong file caching so using a file as a data backend won’t usually affect performance.

The shape and dtype arguments are identical to those of the numpy function numpy.zeros(), and the returned array is indeed initialized to zeros.

The content of the array lives in shared memory and/or in a file and won’t be lost when the numpy array is deleted, nor when the python interpreter exits. To delete a shared array and reclaim system resources use the SharedArray.delete() function.

SharedArray.attach(name)

This function attaches a previously created array in shared memory identified by name, which can use the file:// prefix to indicate that the array is stored as a file, or shm:// to indicate that the array is stored as a POSIX shared memory object. For backward compatibility shm:// is assumed when no prefix is given.

An array may be simultaneously attached from multiple different processes (i.e. python interpreters).

The content of the array lives in shared memory and/or in a file and won’t be lost when the numpy array is deleted, nor when the python interpreter exits. To delete a shared array reclaim system resources use the SharedArray.delete() function.

SharedArray.delete(name)

This function destroys the previously created array identified by name, which can use the file:// prefix to indicate that the array is stored as a file, or shm:// to indicate that the array is stored as a POSIX shared memory object. For backward compatibility shm:// is assumed when no prefix is given

After calling delete, the array will not be attachable anymore, but existing attachments will remain valid until they are themselves destroyed. The data is reclaimed by the system when the very last attachment is deleted.

SharedArray.list()

This function returns a list of previously created arrays stored as POSIX SHM objects, along with their name, data type and dimensions. This function only works on Linux because it directly accesses files exposed under /dev/shm. There doesn’t seem to be a portable method of achieving this.

Constants

SharedArray.MS_ASYNC

Flag for the msync() method of the base object of the returned numpy array (see below). Specifies that an update be scheduled, but the call returns immediately.

SharedArray.MS_SYNC

Flag for the msync() method of the base object of the returned numpy array (see below). Requests an update and waits for it to complete.

SharedArray.MS_INVALIDATE

Flag for the msync() method of the base object of the returned numpy array (see below). Asks to invalidate other mappings of the same file (so that they can be updated with the fresh values just written).

Base object

SharedArray registers its own python object as the base object of the returned numpy array. This base object exposes the following methods and attributes:

msync(array, flags)

This method is a wrapper around msync(2) and is only useful when using file-backed arrays (i.e. not POSIX shared memory). msync(2) flushes the mapped memory region back to the filesystem. The flags are exported as constants in the module definition (see above) and are a 1:1 map of the msync(2) flags, please refer to the manual page of msync(2) for details.

mlock(array)

This method is a wrapper around mlock(2): lock the memory map into RAM, preventing that memory from being paged to the swap area.

munlock(array)

This method is a wrapper around munlock(2): unlock the memory map, allowing that memory to be paged to the swap area.

name

This constant string is the name of the array as passed to SharedArray.create() or SharedArray.attach(). It may be passed to SharedArray.delete().

addr

Base address of the array in memory.

size

Size of the array in memory.

Requirements

• Python 2.7 or 3+

• Numpy 1.8+

• Posix shared memory interface

SharedArray uses the posix shm interface (shm_open and shm_unlink) and so should work on most POSIX operating systems (Linux, BSD, etc.). It has been reported to work on macOS, and it is unlikely to work on Windows.

Installation

The extension uses the distutils python package that should be familiar to most python users. To test the extension directly from the source tree, without installing, type:

python setup.py build_ext --inplace

To build and install the extension system-wide, type:

python setup.py build
sudo python setup.py install

The package is also available on PyPI and can be installed using the pip tool.

FAQ

On Linux, I get segfaults when working with very large arrays.

A few people have reported segfaults with very large arrays using POSIX shared memory. This is not a bug in SharedArray but rather an indication that the system ran out of POSIX shared memory.

On Linux a tmpfs virtual filesystem is used to provide POSIX shared memory, and by default it is given only about 20% of the total available memory, depending on the distribution. That amount can be changed by re-mounting the tmpfs filesystem with the size=100% option:

sudo mount -o remount,size=100% /run/shm

Also you can make the change permanent, on next boot, by setting SHM_SIZE=100% in /etc/defaults/tmpfs on recent Debian installations.

On Linux, I get “Cannot allocate memory” when creating many arrays.

SharedArray uses one memory map per array that is attached (or created). By default the maximum number of memory maps per process is set by the Linux kernel to 65530. If you want to create more arrays than that you need to tune the kernel parameter vm.max_map_count and set it to a higher value.

/sbin/sysctl vm.max_map_count=655300

Note that for the change to be permanent you need to add this line to /etc/sysctl.conf:

vm.max_map_count=655300

I can’t attach old (pre 0.4) arrays anymore.

Since version 0.4 all arrays are now page aligned in memory, to be used with SIMD instructions (e.g. fftw library). As a side effect, arrays created with a previous version of SharedArray aren’t compatible with the new version (the location of the metadata changed). Save your work before upgrading.

Contact

This package is hosted on GitLab at: https://gitlab.com/tenzing/shared-array

Packages are also available on PyPi at: https://pypi.python.org/pypi/SharedArray

For bug reports, feature requests, suggestions, patches and everything else related to SharedArray, feel free to raise issues on the project page. You can also contact the maintainer directly by email at [e-mail].