benkay86

# Python's asynchronous input-output framework speeds up IO-bound operations
# by allowing your python program to make progress on multiple IO-bound tasks
# concurrently. In this example, we load many neuroimaging data files and
# then perform a compute-intensive operation on each of them. Parallelizing the
# compute-intensive operation is beyond the scope of this tutorial. However,
# we can still get a big speed up by reading multiple files concurrently. While
# the program waits for the operating system to make progress on reading one
# file, it can work on computational transformation of another file.
#
# Loading a file asynchronously is straightforward. Here, we define a helper

benkay86

"""Synchronize slurm jobs using advisory file locks (flock).

This module provides functionality for synchronizing concurrent work across
multiple jobs running on multiple nodes in a slurm cluster. Synchronization is
achieved using advisory file locks, or flock. This module is particularly useful
for easily-parallelizable jobs that divide a large problem into chunks that can
be processed independently.

To use an flock like a mutex:
    >>> from slurm_sync import LockFile