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A simple and lightweight package for parallel computing

Project description


Ampeg is a simple and lightweight package for parallel computation. It provides simple functions for scheduling and execution of a set of dependent or independent computational tasks over multiple processes using the multiprocessing package.


Python 2.7 or later

Python 3.4 or later


pip install ampeg


Ampeg exposes a scheduling function earliest_finish_time, an execution function execute_task_lists and a Dependency class. The former takes a directed acyclic graph (DAG) and a number of processes and produces a set of task lists for each process and a corresponding set of task IDs for translating the execution result. These two form the input to execute_task_lists, which returns a dict with the result of each task in the original graph.

The DAG is represented by a python dict of vertices where each key is the ID of a task and each value is a triple of (function, args or kwargs, computation cost). Edges are implicitly defined by instances of the Dependency class in the args or kwargs.

A simple usage example computing (3^2 + 4^2) - (3^2 * 10/2):

>>> import ampeg as ag
>>> n_processes = 3
>>> my_graph = {0: (lambda x: x**2, 3, 10.8),
                1: (lambda x: x**2, 4, 10.8),
                2: (lambda x: x/2, 10, 11),
                3: (lambda x, y: x + y, (ag.Dependency(0, None, 1),
                                         ag.Dependency(1, None, 1), 10.7),
                4: (lambda x, y: x*y, (ag.Dependency(0, None, 1),
                                       ag.Dependency(2, None, 1)), 10.8),
                5: (lambda x, y: x - y, (ag.Dependency(3, None, 1),
                                         ag.Dependency(4, None, 1)), 10.9)}
>>> task_lists, task_ids = ag.earliest_finish_time(my_graph, n_processes)
>>> ag.execute_task_lists(task_lists, task_ids)
{0: 9, 1: 16, 2: 5, 3: 25, 4: 45, 5: -20}

The Dependency class

A dependency is a triple of (task ID or index, key (if any) and communication cost). The key may be a single key, index or slice, or it may be an iterable of such values to be applied in sequence. For example, the key ('values', 2) extracts the value 5 from the dict {'values': [1, 3, 5]}. Dependency instances are created by ampeg.Dependency(task, key, cost) where cost is optional and defaults to 0.


Ampeg catches exceptions raised by individual tasks, returning them as results encapsulated in the Err class. When an Err instance is found among the dependencies for a task, the result for this task will be an Err instance encapsulating a DependencyError.


Note that under Windows, the functions and their arguments must all be picklable.

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