parallelism 0.1.3

Enhance Parallel Task Execution

parallelism

The Parallelism library offers a powerful set of tools for managing concurrent task execution using both processes and threads. It aims to simplify the development of parallel programs, providing a higher-level interface for distributing tasks across multiple execution units. Whether your tasks are CPU-bound or I/O-bound, the Parallelism library facilitates efficient and streamlined parallel processing.

Installation

You can install the Parallelism library using pip:

pip install parallelism

Basic Usage

The Parallelism library offers an intuitive way to create and manage tasks for concurrent execution. Here's a simple example of how to use the library:

1. Import the necessary modules and classes in your Python code:

from multiprocessing import Process
from threading import Thread

from parallelism import scheduled_task, task_scheduler

2. Define your task functions. These functions will be executed concurrently:

def func(*args, **kwargs):
    # Your task logic here
    pass

3. Create task instances using the scheduled_task function, specifying the execution unit (Process or Thread), task name, function, and positional arguments or keyword arguments:

task1 = scheduled_task(Process, 'task1', func, args=(...))
task2 = scheduled_task(Thread, 'task2', func, kwargs={...})

4. Schedule tasks using the task_scheduler function, indicating the tasks to be executed and the desired number of processes and threads:

result = task_scheduler(tasks=(task1, task2), processes=2, threads=4)

5. Access task execution details and results through the result object, providing insights into execution times, elapsed times, exceptions, and return values:

result.execution_time
result.elapsed_time
result.raise_exception
result.return_value

For more comprehensive documentation and advanced usage, please refer to the full API Documentation.