Скачать или смотреть Efficiently Spawn n Workers Using GNU Parallel for Continuous Tasks

Discover how to utilize `GNU Parallel` to efficiently manage multiple workers without input, tailored for continuous tasks in Python.
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Unlocking the Power of GNU Parallel for Worker Management

In today's world of computing, efficient processing is more critical than ever. When running tasks that could utilize multiple cores of your processor, it becomes essential to use tools that allow you to distribute workloads seamlessly. One such tool is GNU Parallel, which can help spawn multiple workers to perform continual jobs without the hassle of managing inputs manually. In this guide, we will answer the question of how to trigger n jobs doing specific tasks using GNU Parallel technique.

The Problem: Triggering Multiple Jobs

You may find yourself in a situation where you need to execute a script continuously across several cores of your machine. For instance, consider a Python script which generates random numbers every second:

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In this example:

The script runs indefinitely, producing a random number between 0 and 9 every second.

It can certainly benefit from being executed over multiple cores, depending on the number of tasks you wish to handle concurrently.

The question then arises: How can you utilize GNU Parallel to effectively manage these jobs?

The Solution: Using GNU Parallel

To seamlessly spawn multiple instances of the work.py script, you can leverage GNU Parallel with a few straightforward commands. Here’s how to do it:

Step-by-Step Guide

Check the Number of Available Cores:
First, determine how many cores your system has available. You can do this with the command:

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Employ the Command:
Use the following command to execute your script across all available cores:

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Explained Breakdown:

seq $(parallel --number-of-cores): This command generates a sequence from 1 to the number of cores available. It essentially creates the workload distribution for GNU Parallel.

parallel -N0 --line-buffered python3 -u ./work.py: This command tells Parallel to run the work.py script without buffering the output. The -N0 option specifies that each invocation of the script should not take any input lines, perfectly fitting the requirement of having no input conditionality.

Alternative Consideration: Python Multiprocessing

While using GNU Parallel is powerful, it's worth considering Python's built-in multiprocessing library for managing parallel tasks. This eliminates the need for an additional dependency and can simplify your workflow if you are primarily using Python.

Conclusion

GNU Parallel is a robust choice for executing multiple jobs simultaneously without any manual input, particularly for tasks that generate continuous output, like our work.py. The simple command structure allows you to be productive with minimal overhead.

Final Thoughts

In summary, you can trigger n jobs across multiple cores effortlessly using GNU Parallel, but don't ignore Python's multiprocessing capabilities if your project requires a Python-centric solution. Whether you choose GNU Parallel or the built-in options provided by Python, the important thing is to maximize your computing resources effectively.