Скачать или смотреть How to Efficiently Run a For Loop in Parallel with Bash on HPC

Discover how to optimize your bash scripting for parallel execution on HPC using SLURM and GNU Parallel. Avoid common pitfalls and boost your productivity.
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How to Efficiently Run a For Loop in Parallel with Bash on HPC

Running multiple instances of a script with different parameters can be a daunting task, especially when you're working in a High Performance Computing (HPC) environment. If you've encountered issues running your Rscript in parallel with bash, you're not alone. In this guide, we will guide you through the problem and how to resolve it with effective solutions.

The Problem

You are likely utilizing a script to automate the execution of an Rscript, which requires different parameters. In your attempt to do this in parallel through a for loop, you might have faced issues that led the system to execute everything simultaneously, resulting in potential overload on the compute node. This is a common challenge when you mistakenly use the & operator in bash scripts.

Your initial script might look something like this:

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This results in a notification that your job is finished almost immediately, which indicates the process is not performing as expected.

The Solution

To successfully run your script in parallel without running into issues of overload, make several adjustments and use SLURM efficiently.

Step 1: Variable Naming and Structure

First, simplify the naming of your loop variables. Later in your script, use these loop variables directly rather than recalling the previous ones. Here’s how to set up your loops:

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Step 2: Manage Processes with SLURM

Running 1824 processes simultaneously can severely reduce efficiency. Instead, utilize srun to control how these processes run on the available CPUs:

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Step 3: Using GNU Parallel

If you have GNU Parallel available, you can simplify your script even further. Here’s how it looks:

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Step 4: Job Array Option

Transforming the script to a job array can optimize it even further. Here’s a friendly way to run the innermost loop as an array:

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This approach allows you to manage the parameters effectively while preventing your compute resources from being overwhelmed.

Conclusion

Running scripts in parallel can significantly increase productivity, but it requires careful management to avoid overwhelming your HPC system. By organizing your script correctly, leveraging SLURM functionalities, and considering tools like GNU Parallel, you can optimize your workflow and run your Rscript with varying parameters efficiently.

Implement these techniques to refine your bash scripts for parallel processing, and watch your computational tasks execute smoothly.