Mastering Parpool in MATLAB: A Step-by-Step Guide to Running in the Command-Line on a Slurm Cluster

Are you tired of waiting for your MATLAB scripts to run sequentially on a single core? Do you want to harness the power of parallel processing on a Slurm cluster? Look no further! In this comprehensive guide, we’ll walk you through the process of setting up and running Parpool in MATLAB on a Slurm cluster using the command-line interface.

What is Parpool in MATLAB?

Parpool is a feature in MATLAB that allows you to parallelize your code using multiple workers. By default, MATLAB uses a single core to execute your scripts. However, with Parpool, you can utilize multiple cores or even distribute your workload across a cluster of machines. This can significantly reduce the execution time of computationally intensive tasks.

Why Use a Slurm Cluster?

Slurm is an open-source workload manager designed for high-performance computing (HPC) environments. It provides a robust and scalable way to manage job scheduling, resource allocation, and job execution on a cluster of machines. By integrating Parpool with a Slurm cluster, you can tap into the collective computing power of multiple machines to accelerate your MATLAB simulations.

Prerequisites

Before we dive into the setup process, make sure you have the following:

• A Slurm cluster with multiple nodes
• MATLAB installed on the cluster’s head node
• A valid MATLAB license with the Parallel Computing Toolbox
• A basic understanding of Linux command-line interface

Setting Up Parpool on the Slurm Cluster

Follow these steps to set up Parpool on your Slurm cluster:

1. Open a terminal on the cluster’s head node and start MATLAB:

   module load matlab
matlab -nodisplay

2. Verify that the Parallel Computing Toolbox is installed and licensed:

   ver

   Look for the “Parallel Computing Toolbox” in the output.

3. Create a cluster object using the Slurm profile:

   cluster = parcluster('slurm')

   This will create a cluster object named “cluster” with the Slurm profile.

4. Specify the number of workers and the number of threads per worker:

   cluster.NumWorkers = 8;
cluster.ThreadPoolSize = 4;

   In this example, we’re using 8 workers with 4 threads per worker.

Running Parpool Jobs on the Slurm Cluster

Now that you’ve set up Parpool on the Slurm cluster, it’s time to run your MATLAB scripts in parallel:

1. Write your MATLAB script using the Parallel Computing Toolbox functions:

   
parfor i = 1:100
% Your computationally intensive code here
end


   Save this script as a file, e.g., “my_script.m”.

2. Submit the job to the Slurm cluster using the batch function:

   
job = batch(cluster, 'my_script', 'Pool', cluster);


   This will submit the job to the Slurm cluster and run it in parallel using the specified cluster object.

3. Monitor the job status using the wait function:

   wait(job)

   This will block the MATLAB command-line interface until the job is complete.

4. Retrieve the job results using the fetchOutputs function:

   results = fetchOutputs(job)

   This will fetch the output of the job and store it in the “results” variable.

Tips and Tricks

Here are some additional tips to help you get the most out of Parpool on your Slurm cluster:

• Use the parpool function to create a parallel pool of workers:

  parpool(cluster, 8)

  This will create a parallel pool of 8 workers.

• Use the spmd function to execute code in parallel:

  
spmd
% Your parallel code here
end


  This will execute the code in parallel across the workers.

• Use the labindex and numlabs functions to access the worker index and number of workers:

  
labindex
numlabs


  These functions can be used to distribute tasks or data across the workers.

Common Errors and Solutions

Here are some common errors you might encounter when running Parpool on a Slurm cluster:

Error Message	Solution
Error using parpool (line 83)	Make sure the Parallel Computing Toolbox is installed and licensed.
Error using batch (line 101)	Verify that the Slurm cluster is properly configured and the MATLAB license is valid.
Error using spmd (line 115)	Check that the parallel pool is created successfully and the workers are available.

Conclusion

In this article, we’ve covered the process of setting up and running Parpool in MATLAB on a Slurm cluster using the command-line interface. By following these steps and tips, you can unlock the full potential of parallel processing and accelerate your MATLAB simulations. Remember to troubleshoot common errors and optimize your code for maximum performance.

With Parpool and Slurm, you can:

• Scale your simulations to thousands of cores
• Reduce execution time by orders of magnitude
• Focus on developing new algorithms and models

Start harnessing the power of parallel computing today and take your MATLAB simulations to the next level!

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