Using the Slurm Executor with Snakemake

Oct 13, 2025 by miodeya.com 100 views

Introduction

When working with high-performance computing (HPC) clusters, optimizing resource utilization is crucial to achieve efficient execution of workflows. Snakemake, a popular workflow management system, provides a flexible and scalable way to manage complex pipelines. However, when running Snakemake on an HPC cluster, requesting nodes can be a limiting step. In this article, we will explore how to execute multiple instances of a Snakemake rule in parallel on the same node using the Slurm executor.

Background

Snakemake is a Python-based workflow management system designed to manage complex pipelines. It provides a flexible and scalable way to manage workflows, making it an ideal choice for HPC clusters. The Slurm executor is a plugin for Snakemake that allows users to run workflows on Slurm-based HPC clusters.

Why Execute Multiple Instances of a Rule in Parallel?

When running Snakemake on an HPC cluster, it's common to request multiple nodes to execute different rules in parallel. However, this approach can lead to underutilization of cores on each node. By executing multiple instances of a rule in parallel on the same node, we can fully utilize the cores of each allocated node, reducing the overall execution time of the workflow.

Using the Slurm Executor with Snakemake

To execute multiple instances of a rule in parallel on the same node using the Slurm executor, we need to configure Snakemake to use the Slurm executor and specify the number of cores to use on each node.

Step 1: Configure Snakemake to use the Slurm Executor

To use the Slurm executor with Snakemake, we need to add the following configuration to our snakemake.config file:

rule:
  executor: 'slurm'
  resources:
    nodes: 1
    cores: 4
    memory: '16G'

This configuration tells Snakemake to use the Slurm executor and request 1 node with 4 cores and 16 GB of memory.

Step 2: Specify the Number of Cores to Use on Each Node

To execute multiple instances of a rule in parallel on the same node, we need to specify the number of cores to use on each node. We can do this by adding the following configuration to our snakemake.config file:

rule:
  executor: 'slurm'
  resources:
    nodes: 1
    cores: 4
    memory: '16G'
  parallel:
    cores: 4

This configuration tells Snakemake to use 4 cores on each node to execute the rule in parallel.

Step 3: Execute the Snakemake Workflow

Once we have configured Snakemake to use the Slurm executor and specified the number of cores to use on each node, we can execute the Snakemake workflow using the following command:

snakemake --configfile snakemake.config --use-conda --jobs 4

This command tells Snakemake to use the snakemake.config file as the configuration file, use Conda to manage dependencies, and execute the workflow with 4 jobs in parallel.

Example Use Case

Let's consider an example use case where we have a Snakemake workflow that consists of 4 rules: rule1, rule2, rule3, and rule4. Each rule requires 1 core to execute, and we want to execute all 4 rules in parallel on the same node.

We can configure Snakemake to use the Slurm executor and specify the number of cores to use on each node as follows:

rule:
  executor: 'slurm'
  resources:
    nodes: 1
    cores: 4
    memory: '16G'
  parallel:
    cores: 4

We can then execute the Snakemake workflow using the following command:

snakemake --configfile snakemake.config --use-conda --jobs 4

This will execute all 4 rules in parallel on the same node, fully utilizing the 4 cores available on the node.

Conclusion

In this article, we explored how to execute multiple instances of a Snakemake rule in parallel on the same node using the Slurm executor. By configuring Snakemake to use the Slurm executor and specifying the number of cores to use on each node, we can fully utilize the cores of each allocated node, reducing the overall execution time of the workflow. We also provided an example use case to demonstrate how to execute multiple instances of a rule in parallel on the same node.

Best Practices

When executing multiple instances of a rule in parallel on the same node using the Slurm executor, it's essential to follow best practices to ensure efficient execution of the workflow. Here are some best practices to keep in mind:

Use the correct number of cores: Make sure to specify the correct number of cores to use on each node to avoid underutilization of cores.
Use the correct memory allocation: Ensure that you allocate sufficient memory for each node to avoid memory-related issues.
Monitor resource utilization: Monitor resource utilization to ensure that the workflow is executing efficiently and that resources are being utilized correctly.
Optimize workflow configuration: Optimize the workflow configuration to ensure that the workflow is executing efficiently and that resources are being utilized correctly.

Q: What is the Slurm executor in Snakemake?

A: The Slurm executor is a plugin for Snakemake that allows users to run workflows on Slurm-based HPC clusters. It provides a flexible and scalable way to manage complex pipelines on HPC clusters.

Q: Why do I need to use the Slurm executor with Snakemake?

A: The Slurm executor is necessary when running Snakemake on an HPC cluster because it allows you to request nodes and cores from the cluster, which is essential for executing complex workflows.

Q: How do I configure Snakemake to use the Slurm executor?

A: To configure Snakemake to use the Slurm executor, you need to add the following configuration to your snakemake.config file:

rule:
  executor: 'slurm'
  resources:
    nodes: 1
    cores: 4
    memory: '16G'

This configuration tells Snakemake to use the Slurm executor and request 1 node with 4 cores and 16 GB of memory.

Q: How do I specify the number of cores to use on each node?

A: To specify the number of cores to use on each node, you need to add the following configuration to your snakemake.config file:

rule:
  executor: 'slurm'
  resources:
    nodes: 1
    cores: 4
    memory: '16G'
  parallel:
    cores: 4

This configuration tells Snakemake to use 4 cores on each node to execute the rule in parallel.

Q: How do I execute the Snakemake workflow using the Slurm executor?

A: To execute the Snakemake workflow using the Slurm executor, you need to use the following command:

snakemake --configfile snakemake.config --use-conda --jobs 4

This command tells Snakemake to use the snakemake.config file as the configuration file, use Conda to manage dependencies, and execute the workflow with 4 jobs in parallel.

Q: Can I use the Slurm executor with other workflow management systems?

A: Yes, the Slurm executor can be used with other workflow management systems, such as Nextflow and Toil.

Q: How do I troubleshoot issues with the Slurm executor?

A: To troubleshoot issues with the Slurm executor, you can check the Snakemake logs for errors and warnings. You can also use the snakemake --debug command to enable debug mode and get more detailed information about the workflow execution.

Q: Can I use the Slurm executor with a cluster that has a different scheduler?

A: Yes, the Slurm executor can be used with clusters that have a different scheduler, such as PBS or LSF. However, you may need to modify the configuration file to specify the correct scheduler and resources.

Q: How do I optimize the workflow configuration for the Slurm executor?

A: To optimize the workflow configuration for the Slurm executor, you can experiment with different values for the nodes, cores, and memory resources. You can also use the snakemake --debug command to get more detailed information about the workflow execution and identify areas for optimization.

Q: Can I use the Slurm executor with a workflow that has multiple rules?

A: Yes, the Slurm executor can be used with workflows that have multiple rules. You can specify the parallel configuration option to execute multiple rules in parallel on the same node.

Q: How do I monitor resource utilization with the Slurm executor?

A: To monitor resource utilization with the Slurm executor, you can use the squeue command to check the status of the jobs and the sstat command to check the resource utilization of each job. You can also use the snakemake --debug command to get more detailed information about the workflow execution and identify areas for optimization.