Running Slurm in the cloud sounds simple at first: spin up some VMs, install Slurm, and start submitting jobs.

In reality, cloud-based HPC introduces a different set of design decisions and trade-offs compared to on-prem clusters. If the architecture is not planned properly, costs increase quickly and performance can drop.

This guide walks through a typical Slurm architecture on AWS/Azure and highlights the most common pitfalls.

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Why Run Slurm in the Cloud?

Common reasons include:

• On-demand scaling for peak workloads
• No upfront hardware investment
• Access to GPU instances when needed
• Flexibility for short-term projects

However, cloud HPC is not always cheaper or faster — it depends heavily on how it is configured.

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Typical Slurm Architecture in Cloud

A standard setup usually includes:

1. Head Node (Controller)

• Runs slurmctld
• Manages scheduling and job queues
• Typically a small-to-medium VM

Key Point:
This node should be stable and always available.

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2. Compute Nodes

• Dynamically provisioned instances
• Can be CPU or GPU-based
• Often scaled up/down based on demand

Common Approach:

• Auto-scaling groups (AWS)
• Virtual Machine Scale Sets (Azure)

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3. Login Node

• User access via SSH
• Job submission and monitoring

This is often combined with the head node in smaller setups, but separated in production environments.

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4. Shared Storage

Required for:

• Input/output data
• Job scripts
• Application binaries

Options:

• AWS: EFS, FSx (Lustre)
• Azure: Azure NetApp Files, Azure Files, Lustre

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5. Networking

• Virtual Private Cloud (AWS) / Virtual Network (Azure)
• Security groups / NSGs
• High-speed networking (placement groups, accelerated networking)

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Basic Workflow

1. User connects to login node
2. Submits job using sbatch
3. Slurm provisions compute nodes (if not already running)
4. Job runs on allocated instances
5. Nodes are terminated after job completion (optional)

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Recommended Architecture Pattern

For most use cases:

• Persistent head/login node
• Auto-scaling compute nodes
• Shared parallel storage
• Private network with restricted access

This balances cost, performance, and manageability.

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Common Pitfalls (and How to Avoid Them)

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1. Ignoring Network Performance

Problem

Using standard cloud networking for MPI workloads.

Impact

• High latency
• Poor scaling across nodes

Fix

• Use placement groups (AWS) or proximity placement groups (Azure)
• Enable enhanced/accelerated networking
• Choose HPC-optimized instance types

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2. Storage Becomes the Bottleneck

Problem

Using basic network storage for high I/O workloads.

Impact

• Slow reads/writes
• Idle compute nodes

Fix

• Use parallel file systems (FSx for Lustre, Azure Lustre)
• Match storage throughput with compute scale

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3. Poor Auto-Scaling Configuration

Problem

Nodes take too long to start or are over-provisioned.

Impact

• Increased wait times
• Higher costs

Fix

• Tune scaling policies
• Keep a small number of warm nodes
• Use instance pools where possible

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4. Using the Wrong Instance Types

Problem

Choosing general-purpose VMs for HPC workloads.

Impact

• Lower performance
• Inefficient scaling

Fix

• Use compute-optimized or HPC-specific instances
• For GPUs, select instances with proper interconnect support

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5. Ignoring Cost Management

Problem

Leaving nodes running after jobs finish.

Impact

• Unexpected cloud bills

Fix

• Enable auto-termination of idle nodes
• Use spot/preemptible instances where suitable

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6. Not Handling Preemption (Spot Instances)

Problem

Using spot instances without fault tolerance.

Impact

• Job failures
• Lost progress

Fix

• Use checkpointing
• Combine on-demand + spot nodes

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7. Single Point of Failure (Head Node)

Problem

Head node goes down → entire cluster stops.

Fix

• Use backups or snapshots
• Consider failover strategies

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8. Security Misconfiguration

Problem

Open SSH access or weak network rules.

Impact

• Security risks

Fix

• Restrict access via VPN or IP whitelisting
• Use IAM roles and proper authentication

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9. Slow Job Startup Times

Problem

VM provisioning delays job execution.

Impact

• Poor user experience

Fix

• Pre-scale nodes
• Use lightweight images
• Optimize bootstrapping scripts

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10. Treating Cloud Like On-Prem

Problem

Applying static cluster design to a dynamic environment.

Impact

• Inefficiency
• Higher costs

Fix

• Design for elasticity
• Scale based on workload demand

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Real-World Example

Initial Setup:

• Static compute nodes
• Standard storage
• No placement group

Issues:

• Poor MPI scaling
• High costs

Improved Setup:

• Auto-scaling compute nodes
• FSx for Lustre storage
• Placement group enabled

Result:

• Better performance
• Reduced costs
• Faster job turnaround

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Final Thoughts

Running Slurm on AWS or Azure can be powerful, but it is not just about lifting and shifting your on-prem setup.

Success depends on:

• Choosing the right architecture
• Understanding cloud limitations
• Avoiding common pitfalls

With the right design, cloud-based Slurm clusters can deliver both flexibility and performance — without unnecessary cost.