HPC Configuration: How Configuration Management Can Enhance AI Workload Efficiency

High-performance computing (HPC) configuration has become paramount for AI workloads. Companies investing in AI workloads often find themselves with predictable problems: Lots of nodes that are hard to integrate and harder to manage together, under-utilized resources, deployment struggles, inconsistent configurations, and drift.

Using configuration management to control it all isn’t a new concept — but best practices for this relatively new application are scarce, leaving plenty of organizations with HPC environments to figure it out on their own.

As it turns out, configuring HPC clusters for training AI models is a perfect use case for great configuration management. In this post, we’ll cover the topic of HPC configuration and explain how enforcing a desired infrastructure state can help you manage, optimize, and scale HPC clusters better.

What is HPC Configuration?

High-performance computing (HPC) configuration means setting up and tuning infrastructure components of an HPC environment to maintain groups of servers (clusters). HPC configuration includes settings for hardware, software, networking, and security that maximize performance and ensure reliability of an HPC cluster.

Companies use HPC (sometimes called supercomputing) because it gives them the computational power to analyze huge amounts of data, conduct large-scale simulations, and train large language models for AI.

HPC is a pretty big investment for any business (2024 estimates indicate an HPC market size of $50 billion USD) with potential for huge ROI, which is one of the reasons HPC configuration management is so important. Configuration management helps protect infrastructure investments in the long term by cutting down on the manual work needed to keep them running optimally and securely.

Benefits of Configuration Management for HPC: Optimization, Scalability, Security & Compliance

Configuring an HPC environment the right way — and keeping it that way — improves HPC performance with better resource utilization. Configuration management also makes it easier to scale an HPC cluster by centralizing, standardizing, and enforcing consistency among HPC nodes.

Because configuration management tools can repeat baseline configurations, they also help keep the whole HPC environment secure by enforcing configurations for the security automation tools used to ensure HPC security. A configuration management system (CMS) also streamlines HPC compliance by making sure each node’s configurations adhere to regulatory standards.

And on the other end, a configuration management system makes auditing easier, cleaner, and faster by automatically documenting every system change (whether intentional or corrective).

Why is HPC Configuration Important for AI Workloads?

Configuring HPC for AI helps balance performance, resource management and optimization, and security and compliance. Configuring HPC for AI should include configuring storage, parallel computing libraries, GPU drivers, networks to support large data transfers, system hardening to limit access, and security to protect data privacy.

HPC environments are commonly used to train, deploy, and manage AI models. This is because AI workloads require massive computational resources, huge datasets, and a wide diversity of resources. Configuring an HPC environment the right way makes sure it can support an AI workload’s specialized infrastructure, scalability, and efficiency requirements.

The Two Broad Kinds of HPC Configuration

Speaking generally, you can lump HPC configuration into two broad stages or steps.

1. Creating and using a master image as a template for the nodes in a cluster
2. Managing configurations on those servers to address security, compliance, software needs, and policy adjustments

Creating an HPC Cluster Master Image

Configuring a master image for an HPC cluster is crucial, as it acts as the template for nodes in the cluster (whether they’re servers, VMs, and/or containers). Here are a few key elements in this step:

• Setting up a reference node
• Installing and configuring the operating system
• Partitioning the disk for the purposes of the node
• Installing software, libraries, and dependencies
• Mounting shared file systems and configuring access to data lakes
• Applying system and network configurations
• Testing to make sure it all works as designed
• Creating the image with imaging tools, virtualization, or containerization
• Deploying the image to the cluster nodes

Managing HPC Cluster Configurations After Deploying the Master Image

After the master node image is deployed, it’s crucial to manage configurations of a HPC cluster. The master image provides a baseline configuration for the rest of the nodes, but post-deployment configuration covers pretty much everything after that stage. Things that initial HPC configuration doesn’t cover include:

• Configurations specific to the role of each node type. Compute nodes need GPU drivers and AI frameworks, while storage nodes need setups specific to their file system.
• Settings specific to each cluster. Each node in a cluster has its specific purpose, and each cluster has its own purpose in the overall HPC environment: simulations, modeling, training, data processing, unique mixes of each. As such, settings like job scheduling, networking, storage integration, user policies, and other configurations will vary between clusters.
• Software updates, AI framework installs, and patch management.
• Optimizing performance. Optimizing GPU and memory usage, adjusting parallel file system parameters based on access patterns, and fine-tuning job schedulers for workload efficiency.
• Security and compliance. Adding access controls for new users, updating configurations (like data encryption and auditing) to comply with specific regulations, and enforcing system hardening configurations aligned to organizational compliance policies.
• Ensuring scalability. An HPC environment rarely stays the same size for long. Ongoing configuration makes integrating new nodes seamless, and keeps the whole cluster updated with the latest changes to storage, scheduling, or workload distribution.

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Important HPC Configurations for AI Workloads

Here are some configurations for ensuring the HPC environment is optimized for AI workloads:

• Node Configurations: GPU drivers like NVIDIA, parallel computing libraries like MPI and OpenMP, and container support like Docker
• Storage: Parallel file systems that can handle large datasets (like Lustre and GPFS), data caching, data masking or encryption, and temporary high-speed scratch storage for data used during computational tasks
• Networking: Ethernet, segmentation to separate AI workloads from other cluster traffic, and enabling direct memory access using remote direct memory access (RDMA)
• Resource Management: Job schedulers like SLURM (Simple Linux Utility for Resource Management) and dynamic allocation of resources based on the needs of various AI workloads running on the HPC cluster
• Security: System hardening measures like role-based access control (RBAC), multi-factor authentication (MFA), protections for AI input/output data, patching and updates, and logging

HPC Configuration Tools

How Puppet Helps Manage, Maintain, Scale & Secure HPC Environments

Puppet makes it easier and more cost-effective to manage configurations, add nodes, update cluster configurations, install packages, apply patches, enforce security settings, align to compliance policies, and more. Puppet’s agent-based automation maintains the desired state of thousands of nodes at once, with automated change documentation and reporting for audits.

Puppet is the automation and configuration management platform of choice for managing huge infrastructure. Puppet keeps server configurations, VM configurations, OS configurations, and app configurations in your desired state across data centers and cloud environments. And updating them is as straightforward as changing your Puppet code. That means every cluster node and cluster in your HPC environment can be kept in its ideal configuration

Centralized HPC Configuration Management

Using Puppet to define your infrastructure as code means you can manage the configuration state of every node and HPC cluster from one centralized location. It’s a central repository for configuration code; a single source of truth for how different types of nodes should be configured.

Any time you make an approved change to that code, Puppet will deploy your new code to the relevant resources every 30 minutes, whether that’s updating settings for one VM or for every node managed by Puppet. Unauthorized changes on managed nodes and configuration drift are remediated on the same schedule — 48 times every day by default — so you can be sure every managed node stays in its desired state.

HPC Resource Allocation

With desired state automation, Puppet maintains a record of CPU settings, RAM settings, and other configurations related to how your HPC uses resources. Once you configure those settings, Puppet keeps them in that state (either in the Puppet database or in a CMDB like ServiceNow), which helps ensure consistent record-keeping of HPC performance.

HPC Compliance

HPC environments are subject to many of the same compliance regulations as any environment that handles sensitive data (and more specifically made to set expectations for configuring AI environments). Compliance is a metric of the controls you put in place to secure that environment — things like your RBAC policies, MFA enforcement, your patch management process, and more.

Puppet lets you fine-tune and manage each of those controls with automation, and it documents each configuration change, update, patch, and remediation as part of your version control. That means it helps you meet and prove compliance with existing frameworks like CIS Benchmarks, PCI-DSS, the NIST framework (including its AI provisions), NIS2, and more.

When you define HPC configurations as infrastructure code, you’re also saving huge amounts of time when your compliance configurations need to change. As more organizations start to take on AI workloads, regulatory pressure around the training and handling of AI is likely to increase. That’ll mean new regulations, frameworks, and directives to secure your AI environment. Every node you use to store, process, and deliver data will need to be updated eventually — with or without centralized configuration management.

Puppet Enterprise Advanced includes a premium feature that maintains up-to-date configurations to stay compliant with CIS Benchmarks and DISA STIGs automatically, no matter where you’re deployed. Get info about Security Compliance Enforcement >>

HPC Scalability

When you need to add or update nodes in a cluster, Puppet can automatically apply baseline configurations, install necessary software, and set up network settings and security policies to make sure those nodes align with the cluster's existing standards. Puppet can apply updates to configurations simultaneously across thousands of nodes and then maintain them no matter how your cluster scales.

AI workloads often dictate scaling your HPC clusters, with varying degrees of resource intensity depending on the stage (e.g., processing, training, inference). Puppet can configure certain nodes to include specific libraries or tools required for AI/ML training, maintain CPU and GPU configurations between nodes of the same type, and automatically enforce consistent dependencies within the cluster.

HPC Auditing

Because Puppet lets you express your infrastructure as code, every change is documented in the change record. That gives you a comprehensive trail of authorized and unauthorized changes, who made them and when, and what Puppet did to restore your desired state remediation efforts. Automated reporting cuts down on paperwork and prep time for security and compliance audits.

Case Study: Puppet for HPC Configuration

As the AI race heated up, an HPC service provider chose Puppet to manage the HPC infrastructure supporting its AI workloads. The company provides AI research, training, tuning, inferencing, and enablement services to enterprises.

Facing constant threats and a volatile regulatory landscape, the company needed to enforce HPC configurations in line with CIS Benchmarks across their on-premises data center infrastructure. While the company considered alternatives like Ansible Tower, Puppet’s ability to continuously and reliably enforce CIS Benchmarks made it the clear choice for managing their HPC environment.

Now, Puppet’s desired state automation capabilities allow the organization to lock in their perfect compliance state across thousands of servers at once. That means each new server, VM, and container has exactly the configurations it needs to stay compliant with internal and external policies, saving massive amounts of time on configuration and drift remediation.  

If you’re investing in AI workloads, you’re probably already managing thousands of diverse nodes. To keep it all performant, secure, and under control, you’ll need a configuration management tool that’s up to the task. Get a demo of Puppet now to learn how Puppet can streamline your HPC configuration management.

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