How to Build a High-Performance Computing Cluster on the Cloud

For decades, High-Performance Computing (HPC) was the exclusive domain of organizations with the capital to build and maintain sprawling, power-hungry, on-premise supercomputers. The barriers to entry—massive procurement costs, long deployment cycles, and specialized facility management—kept compute-intensive workloads like genomic sequencing, computational fluid dynamics (CFD), and complex financial modeling out of reach for many.

The cloud has fundamentally democratized HPC. By providing on-demand access to bare-metal performance, specialized accelerators (GPUs, FPGAs), and ultra-low-latency networking, cloud platforms (AWS, GCP, Azure) allow engineers to provision and dismantle complex clusters in minutes, not months. This "Elastic HPC" (E-HPC) model shifts the primary challenge from physical infrastructure management to sophisticated infrastructure orchestration.

This article is a technical blueprint for CTOs and senior engineers. We will dissect the architectural components, practical implementation steps, and critical performance considerations for building a robust, scalable, and cost-effective HPC cluster in a cloud environment. We will bypass high-level marketing and focus on the engineering decisions required for success.

Core Architectural Pillars of a Cloud HPC Cluster

A functional cloud HPC cluster is not a single service but a tightly integrated system of four key components. The performance of the entire system is dictated by its weakest link.

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Compute: Specialized Virtual Machines

The core of the cluster is its compute fleet. Generic VMs will not suffice. You must select instance families specifically engineered for HPC workloads.

• CPU-Bound: Look for instances with high clock speeds, a high core count, and a fixed, non-hyperthreaded physical core-to-vCPU mapping.
  • AWS: hpc7g, c7g (Graviton), c6i (Intel), c6a (AMD)
  • Azure: HBv3, HBv4, HC series (AMD)
  • GCP: c2, c2d, h3 series
• GPU-Bound (AI/ML/Rendering): Instances equipped with data center-grade GPUs and high-speed interconnects like NVLink.
  • AWS: p4, p5 series (NVIDIA A100/H100)
  • GCP: a2, a3 (A100), g2 (L4)
  • Azure: ND series (A100/H100)

Critical Consideration: Placement Groups (AWS), Proximity Placement Groups (Azure), or Compact Placement Policies (GCP) are non-negotiable. These policies ensure your compute instances are physically co-located within the data center (e.g., on the same rack) to guarantee the lowest possible node-to-node latency.

Networking: The Low-Latency Fabric

Standard TCP/IP networking, even with 100Gbps interfaces, introduces unacceptable latency and CPU overhead for tightly coupled HPC workloads that rely on the Message Passing Interface (MPI). The solution is a high-bandwidth, low-latency "fabric" that bypasses the kernel.

• AWS Elastic Fabric Adapter (EFA): A custom OS-bypass network interface that leverages the Scalable Reliable Datagram (SRD) protocol. It's designed for MPI and ML workloads and exposes a libfabric API.
• Azure InfiniBand: The HB and HC series instances provide direct access to dedicated, low-latency InfiniBand (HDR) networks, the de facto standard in on-premise supercomputing.
• GCP High-Performance Networking: GCP's h3 VMs use the Google Virtual NIC (gVNIC) and a "Jupiter" network backbone, which, while still Ethernet-based, is highly optimized for HPC and MPI traffic.

Your choice of instance (Pillar 2.1) is often dictated by the networking fabric (Pillar 2.2) it supports.

Storage: The Parallel File System

A defining feature of an HPC cluster is a shared, high-throughput, POSIX-compliant file system accessible by all compute nodes. This is where your input data resides and where simulation outputs are written.

• Requirement: High IOPS, high throughput (hundreds of GB/s), and low metadata latency for handling operations from thousands of concurrent clients.
• Primary Solution: Parallel File Systems.
  • Amazon FSx for Lustre: A fully managed service providing a Lustre file system, the most popular parallel file system in HPC. It integrates with S3, allowing you to "lazy load" data from object storage on first read and "write-back" results.
  • Alternatives: Managed BeeGFS (Azure) or self-managing GlusterFS or WekaIO on a fleet of storage-optimized VMs.
• Storage Hierarchy:
  1. Object Storage (S3/GCS/Blob): The "cold" persistent layer for your master datasets.
  2. Parallel File System (FSx for Lustre): The "hot" working directory for the cluster, linked to S3.
  3. Local Instance Storage (NVMe): The "scratch" space on each compute node for temporary, high-speed I/O.

Orchestration: The Workload Manager

You cannot manually manage thousands of cores. A workload manager (or job scheduler) is the cluster's brain. It manages the queue of jobs, provisions/de-provisions resources, and allocates nodes to specific tasks.

• Slurm (Simple Linux Utility for Resource Management): The open-source standard. It's powerful, highly configurable, and what most researchers and engineers are familiar with.
• Cloud-Native Orchestrators:
  • AWS ParallelCluster: An open-source tool that abstracts the entire cluster deployment (VPC, Slurm, FSx, Auto Scaling Groups) into a single YAML configuration file. This is the recommended starting point on AWS.
  • Azure CycleCloud: A graphical tool for orchestrating and managing HPC clusters using various schedulers (Slurm, PBS Pro, etc.).
  • GCP Batch: A managed service for submitting and running batch jobs, which can provision and manage HPC-style clusters under the hood.

Implementation Blueprint: Building a Slurm Cluster with AWS ParallelCluster

We will use AWS ParallelCluster as our primary example because it codifies the best practices for most of the components discussed.

Phase 1: Configuration (cluster-config.yaml)

ParallelCluster uses a single YAML file to define the entire stack.

# cluster-config.yaml
# This configuration defines a scalable Slurm cluster with an FSx for Lustre file system.

Region: us-east-1
Image:
  Os: alinux2 # Amazon Linux 2
HeadNode:
  InstanceType: c6i.xlarge
  Networking:
    SubnetId: subnet-0123456789abcdef0 # A public subnet for SSH access
  Ssh:
    KeyName: my-hpc-key

Scheduling:
  Scheduler: slurm
  SlurmQueues:
    - Name: compute-queue
      ComputeResources:
        - Name: hpc-nodes
          InstanceType: hpc6a.48xlarge # HPC-optimized instance
          MinCount: 0
          MaxCount: 16 # Scale up to 16 nodes (1536 cores)
          DisableSimultaneousMultithreading: true # Critical for HPC: 1 core = 1 vCPU
      Networking:
        SubnetIds:
          - subnet-fedcba9876543210f # A private subnet
        PlacementGroup:
          Enabled: true # Automatically create and manage a Placement Group
        ComputeNetwork:
          Efa:
            Enabled: true # Enable Elastic Fabric Adapter
SharedStorage:
  - Name: fsx-lustre
    StorageType: FsxLustre
    MountDir: /fsx
    FsxLustreSettings:
      StorageCapacity: 1200 # 1.2 TiB
      DeploymentType: SCRATCH_2 # High-performance scratch file system
      DataRepositoryConfiguration:
        ImportPath: s3://my-hpc-data-bucket/ # Link to S3 bucket
        AutoImport: true

Phase 2: Deployment

Deployment is a single CLI command.

# Ensure you have the AWS CLI and ParallelCluster installed
# pip install aws-parallelcluster
pcluster create-cluster --cluster-name my-hpc-cluster --cluster-configuration cluster-config.yaml

This command will take 15-20 minutes to provision the VPC (if not pre-existing), the Head Node, the FSx file system, and the Auto Scaling Group configurations. Initially, the MinCount: 0 ensures no compute nodes are running, saving costs.

Phase 3: Job Execution Example (MPI)

Submit the Job:

[ec2-user@head-node ~]$ cd /fsx
[ec2-user@head-node fsx]$ sbatch submit_job.sh
Submitted batch job 1234

Create a Slurm Submission Script: This script tells the scheduler what resources are needed.

# /fsx/submit_job.sh

#!/bin/bash

#SBATCH --job-name=mpi-hello
#SBATCH --output=/fsx/mpi-hello-%j.out  # %j is the job ID
#SBATCH --error=/fsx/mpi-hello-%j.err
#SBATCH --nodes=4                       # Request 4 nodes
#SBATCH --ntasks-per-node=96            # hpc6a.48xlarge has 96 physical cores
#SBATCH --cpus-per-task=1
#SBATCH --partition=compute-queue       # The queue defined in our YAML

# Load the MPI module environment
module load openmpi

# Run the MPI application
# srun will coordinate with Slurm to launch the processes across the 4 nodes
echo "Starting MPI job on $SLURM_JOB_NUM_NODES nodes..."
srun python3 /fsx/hello_mpi.py
echo "MPI job finished."

Create an MPI Program: We'll use a simple Python example with mpi4py. The head node (via ParallelCluster) comes with MPI and Slurm pre-configured.Python

# /fsx/hello_mpi.py
# This file is on the shared file system, accessible by all nodes.

from mpi4py import MPI

comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()

print(f"Hello from rank {rank} of {size} on host {MPI.Get_processor_name()}", flush=True)

SSH into the Head Node: This is your submission-and-control node.

pcluster ssh --cluster-name my-hpc-cluster -i ~/.ssh/my-hpc-key.pem

What Happens Next? (The "Elastic" Part)

1. Slurm receives the job and sees it needs 4 nodes.
2. The compute-queue currently has 0 nodes.
3. Slurm communicates with the ParallelCluster daemons, which in turn make an API call to the Auto Scaling Group.
4. The ASG provisions 4 new hpc6a.48xlarge instances inside the specified Placement Group.
5. As these nodes boot, their user-data scripts automatically configure them, install EFA drivers, mount /fsx, and join the Slurm cluster.
6. Once the nodes are READY, Slurm assigns the job. srun executes the Python script across all 384 cores (4 nodes * 96 cores/node).
7. The output is written to /fsx/mpi-hello-1234.out.
8. After the job completes, the cluster's idle timer starts. If no new jobs are queued for (e.g.) 10 minutes, ParallelCluster terminates the 4 compute nodes, shrinking the cost back to zero.

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Critical CTO-Level Considerations

Cost vs. Performance: The Spot Instance Dilemma

HPC workloads are often ideal for Spot Instances, which can offer up to 90% cost savings. However, this introduces the risk of termination.

• Strategy: Implement checkpointing within your application. The simulation must be able to save its state periodically to the parallel file system.
• Slurm & Spot: Configure Slurm to detect node terminations (preemption) and automatically re-queue the job. AWS ParallelCluster supports this natively. You can create a "Spot" queue and a "On-Demand" queue, allowing users to choose cost-savings vs. guaranteed runtime.

Data Gravity and Ingress/Egress

Your compute cluster is fast, but how do you feed it? A 50 TB dataset cannot be transferred over a standard internet connection.

• Ingress: Use physical transfer devices (AWS Snowball, Azure Data Box) or dedicated high-speed interconnects (AWS Direct Connect, Azure ExpressRoute) to ship data directly from your on-premise environment to object storage (S3).
• Egress: Egress costs are a major financial consideration. Plan to perform as much post-processing and visualization within the cloud as possible. Generate summary reports, graphs, or reduced datasets before pulling data back on-premise.

Software Environment and Reproducibility

An MPI job compiled on the head node may not run on a compute node if the libraries differ.

• Solution 1: Containerization. Use Singularity/Apptainer (preferred in HPC over Docker due to its rootless, more secure model). Build a container with your entire software stack (e.g., specific Python, MPI, and library versions), store it on /fsx, and run it via Slurm.
• Solution 2: Environment Modules. Use spack or easybuild to create module files (module load ...) that ensure a consistent, reproducible software environment. AWS ParallelCluster AMIs come with this pre-configured.

Conclusion

Building an HPC cluster in the cloud fundamentally re-frames the engineering challenge. The focus shifts from hardware procurement and physical data center management to automation, orchestration, and financial engineering.

By leveraging cloud-native tools like AWS ParallelCluster or Azure CycleCloud, a small team of engineers can command a supercomputer that rivals national labs, paying only for the femtoseconds of compute they consume. The architectural blueprint is clear: combine HPC-optimized instances, a low-latency network fabric, a high-throughput parallel file system, and an intelligent workload manager. Success is no longer measured by FLOPS alone, but by the elasticity of your infrastructure and the speed at which your organization can move from raw data to actionable insight.