High Performance Computing (HPC) | Deep Dive.

 

A deep dive into High Performance Computing (HPC).

Scope:

•        Architecture,
•        Components,
•        Workloads,
•        Scheduling,
•        Storage,
•        Networking,
•        Cloud HPC,
•        DevOps/DevSecOps integration, 
•        Modern trends like GPU computing,
•        AI-driven optimization.

Breakdown:

•        HPC Core Concepts,
•        HPC Architecture Overview,
•        HPC Networking,
•        HPC Storage Systems,
•        HPC Workload Scheduling & Resource Management,
•        HPC Software Stack,
•        HPC & Cloud,
•        HPC vs Cloud Native / Kubernetes,
•        HPC Security (DevSecOps Perspective),
•        HPC Monitoring & Observability,
•        HPC Use Cases,
•        Modern Trends in HPC,
•        HPC for DevOps/DevSecOps/Cloud Engineers.

Intro:

•        High Performance Computing (HPC) refers to the practice of aggregating computing power to solve large-scale, complex problems at extremely high speeds.
•        High Performance Computing (HPC) often involves parallel processing across clusters of powerful CPUs, GPUs, or specialized accelerators.
•        HPC is a foundation for scientific research, engineering simulations, AI model training, financial modeling, and large-scale analytics.

1. HPC Core Concepts

1.1 Parallelism Types

1.     Shared-Memory Parallelism (SMP)

•    Multiple cores share the same memory space.
•    Typically implemented via OpenMP.

2.     Distributed-Memory Parallelism

•    Each node has its own memory.
•    Nodes communicate via high-speed interconnects like InfiniBand.
•    Implemented via MPI (Message Passing Interface).

3.     Hybrid Parallelism

•    Combination of MPI + OpenMP (or CUDA/OpenACC for GPUs).
•    Common in large scientific simulations.

2. HPC Architecture Overview

2.1 Node-Level Architecture

A typical compute node includes:

•         Multi-core CPU (Intel Xeon, AMD EPYC)
•         High-bandwidth RAM
•         Optional GPU/Accelerators (NVIDIA A100/H100, AMD MI250)
•         High-speed NIC (100–400 Gbps)

2.2 Cluster Architecture

An HPC cluster typically consists of:

1.     Head/Login Node

•    User access, job submission, environment setup.

2.     Compute Nodes

•    Run jobs; large numbers (hundreds to thousands).

3.     GPU Nodes

•    Specialized for AI/ML, deep learning, compute-heavy kernels.

4.     Storage Nodes

•    Parallel file systems (Lustre, GPFS).

5.     Interconnect

•    High-speed, low-latency networks (InfiniBand HDR/EDR).

6.     Management Nodes

•    Provisioning, monitoring, orchestration.

3. HPC Networking

3.1 InfiniBand

•         High bandwidth: 100 Gbps to 400 Gbps.
•         Extremely low latency (<1µs).
•         Critical for MPI workloads.

3.2 Ethernet in HPC

•         25/40/100/400 Gbps Ethernet.
•         Used more in cloud-based HPC systems.

4. HPC Storage Systems

4.1 Parallel File Systems

Used for large-volume, high-throughput workloads:

•         Lustre
•         GPFS / IBM Spectrum Scale
•         BeeGFS

4.2 Burst Buffers

• High-speed SSD layer between compute nodes and storage.

4.3 Object Storage

Cloud-oriented HPC uses:

•         Amazon S3
•         Azure Blob
•         Google Cloud Storage

5. HPC Workload Scheduling & Resource Management

5.1 Job Schedulers

Schedulers allocate compute resources, queue jobs, and optimize cluster usage:

•         SLURM (industry standard)
•         PBS Pro / Torque
•         LSF
•         Grid Engine

5.2 Scheduler Features

•         Resource allocation (CPU, GPU, memory)
•         MPI job orchestration
•         Fair-share scheduling
•         Preemption
•         Job arrays
•         Accounting (SAcct for SLURM)

6. HPC Software Stack

Programming Models

•         MPI (Distributed)
•         OpenMP (Shared)
•         CUDA (NVIDIA GPUs)
•         HIP/ROCm (AMD GPUs)
•         OpenACC (Portable acceleration)
•         SYCL/DPC++ (Intel GPUs and heterogeneous systems)

Compilers

•         GCC
•         Intel OneAPI
•         NVIDIA HPC SDK
•         pgcc/pgfortran

Libraries

•         BLAS / LAPACK
•         ScaLAPACK
•         FFTW
•         PETSc
•         Tensor libraries for AI/ML

7. HPC and Cloud

• Cloud HPC is now common due to elasticity and high availability.

AWS HPC Components

•         EC2 H-series (HPC-optimized)
•         ParallelCluster
•         FSx for Lustre
•         EFA (Elastic Fabric Adapter) – HPC-grade networking
•         Batch + Slurm integration

Azure HPC

•         HB, HC, NDv2 (GPU)
•         Cray supercomputers on Azure

GCP HPC

•         HPC VM families
•         Slingshot networking (Cray)
•         Filestore High Scale

Cloud HPC Use Cases

•         Burst workloads
•         Scalable AI/ML training
•         Large simulations without on-prem CapEx

8. HPC vs Cloud Native / Kubernetes

Kubernetes is not natively optimized for HPC MPI-style jobs. However, HPC and K8s are converging via:

•         KubeFlow
•         Volcano Scheduler (batch/HPC workloads)
•         Run:AI
•         NVIDIA GPU Operator

NB:

• Still, traditional HPC workloads favor SLURM + bare metal or high-performance VM clusters.

9. HPC Security (DevSecOps Perspective)

Key security focus areas:

•         Node hardening
•         Encrypted storage and communication
•         IAM integration (LDAP, AD, AWS IAM)
•         Zero-trust for multi-tenant research clusters
•         Secret management for distributed jobs
•         Container security (Singularity/Apptainer)
•         Network segmentation for MPI traffic

Containers in HPC

•         Apptainer/Singularity is widely adopted:
•    Portable
•    Secure (non-root containers)
•    Reproducible for scientific workflows

10. HPC Monitoring and Observability

Tools include:

•         Grafana + Prometheus
•         Slurm accounting & metrics exporters
•         Elastic Stack
•         NVIDIA DCGM for GPU telemetry
•         InfluxDB integrations
•         Ganglia (legacy but still in use)

11. HPC Use Cases

Scientific & Engineering

•         Computational Fluid Dynamics (CFD)
•         Weather and climate modeling
•         Molecular dynamics (GROMACS, LAMMPS)
•         Quantum chemistry (Gaussian, ORCA)
•         Astrophysics simulations

AI / Machine Learning

•         Massive model training on GPU clusters
•         Distributed deep learning (Horovod, DeepSpeed)

Business & Enterprise

•         Risk modeling and Monte Carlo simulations
•         Fraud detection
•         Large-scale data analytics

12. Modern Trends in HPC

✔ GPU-first architectures
✔ AI accelerators (TPUs, AWS Trainium, Habana Gaudi)
✔ Exascale computing
✔ Serverless HPC (emerging concept)
✔ Quantum HPC hybrid workloads

13. HPC for DevOps/DevSecOps/Cloud Engineers

• As someone in DevOps/DevSecOps/Cloud Engineering, HPC ties into twtech world through:

Infrastructure-as-Code for HPC

•         Terraform for HPC cluster provisioning
•         AWS ParallelCluster automation
•         Azure CycleCloud templating

CI/CD for Scientific Workflows

•         Building and validating scientific codes
•         Containerizing HPC applications

Observability & Telemetry

•         Cluster efficiency tracking
•         GPU utilization metrics
•         Job success/failure analytics

Security automation

•         Compliance
•         Key management
•         Cluster isolation