High Performance Computing (HPC) | Deep Dive.

High Performance Computing (HPC) - Deep Dive.

Scope:

• Intro,       
• 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, 
• specialized accelerators.
•  HPC is a foundation for:
• Scientific research, 
• Engineering simulations, 
• AI model training, 
• Financial modeling, 
• Large-scale analytics.

1. HPC Core Concepts

1.1 Parallelism Types

A.     Shared-Memory Parallelism (SMP)

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

B.     Distributed-Memory Parallelism

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

C.     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:

A.     Head/Login Node

• User access, job submission, environment setup.

B.     Compute Nodes

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

C.     GPU Nodes

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

D.     Storage Nodes

• Parallel file systems (Lustre, GPFS).

E.     Interconnect

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

F.     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
• 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