high performance computing (hpc)

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High Performance Computing (HPC) Training is designed for research engineers, data scientists, simulation analysts & system administrators who want to master parallel computing, cluster management & job scheduling for scientific & engineering workflows. This training builds skills to harness powerful computing resources for complex problem-solving & large-scale data processing. It is essential for professionals aiming to accelerate simulations, optimize computational performance & drive innovation in research & industry environments.

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Overview

The Syllabus

Curriculum Designed by Experts

Module 1: Introduction to HPC

• What is High Performance Computing?
• Evolution and relevance in Oil & Gas, Research, Genomics, AI
• HPC vs Cloud vs Supercomputers
• HPC architecture: nodes, cores, interconnects, storage
Hands-on:
• Explore HPC cluster layout and hardware components

Module 2: Linux for HPC Users

• Basic and advanced Linux commands
• Filesystem layout in shared clusters
• Using ssh, screen, tmux, environment variables
• Disk quotas, user limits, and scratch storage
Assignment:
• Create job folders and manage scratch space
• Bash script to automate simulation directory setup

Module 3: HPC Cluster Architecture

• Compute Nodes, Login Nodes, Head Nodes
• High-speed networks (InfiniBand, Ethernet)
• Parallel file systems: Lustre, BeeGFS, GPFS
• Cluster software stack: OS, compilers, libraries
Assignment:
• Understand and visualize node interconnection topology

Module 4: Compilers and Performance Libraries

• GCC, Intel, and PGI Compilers
• Math Libraries: OpenBLAS, MKL, LAPACK
• MPI and OpenMP Basics
• Linking scientific codes with external libraries
Hands-on:
• Compile a basic parallel C program with mpicc
• Test performance digerences with OpenMP threading

Module 5: Workload Management with Job Schedulers

• Resource Managers Overview: SLURM, PBS, LSF
• Job types: batch, interactive, array jobs
• Writing and submitting job scripts
• Managing and monitoring jobs
Hands-on:
• Write a SLURM script to run a simulation on 4 nodes
• Monitor job egiciency and runtime with sacct or qstat

Module 6: Parallel Programming Models

• Introduction to MPI (Message Passing Interface)
• Introduction to OpenMP (Shared Memory)
• Hybrid MPI + OpenMP concepts
• Profiling tools: gprof, perf, Intel VTune
Assignments:
• Write a simple MPI hello-world and scale it
• Optimize a sample Fortran/OpenMP CFD routine

Module 7: HPC and Cloud Integration

• Hybrid HPC setups with AWS ParallelCluster / Azure Batch
• Job orchestration and cost optimization
• Burst compute workloads to cloud clusters

Module 8: Security, Storage & Data Management

• LDAP/AD integration for user auth
• Scratch vs project vs archive storage
• Backup & archival procedures (HPSS, object storage)
• Data security in research and simulations

Module 9: Monitoring and Cluster Administration (For Admins)

• Ganglia, Grafana, Prometheus for HPC metrics
• Node health monitoring and alerting
• Managing SLURM partitions and QoS
• Troubleshooting node crashes and job failures

Module 10: Scientific & Industry Workflows

• Running software like ANSYS, MATLAB, Gaussian, OpenFOAM, GROMACS
• Workflow management tools: Snakemake, Nextflow, Singularity containers
• Containerizing simulation workloads for reproducibility

Capstone Project Ideas

Choose one of the following:
• Performance benchmarking of MPI-based reservoir simulator on 4 vs 64 cores
• Automate job submission and monitoring for CFD simulation with SLURM + Bash
• Containerize a data analytics job using Singularity and run on the cluster

Training Deliverables

• 10 Hands-on Labs + 1 Capstone
• Real-world cluster usage scenarios
• SLURM scripting templates
• Performance tuning cheat sheets
• Optional: Access to a simulated HPC environment

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