High Performance Computing
Develop the skills to plan a scalable HPC workload with reproducible measurements through a learning path centred on High Performance Computing. Explore concepts, guided practice and an applied project.
Build practical High Performance Computing skills.
Skills and knowledge developed in this course.
- HPC environment
- Parallel models
- Execution
- Scaling study
- Applied project: Plan a scalable HPC workload with reproducible measurements
- High Performance Computing — validation and review
About this course
This course develops practical capability in High Performance Computing. Its course-specific content covers HPC environment, Parallel models, Execution, Scaling study, followed by an applied project focused on plan a scalable hpc workload with reproducible measurements. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with High Performance Computing or plan to take on responsibilities in this area.
Before you start
Working knowledge of emerging & specialized technologies and practical familiarity with the named platform or subject. Review the course topics before enrolment to confirm the lab starting point.
Plan a scalable HPC workload with reproducible measurements
Deliver plan a scalable hpc workload with reproducible measurements. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01HPC environment
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- Nodes, processors and accelerators
- Batch scheduler and resource requests
Practical exerciseApply hpc environment to the plan a scalable hpc workload with reproducible measurements and capture the validation evidence.
02Parallel models
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- Shared and distributed memory concepts
- MPI and threaded workload responsibilities
Practical exerciseApply parallel models to the plan a scalable hpc workload with reproducible measurements and capture the validation evidence.
03Execution
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- Modules, environment and job scripts
- Input staging and result collection
Practical exerciseApply execution to the plan a scalable hpc workload with reproducible measurements and capture the validation evidence.
04Scaling study
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- Run a bounded workload at multiple sizes
- Report efficiency, bottlenecks and reproducibility details
Practical exerciseApply scaling study to the plan a scalable hpc workload with reproducible measurements and capture the validation evidence.
05Applied project: Plan a scalable HPC workload with reproducible measurements
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- Define inputs, assumptions and acceptance criteria for: Plan a scalable HPC workload with reproducible measurements.
- Implement the project using hpc environment, parallel models, execution, scaling study.
Practical exercisePlan a scalable HPC workload with reproducible measurements.
06High Performance Computing — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
HPC environment · Parallel models
Establish the High Performance Computing scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Execution · Scaling study
Apply the core High Performance Computing techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for plan a scalable hpc workload with reproducible measurements. - 04Learning milestone 4 In progress
Deliver the applied project
Complete plan a scalable hpc workload with reproducible measurements, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| SRE / Observability Engineer | ₹8–14 LPA | ₹18–32 LPA | ₹35–58 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗