GPU Computing
Develop the skills to measure a data-parallel computation on a GPU through a learning path centred on GPU Computing. Explore concepts, guided practice and an applied project.
Build practical GPU Computing skills.
Skills and knowledge developed in this course.
- GPU architecture
- Parallel workload
- Performance
- Compute lab
- Applied project: Measure a data-parallel computation on a GPU
- GPU Computing — validation and review
About this course
This course develops practical capability in GPU Computing. Its course-specific content covers GPU architecture, Parallel workload, Performance, Compute lab, followed by an applied project focused on measure a data-parallel computation on a gpu. 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 GPU Computing or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of emerging & specialized technologies. Prior experience with the named product helps with the practical exercises.
Measure a data-parallel computation on a GPU
Deliver measure a data-parallel computation on a gpu. 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.
01GPU architecture
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- Threads, blocks and execution units
- Host-device memory and transfer cost
Practical exerciseApply gpu architecture to the measure a data-parallel computation on a gpu and capture the validation evidence.
02Parallel workload
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- Data parallelism and kernel boundaries
- Coalesced access and divergence concepts
Practical exerciseApply parallel workload to the measure a data-parallel computation on a gpu and capture the validation evidence.
03Performance
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- Occupancy and launch overhead
- Profiling and correctness checks
Practical exerciseApply performance to the measure a data-parallel computation on a gpu and capture the validation evidence.
04Compute lab
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- Implement a bounded parallel operation
- Compare output and runtime against a CPU baseline
Practical exerciseApply compute lab to the measure a data-parallel computation on a gpu and capture the validation evidence.
05Applied project: Measure a data-parallel computation on a GPU
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- Define inputs, assumptions and acceptance criteria for: Measure a data-parallel computation on a GPU.
- Implement the project using gpu architecture, parallel workload, performance, compute lab.
Practical exerciseMeasure a data-parallel computation on a GPU.
06GPU 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
GPU architecture · Parallel workload
Establish the GPU Computing scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Performance · Compute lab
Apply the core GPU 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 measure a data-parallel computation on a gpu. - 04Learning milestone 4 In progress
Deliver the applied project
Complete measure a data-parallel computation on a gpu, 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 |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 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 ↗