CUDA Programming Course
This course equips learners with essential skills for developing high-performance CUDA applications, covering GPU architecture, optimization techniques, profiling, and scaling strategies for numeric workloads.

4 to 360h flexible workload
certificate valid in your country
What will I learn?
This CUDA Programming Course provides practical, performance-oriented skills to design, optimize, and benchmark high-speed GPU kernels. You will learn GPU architecture, memory hierarchy, warps, and synchronization, then apply coalesced access, shared memory, streams, and vectorized loads to real numeric workloads. Finish with profiling, multi-GPU scaling, and a production-ready checklist for robust, reproducible results.
Elevify advantages
Develop skills
- Optimize CUDA kernels: tune blocks, streams, and memory for fast numeric code.
- Master GPU memory: shared, global, caches, and coalesced access patterns.
- Profile CUDA apps: use Nsight tools to find and fix real performance bottlenecks.
- Design robust experiments: benchmark kernels, measure speedup, and report clearly.
- Scale GPU workloads: apply multi-GPU, unified memory, and kernel fusion tactics.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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