In development
GPU Programming with NVIDIA CUDA
Make GPU code correct first, then prove the performance work mattered.
Practice host/device flow, kernels, grids, blocks, threads, memory transfers, coalescing, timing, shared memory, occupancy, races, streams, and profiling.
Planned practice
Skills currently represented in the roadmap.
- Explain GPU execution geometry and memory flow
- Fix indexing, transfer, race, and memory-pattern bugs
- Prove performance claims only after correctness
- Reason about coalescing, shared memory, divergence, and profiling signals
Current roadmap
The detailed syllabus is not published yet.
The course is listed to collect launch interest without pretending the material is ready.
Course update
Ask for one launch notification.
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