NVIDIA CUDA & GPU Performance Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B43
Turn CPU-bound numerical work into measured GPU acceleration: CUDA execution and memory models, kernels, tiling, Triton/PyTorch extensions, Nsight profiling and performance validation.
Benchmark: The core CUDA course is publicly available from freeCodeCamp / Elliot Arledge, with additional public video instruction from Stanford, NVIDIA Developer and Computerphile. Zuyini independently curates these resources and applies its own build-and-proof rubric; this is not an NVIDIA, Stanford or freeCodeCamp-issued certification.
Core Video Sequence
01 — Complete ~12-hour CUDA programming course
02 — Parallel GPU mental model + execution hierarchy
03 — GPU architecture, grids/blocks + first kernels
04 — CUDA API, memory movement + optimized matrix multiply
05 — Kernels, Triton + XLA from language-model systems
06 — Triton kernels + PyTorch CUDA extensions
07 — Profile GPU workloads with Nsight Systems
08 — Timeline, NVTX, async work + latency hiding
09 — Current bridge: CUDA platform and new features
10 — BONUS conceptual bridge: what CUDA is and why it matters
Build-Along Checkpoints
- Checkpoint 1: Create a reproducible CPU baseline and GPU timing harness with warm-up, synchronization, correctness tolerances and p50/p95 runtime evidence.
- Checkpoint 2: Implement a CUDA kernel with explicit grid/block sizing, bounds checks and deterministic correctness tests; explain the execution hierarchy.
- Checkpoint 3: Optimize host-device movement, coalescing and shared-memory use; implement tiling on a bandwidth-heavy operation such as matrix multiplication or stencil.
- Checkpoint 4: Measure occupancy, achieved bandwidth and kernel timing across problem sizes; identify whether the workload is compute-, memory- or launch-bound.
- Checkpoint 5: Implement one equivalent kernel in Triton or as a PyTorch CUDA extension; compare developer complexity, portability and measured performance.
- Checkpoint 6: Profile with Nsight Systems, annotate with NVTX where useful, remove one verified bottleneck and add a regression benchmark that catches future slowdown.
7-Day Independent Capstone
- Day 1: Select a real numerical or ML workload; define correctness, latency/throughput target, hardware and a CPU/framework baseline.
- Day 2: Build the first CUDA kernel and test harness; validate results against the reference implementation across edge cases and shapes.
- Day 3: Optimize memory access, transfers and shared-memory/tiling behavior; record before/after profiler and benchmark evidence.
- Day 4: Build a Triton or PyTorch-extension variant; compare speed, readability, integration effort and failure modes on the same workload.
- Day 5: Use Nsight to locate stalls or under-utilization; apply one evidence-driven optimization and quantify the effect across multiple sizes.
- Day 6: Stress precision, shapes and concurrency/streams where relevant; identify the crossover point where GPU acceleration stops paying off.
- Day 7: Publish source, benchmark harness, profiler captures, architecture/optimization notes, demo and a concise performance decision memo.
CUDA - Build, Profile & Optimize GPU Workloads
Ship a correct and measurable GPU acceleration project: establish a CPU baseline, implement kernels, optimize memory behavior, compare CUDA/Triton approaches and prove the speedup with profiler evidence.
Proof Rubric — What Must Be Shown
- CORRECTNESS: reference parity + edge cases
- KERNELS: CUDA + Triton/extension comparison
- PARALLELISM: grid/block + execution rationale
- BENCHMARKS: repeatable speedup + scaling
- MEMORY: coalescing, transfers + tiling
- INTEGRATION: usable application or ML path
- PROFILE: Nsight evidence + diagnosis
- ENGINEERING: tradeoffs + regression guard