GPU Architecture · Reference
Curated, verified sources for this course. The lessons draw their facts from here — not from guesses.
Tags mark the kind of source: official NVIDIA docs/whitepapers · book / course · paper measured microbenchmarks · community · in workspace a reference card living beside these lessons. Start with the Programming Guide and the "best first whitepaper," then follow the thread your question needs.
The ground-truth spec: kernels, threads, blocks, grids, warps, the SIMT model, and the full memory-space taxonomy. The "Programming Model" and "Hardware Implementation" chapters are the block→SM / warp→scheduler mapping. Reach for it when precise semantics matter.
The "why it's slow" companion — coalescing, occupancy, shared-memory bank conflicts. Reach for it after the Programming Guide.
Best first whitepaper. The most accessible, diagram-heavy explanation of CUDA cores / SM sub-partitions / warp schedulers (RTX 30-series, consumer).
Datacenter Ampere deep dive: SM layout, Tensor Cores, 40 MB L2, HBM2e.
Hopper SM, thread-block clusters, TMA, HBM3. The un-gated equivalent of the H100 whitepaper.
Where the GigaThread engine / global work-distribution engine and host interface are named (Lesson 7). Older, but the clearest statement of the two-level scheduler.
Hwu, Kirk, El Hajj (Morgan Kaufmann, ISBN 9780323912310). The single best linear read for a strong programmer new to GPUs — teaches architecture and the CUDA model together, and how they map.
The lecture counterpart to PMPP, taught by its author. Video playlist →
The two-path compilation trajectory, PTX vs cubin, -arch/-gencode, JIT. Ground truth for "what nvcc produces."
cuobjdump/nvdisasm, cubin ELF sections, disassembly. Use it to see the SASS/PTX/metadata embedded in your binary.
Contexts, primary-context lazy init, module management — the layering under libcudart.
Per-kernel HW-counter profiling, kernel replay, warp-stall reasons, roofline. Reach for it to answer "why is this kernel slow." Timeline sibling: Nsight Systems, built on CUPTI.
memcheck/racecheck/initcheck/synccheck — the correctness guardrail (replaces the removed cuda-memcheck).
The WSL2 GPU path (stub libcuda.so → /dev/dxg → dxgkrnl → VM bus → Windows driver). Directly relevant to this machine. Companion: Microsoft's "DirectX ❤ Linux."
Multi-tenancy & launch-overhead amortization (Lesson 9): MPS, MIG User Guide (datacenter-only, not GeForce), Getting Started with CUDA Graphs.
The pushbuffer / GPFIFO / doorbell mechanics — documented in NVIDIA's open sources (not the CUDA guide). open-gpu-kernel-modules →
Hyperlinked glossary split into Device Hardware / Device Software / Performance — its whole purpose is mapping hardware terms to CUDA software terms. Excellent quick lookup.
Peer-style pointer-chase studies measuring real latency/bandwidth at each memory level. Use for defensible cycle-latency and cache-size numbers when NVIDIA doesn't publish them. Companions: Hopper · Volta.
Ten iterative kernels, naive → ~94% of cuBLAS, each step teaching one hardware truth. Reach for it once the basics click and you want to feel how memory drives performance — this is the next hands-on for this course. code →
The reference layer living beside the ten lessons — open these locally.
Canonical nomenclature across all three axes (physical / logical / operational), one section per lesson.
Every tool the course touched plus the wider ecosystem (compile, inspect, profile, debug, monitor) — each with its data source, an example, and the best article — then end-to-end scenario tutorials. Verified against CUDA 13.3; deprecations flagged.
Pascal through Rubin — key differences per generation, the one reason each is worth its price premium, concrete products (V100/A100/H100/B200), the mapping to GeForce cards, and the $/token economics. Dense-vs-sparse and per-die-vs-per-rack traps flagged.
What of the course transfers to AMD hardware, what is only renamed, and the five real architectural divergences. Vocabulary map, lesson-by-lesson audit, tool equivalents, porting hazards, and AMD's primary documents.
Register-file numbers, occupancy budgets, and the two-budget rule.
The center of gravity for serious GPU/CUDA learners (PyTorch orbit) — lectures, reading groups, a kernel-benchmark bot. Best place to test understanding against practitioners. talks → · resources →
Official NVIDIA-run Q&A, often answered by staff. For precise, hardware-specific questions.