Official NVIDIA Developer & Educator teaching kits, university course materials, and technical resources on parallel computing and CUDA GPU programming.
NVIDIA's Educator and Developer portal provides curated university teaching materials, parallel programming slides, and reference guides on thread hierarchies (grids, blocks, warps), shared memory tiling, coalesced global memory access, and hardware profiling.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 8 Module 8.1 GPU Hardware & CUDA Reference
Publicly accessible university curriculum materials and CUDA programming references on NVIDIA Developer.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 8: Advanced LLM Systems & Research
Use during Module 8.1 (GPU & Training Systems) to understand thread blocks, warps, shared memory SRAM, and memory coalescing.
Community-driven advanced research and engineering lecture series covering GPU hardware architecture, CUDA kernels, Triton, FlashAttention, quantization, and PyTorch compiler internals.
Official hands-on tutorials for OpenAI's Triton language: vector addition, fused softmax, tiled matrix multiplication, LayerNorm, and FlashAttention kernels in Python.
NVIDIA Deep Learning Institute (DLI) self-paced training catalog featuring both free introductory courses and paid hands-on GPU cluster labs on CUDA, accelerated computing, and LLM deployment.