CUDA, GPU kernel, and AI infrastructure optimization notes.
This repository collects hands-on CUDA kernels, CUTLASS/CuTe notes, Triton examples, PTX ISA notes, PyTorch internals notes, and LLM inference/training optimization material. It is one of my main public study and engineering notebooks for GPU systems work.
cuda-kernels/: handwritten CUDA kernels for reduce, softmax, elementwise, GEMV, indexing, atomic add, upsampling, and linear attention.cuda-mode/: notes and code from the CUDA-MODE lecture series.cutlass/: CUTLASS and CuTe DSL notes, including GEMM, TMA, WGMMA, swizzling, and instruction-level material.triton/: Triton kernels, PyTorch interop examples, and meetup notes.large-language-model/: LLM serving, training, and systems optimization notes.pytorch/: PyTorch internals and CUDA-related notes.papers/: GPU architecture and ML systems paper notes.ptx-isa/: PTX ISA study notes.tools/: small helper scripts.deprecated/: older material kept for reference.
- CUDA and GPU optimization: this repository.
- Deep learning compiler notes: https://github.com/BBuf/tvm_mlir_learn
- Deep learning framework notes: https://github.com/BBuf/how-to-learn-deep-learning-framework
Actively curated around CUDA kernels, LLM inference optimization, and AI infrastructure. Older Chinese-language notes are being consolidated or replaced with English entry points.
