CUDA MODE

Lecture 1 How to profile CUDA kernels in PyTorch

Lecture 5: Going Further with CUDA for Python Programmers

Lecture 7 Advanced Quantization

Lecture 26: SYCL Mode (Intel GPU)

Lecture 22: Hacker's Guide to Speculative Decoding in VLLM

Lecture 16: On Hands Profiling

Lecture 27: gpu.cpp - Portable GPU compute using WebGPU

Lecture 20: Scan Algorithm

Lecture 21: Scan Algorithm Part 2

Lecture 24: Scan at the Speed of Light

Lecture 14: Practitioners Guide to Triton

Lecture 17: NCCL

Lecture 25: Speaking Composable Kernel (CK)

Lecture 12: Flash Attention

Lecture 6 Optimizing Optimizers

Lecture 18: Fusing Kernels

Lecture 11: Sparsity

Lecture 9 Reductions

Lecture 28: Liger Kernel - Efficient Triton Kernels for LLM Training

Mojo: Modular’s unified device accelerator language

Lecture 71: [ScaleML Series] FlexOlmo: Open Language Models for Flexible Data Use

Lecture 23: Tensor Cores

Lecture 19: Data Processing on GPUs

Lecture 3: Getting Started With CUDA for Python Programmers

Lecture 2 Ch1-3 PMPP book

Lecture 1 How to profile CUDA kernels in PyTorch

Factorio Learning Environment

cuDNN