Supplementary Material for Lectures

YouTube Channel

The PMPP Book: Programming Massively Parallel Processors: A Hands-on Approach (Amazon link)

Lecture 1: Profiling and Integrating CUDA kernels in PyTorch

• Speaker: Mark Saroufim
• Notebook and slides in lecture_001 folder

Lecture 2: Recap Ch. 1-3 from the PMPP book

• Speaker: Andreas Koepf
• Slides: The powerpoint file lecture_002/cuda_mode_lecture2.pptx can be found in the root directory of this repository. Alternatively here as Google docs presentation.

Lecture 3: Getting Started With CUDA

• Speaker: Jeremy Howard
• Notebook: See the lecture_003 folder, or run the Colab version

Lecture 4: Intro to Compute and Memory Architecture

• Speaker: Thomas Viehmann
• Notebook and slides in the lecture_004 folder.

Lecture 5: Going Further with CUDA for Python Programmers

• Speaker: Jeremy Howard
• Notebook in the lecture_005 folder.

Lecture 6: Optimizing PyTorch Optimizers

• Speaker: Jane Xu
• Slides

Lecture 7: Advanced Quantization

• Speaker: Charles Hernandez
• Slides

Lecture 8: CUDA Performance Checklist

• Speaker: Mark Saroufim
• Code in the lecture_008 folder
• Slides

Lecture 9: Reductions

• Speaker: Mark Saroufim
• Code in the lecture_009 folder
• Slides

Lecture 10: Build a Prod Ready CUDA Library

• Speaker: Oscar Amoros Huguet
• slides

Lecture 11: Sparsity

• Speaker: Jesse Cai
• Slides

Lecture 12: Flash Attention

• Speaker: Thomas Viehmann

Lecture 13: Ring Attention

• Speaker: Andreas Koepf
• Slides

Lecture 14: Practitioner's Guide to Triton

• Date: 2024-04-13, Speaker: Umer Adil
• Notebook

Lecture 15: CUTLASS

• Speaker: Eric Auld

Lecture 16: On Hands profiling

• Speaker: Taylor Robbie

Bonus Lecture: CUDA C++ llm.cpp

• Speaker: Jake Hemstad & Georgii Evtushenko
• Slides

Lecture 17: GPU Collective Communication (NCCL)

• Speaker: Dan Johnson
• Code in the lecture_017 folder

Lecture 18: Fused Kernels

• Speaker: Kapil Sharma
• Code in the lecture_018 folder

Lecture 19: Data Processing on GPUs

• Speaker: Devavret Makkar

Lecture 20: Scan Algorithm

• Speaker: Izzat El Haj
• Slides

Lecture 21: Scan Algorithm Part 2

• Speaker: Izzat El Haj
• Slides

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

• Speaker: Cade Daniel
• Slides

Lecture 23: Tensor Cores

• Speaker: Vijay Thakkar & Pradeep Ramani
• Slides

Lecture 24: Scan at the Speed of Light

• Speaker: Jake Hemstad & Georgii Evtushenko

Lecture 25: Speaking Composable Kernel

• Speaker: Haocong Wang
• Slides

Lecture 26: SYCL MODE (Intel GPU)

• Speaker: Patric Zhao
• Slides

Lecture 27: gpu.cpp

• Speaker: Austin Huang
• Slides

Lecture 28: Liger Kernel

• Speaker: Byron Hsu
• Slides
• Hands-on Notebooks
  1. RMSNorm: Verifying Correctness and Performance
  2. FusedLinearCrossEntropy: Verifying Memory Reduction
  3. Convergence Comparison: Triton Kernel Patched vs. Original Model Layer-by-Layer
  4. Contiguity is the hidden killer
  5. Address int32 overflow

Lecture 29: Triton Internals

• Speaker: Kapil Sharma
• Code/presentation in the lecture_029 folder

Lecture 30: Quantized training

• Speaker: Thien Tran
• Code/presentation in the lecture_030 folder

Lecture 31: Beginners Guide to Metal Kernels

• Speaker: Nikita Shulga
• Code/presentation in the lecture_031 folder

Lecture 32: Unsloth - LLM Systems Engineering

• Speaker: Daniel Han
• Slides

Lecture 33: BitBLAS

• Speaker: Wang Lei
• Code/presentation in the lecture_033 folder

Lecture 34: Low Bit Triton Kernels

• Speaker: Hicham Badri
• Slides

Lecture 35: SGLang Performance Optimization

• Speaker: Yineng Zhang
• Slides

Lecture 36: CUTLASS and Flash ATtention 3

• Speaker: Jay Shah
• Slides

Lecture 37: Introduction to SASS & GPU Microarchitecture

• Speaker: Arun Demeure
• Slides

Lecture 38: Lowbit kernels for ARM CPU

• Speaker: Scott Roy
• Slides