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Supplementary Material for Lectures

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

Lecture 1: Profiling and Integrating CUDA kernels in PyTorch

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

Lecture 3: Getting Started With CUDA

Lecture 4: Intro to Compute and Memory Architecture

Lecture 5: Going Further with CUDA for Python Programmers

Lecture 6: Optimizing PyTorch Optimizers

Lecture 7: Advanced Quantization

Lecture 8: CUDA Performance Checklist

Lecture 9: Reductions

Lecture 10: Build a Prod Ready CUDA Library

Lecture 11: Sparsity

Lecture 12: Flash Attention

Lecture 13: Ring Attention

Lecture 14: Practitioner's Guide to Triton

Lecture 17: GPU Collective Communication (NCCL)

Lecture 18: Fused Kernels