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License: Apache License 2.0

C++ 0.13% Python 10.06% Cuda 2.33% Makefile 0.02% CMake 0.07% Jupyter Notebook 87.38%

cuda-mode-lectures's Introduction

Supplementary Material for Lectures

discord.gg/cudamode The PMPP Book: Programming Massively Parallel Processors: A Hands-on Approach (Amazon link) YouTube Channel

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 15: CUTLASS

Lecture 16: On Hands profiling

Lecture 17: GPU Collective Communication (NCCL)

Lecture 18: Fused Kernels

Lecture 19: Data Processing on GPUs

Lecture 20: Scan Algorithm

Lecture 21: Scan Algorithm Part 2

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

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