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Showing content from https://github.com/cuda-mode/lectures below:

gpu-mode/lectures: Material for gpu-mode lectures

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 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 Bonus Lecture: CUDA C++ llm.cpp 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 Lecture 23: Tensor Cores Lecture 24: Scan at the Speed of Light Lecture 25: Speaking Composable Kernel Lecture 26: SYCL MODE (Intel GPU) Lecture 27: gpu.cpp Lecture 28: Liger Kernel Lecture 29: Triton Internals Lecture 30: Quantized training Lecture 31: Beginners Guide to Metal Kernels Lecture 32: Unsloth - LLM Systems Engineering Lecture 33: BitBLAS Lecture 34: Low Bit Triton Kernels Lecture 35: SGLang Performance Optimization Lecture 36: CUTLASS and Flash ATtention 3 Lecture 37: Introduction to SASS & GPU Microarchitecture Lecture 38: Lowbit kernels for ARM CPU Lecture 39: TorchTitan Lecture 40: Flash Infer Lecture 41: CUDA Docs for Humans Lecture 42: Mosaic GPU Lecture 43: Lecture 57:

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