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<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Practice Notebooks - GPU Learning</title>
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</style>
</head>
<body>
<a href="#main-content" class="skip-link">Skip to main content</a>
<main class="notebooks-container" id="main-content">
<header class="notebooks-header">
<div class="notebooks-header__label">Hands-On Practice</div>
<h1 class="notebooks-header__title">Practice Notebooks</h1>
<p class="notebooks-header__desc">
33 Jupyter notebooks covering the complete GPU programming journey.
Run them in Google Colab with free GPU access.
</p>
<a href="index.html" class="notebooks-header__back">
<span>←</span> Back to Course
</a>
</header>
<!-- Part 0: Parallel Mindset -->
<section class="notebooks-part" style="margin-bottom: var(--space-3xl);">
<div class="notebooks-part__header">
<span class="notebooks-part__number" style="background: var(--accent-purple);">Part 0</span>
<h2 class="notebooks-part__title">The Parallel Mindset</h2>
<span class="notebooks-part__chapter">Chapter 0</span>
</div>
<div class="notebooks-list">
<div class="notebook-item">
<span class="notebook-item__number">01</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Sequential vs Parallel</div>
<p class="notebook-item__desc">Race conditions, data dependencies, parallel hazards</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part0/01_sequential_vs_parallel.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">02</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Amdahl's Law</div>
<p class="notebook-item__desc">Speedup limits, sequential bottlenecks, efficiency</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part0/02_amdahls_law.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">03</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Parallel Patterns</div>
<p class="notebook-item__desc">Embarrassingly parallel, reduction, stencil, irregular</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part0/03_parallel_patterns.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">04</span>
<div class="notebook-item__info">
<div class="notebook-item__title">NumPy Vectorization</div>
<p class="notebook-item__desc">Loops vs vectors, broadcasting, GPU thinking</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part0/04_numpy_vectorization.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
</div>
</section>
<!-- Part 1: Foundations -->
<section class="notebooks-part notebooks-part--1">
<div class="notebooks-part__header">
<span class="notebooks-part__number">Part 1</span>
<h2 class="notebooks-part__title">Foundations: NumPy to Triton</h2>
<span class="notebooks-part__chapter">Chapters 0-3</span>
</div>
<div class="notebooks-list">
<div class="notebook-item">
<span class="notebook-item__number">00</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Environment Check</div>
<p class="notebook-item__desc">Verify GPU setup and dependencies</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/00_ready_check.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">01</span>
<div class="notebook-item__info">
<div class="notebook-item__title">NumPy Baseline</div>
<p class="notebook-item__desc">Establish CPU performance benchmarks</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/01_numpy_baseline.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">02</span>
<div class="notebook-item__info">
<div class="notebook-item__title">CuPy Introduction</div>
<p class="notebook-item__desc">Instant GPU speedup with drop-in NumPy replacement</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/02_cupy_intro.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">03</span>
<div class="notebook-item__info">
<div class="notebook-item__title">GPU Architecture</div>
<p class="notebook-item__desc">Understand SMs, warps, and the execution model</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/03_gpu_architecture.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">04</span>
<div class="notebook-item__info">
<div class="notebook-item__title">First Triton Kernel</div>
<p class="notebook-item__desc">Write your first GPU kernel with Triton</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/04_first_triton_kernel.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">05</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Memory Hierarchy</div>
<p class="notebook-item__desc">Profile and understand memory access patterns</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/05_memory_hierarchy.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">06</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Tiling Basics</div>
<p class="notebook-item__desc">Implement tiled memory access for better cache utilization</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/06_tiling_basics.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">07</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Fast Matrix Multiplication</div>
<p class="notebook-item__desc">Achieve 500+ GFLOPS with optimized matmul</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part1/07_fast_matmul.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</a>
</div>
</div>
</section>
<!-- Part 2: Optimization -->
<section class="notebooks-part notebooks-part--2">
<div class="notebooks-part__header">
<span class="notebooks-part__number">Part 2</span>
<h2 class="notebooks-part__title">Optimization Deep Dive</h2>
<span class="notebooks-part__chapter">Chapters 4-6</span>
</div>
<div class="notebooks-list">
<div class="notebook-item">
<span class="notebook-item__number">01</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Profiling with Nsight</div>
<p class="notebook-item__desc">Learn to use Nsight Compute for kernel analysis</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/01_profiling.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">02</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Memory Coalescing</div>
<p class="notebook-item__desc">Optimize global memory access patterns</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/02_coalescing.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">03</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Bank Conflicts</div>
<p class="notebook-item__desc">Eliminate shared memory bottlenecks</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/03_bank_conflicts.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">04</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Software Pipelining</div>
<p class="notebook-item__desc">Overlap compute and memory operations</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/04_pipelining.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">05</span>
<div class="notebook-item__info">
<div class="notebook-item__title">TMA (Hopper+)</div>
<p class="notebook-item__desc">Hardware-accelerated async data movement</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/05_tma.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">06</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Tensor Cores</div>
<p class="notebook-item__desc">Use MMA operations for matrix math</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/06_tensor_cores.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">07</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Optimized GEMM</div>
<p class="notebook-item__desc">Put it all together for peak performance</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part2/07_optimized_gemm.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</a>
</div>
</div>
</section>
<!-- Part 3: Attention -->
<section class="notebooks-part notebooks-part--3">
<div class="notebooks-part__header">
<span class="notebooks-part__number">Part 3</span>
<h2 class="notebooks-part__title">Attention Mechanisms</h2>
<span class="notebooks-part__chapter">Chapter 8</span>
</div>
<div class="notebooks-list">
<div class="notebook-item">
<span class="notebook-item__number">01</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Dot Product Attention</div>
<p class="notebook-item__desc">Implement basic Q K^T computation</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/01_dot_product.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">02</span>
<div class="notebook-item__info">
<div class="notebook-item__title">The Softmax Problem</div>
<p class="notebook-item__desc">Why naive softmax fails at scale</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/02_softmax_problem.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">03</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Stable Softmax</div>
<p class="notebook-item__desc">Numerical stability with max subtraction</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/03_stable_softmax.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">04</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Full Attention</div>
<p class="notebook-item__desc">Complete attention implementation</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/04_full_attention.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">05</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Online Softmax</div>
<p class="notebook-item__desc">Single-pass softmax algorithm</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/05_online_softmax.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">06</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Tiled Attention</div>
<p class="notebook-item__desc">Block-wise computation for memory efficiency</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/06_tiled_attention.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</div>
<div class="notebook-item">
<span class="notebook-item__number">07</span>
<div class="notebook-item__info">
<div class="notebook-item__title">FlashAttention</div>
<p class="notebook-item__desc">Production-grade fused attention kernel</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part3/07_flash_attention.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</a>
</div>
</div>
</section>
<!-- Part 4: Production -->
<section class="notebooks-part notebooks-part--4">
<div class="notebooks-part__header">
<span class="notebooks-part__number">Part 4</span>
<h2 class="notebooks-part__title">Production & Quantization</h2>
<span class="notebooks-part__chapter">Chapters 9 & 12</span>
</div>
<div class="notebooks-list">
<div class="notebook-item">
<span class="notebook-item__number">01</span>
<div class="notebook-item__info">
<div class="notebook-item__title">FP8 Conversion</div>
<p class="notebook-item__desc">Convert between floating point formats</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/01_fp8_conversion.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">02</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Quantization Fundamentals</div>
<p class="notebook-item__desc">Symmetric and asymmetric quantization</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/02_quantization_fundamentals.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">03</span>
<div class="notebook-item__info">
<div class="notebook-item__title">INT8 and INT4</div>
<p class="notebook-item__desc">Integer quantization for inference</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/03_int8_int4.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
Open in Colab
</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">04</span>
<div class="notebook-item__info">
<div class="notebook-item__title">NVFP4</div>
<p class="notebook-item__desc">NVIDIA's 4-bit floating point format</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/04_nvfp4.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">05</span>
<div class="notebook-item__info">
<div class="notebook-item__title">KV Cache Strategy</div>
<p class="notebook-item__desc">Efficient key-value cache management</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/05_kv_cache_strategy.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">06</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Fused Quantized Attention</div>
<p class="notebook-item__desc">Combine quantization with FlashAttention</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/06_fused_quantized_attention.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
<svg viewBox="0 0 24 24" fill="currentColor"><path d="M16.9414 4.9757a7.033 7.033 0 0 0-4.9308 2.0646 7.033 7.033 0 0 0-.1232 9.8068l2.395-2.395a3.6455 3.6455 0 0 1 5.1497-5.1478l2.397-2.3989a7.033 7.033 0 0 0-4.8877-1.9297zM7.07 4.9855a7.033 7.033 0 0 0-4.8878 1.9316l2.3911 2.3911a3.6434 3.6434 0 0 1 5.0227.1271l1.7341-2.9737a7.033 7.033 0 0 0-4.2601-1.4761zm10.4413 6.4862l-1.7261 2.9617a3.6455 3.6455 0 0 1-5.0227-.1231l-2.395 2.3949a7.033 7.033 0 0 0 9.1438.1114 7.033 7.033 0 0 0-.0001-5.345z"/></svg>
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</a>
</div>
<div class="notebook-item">
<span class="notebook-item__number">07</span>
<div class="notebook-item__info">
<div class="notebook-item__title">Production Integration</div>
<p class="notebook-item__desc">Deploy optimized kernels in serving systems</p>
</div>
<a href="https://colab.research.google.com/github/mulf0/gpu-learning/blob/master/notebooks/part4/07_production_integration.ipynb"
target="_blank" rel="noopener" class="notebook-item__colab">
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</div>
</div>
</section>
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