Learn CUDA programming for NVIDIA Hopper GPUs.

We just posted a course on the freeCodeCamp.org YouTube channel that will teach you to build efficient WGMMA pipelines and leverage Cutlass optimizations to perform the massive matrix multiplications that power modern AI.

Beyond single-chip performance, the curriculum covers multi-GPU scaling and NCCL primitives necessary for training trillion-parameter models. To get the most out of these lessons, you should have a foundational grasp of C++ syntax and linear algebra, particularly how matrices are tiled and multiplied.

Here are all the sections in this massive course:

• Course Introduction

• Table of Contents & Course Overview

• LESSON 1 — H100 Hopper GPU Architecture

• H100 Specifications: HBM3, Bandwidth & Power

• Tensor Cores Overview

• Tensor Memory Accelerator (TMA)

• Transformer Engine

• L2 Cache Architecture

• GPCs, TPCs & SM Layout

• Thread Block Clusters

• Distributed Shared Memory

• SM Sub-Partitions (SMSPs)

• Warp Schedulers & Dispatch Units

• Shared Memory & Data Movement

• Occupancy

• LESSON 2 — Clusters, Data Types, Inline PTX & Pointers

• Thread Block Clusters Programming

• Configuring Cluster Dimensions

• Inline PTX Assembly

• State Spaces

• Data Types in PTX

• Generic Pointers

• Address Space Conversion

• LESSON 3 — Asynchronicity & Barriers

• Introduction to Async Operations

• Proxies

• Fences & Memory Ordering

• Fence Ordering & Visibility

• Fence Scopes

• Acquire & Release Fences

• Expected Count & Thread Arrival

• M-Barrier Arrive Operations

• M-Barrier PTX Instructions

• Barrier Wait Operations

• Phase & Parity

• Commit Operations

• LESSON 4 — CuTensorMap Descriptors

• Tensor Shape, Stride & Data Type

• Element Stride & Dimensions

• Box Dimensions (Tile Size)

• Bank Conflicts

• Swizzling

• Swizzle Formula Deep Dive

• Interleave Layouts

• Out-of-Bounds Fill (OOB)

• LESSON 5 — cp.async.bulk (Async Bulk Copies via TMA)

• Bulk Tensor Operations (1D–5D)

• Multicast Operations

• Prefetch

• LESSON 6 — WGMMA Part 1 (Warp Group Matrix Multiply Accumulate)

• Warp Groups & Matrix Multiplication

• WGMMA Descriptors

• Accumulators & Register Reuse

• Scale Factors (Scale D, Scale A, Scale B)

• Core Matrices & 16×16 Tiles

• LESSON 7 — WGMMA Part 2

• Commit Groups & Wait Groups

• WGMMA with FP8 Data Types

• LESSON 8 — Kernel Design

• Compute-Bound vs. Memory-Bound Kernels

• Warp Specialization

• Cooperative vs. Ping-Pong Pipelines

• Pipelining Fundamentals

• Circular Buffering

• Ping-Pong Pipeline Deep Dive

• Epilogue Handling in Pipelines

• Persistent Scheduling

• Split-K & Stream-K Strategies

• Data-Parallel Tile Scheduling

• Epilogue Fusion (Bias, Activation, Scaling)

• Epilogue Operations Overview

• CUTLASS SOURCE CODE WALKTHROUGH

• Main Loop & Scheduling Policies

• Dispatch Policy

• SM90 Tile Scheduler

• SM90 Epilogue (TMA Warp Specialized)

• SM90 Builder

• Collective Builder

• FAST.CU KERNEL WALKTHROUGH

• Main Loop Implementation

• Producer Warp Group (Dependence Wall)

• Consumer Warp Group

• Prologue

• MULTI-GPU PROGRAMMING — Part 1

• NVSwitch

• Topology & System Architecture

• NVSwitch, BlueField DPUs & Storage Fabrics

• CUDA Peer-to-Peer Communication

• MPI (Message Passing Interface)

• P2P Limitations & Trade-offs

• MULTI-GPU PROGRAMMING — Part 2

• SLURM Resource Allocation

• PMIx Process Management

• NCCL (NVIDIA Collective Communications Library)

• NCCL Internals & Ring Algorithm

• AllReduce Operations

• NCCL Collectives: Broadcast, AllGather, ReduceScatter

• Parallelism Strategies: Data, Tensor, Pipeline & Expert Parallelism

• Course Conclusion & Next Steps

Watch the course on the freeCodeCamp.org YouTube channel (24-hour watch).