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Write Real CUDA Kernels in Python

CUDA with Numba Study Plan

A focused 3-chapter path into GPU programming.

3

chapters

12

topics

2

free chapters

PathChapters3Practice1
01

The path

4 phases, in the order we recommend.

  1. 1

    Prerequisites

    Foundations Study Plan
    Python FoundationsNumPy & ArraysBasic Linear Algebra

    Complete the Foundations study plan first →

  2. 2

    The CUDA Model

    Week 1
    Ch 1: Threads, Blocks & Grids

    Global index, bounds checks, grid-stride loops

  3. 3

    GPU Memory

    Week 2
    Ch 2: Memory & 2D Grids

    to_device, shared memory, __syncthreads()

  4. 4

    Parallel Patterns

    Week 3
    Ch 3: Reductions, Atomics & Tiling

    Tree reductions, atomics, tiled matmul

Tip: Each chapter includes worked examples, math intuition, and a concept map. Practice the kernels in the CUDA Basics with Numba collection.

Pro chapters (2-3) require a premium subscription.

02

Chapters

2 of 3 chapters free. The rest unlock with Pro.

CH 01

Thinking in Threads: The CUDA Model with Numba

Write real CUDA kernels in Python with @cuda.jit. The thread/block/grid launch hierarchy, computing a thread's global index, bounds checks for the ragged tail, and grid-stride loops.

4 topicsStart→
CH 02

Moving Data: GPU Memory with Numba

The GPU's separate memory and how to use it: host↔device transfers with to_device/copy_to_host, device arrays, 2D grids for matrices, and fast per-block shared memory with __syncthreads().

4 topicsStart→
CH 03

Parallel Patterns: Reductions, Atomics & Tiling

PRO

The patterns behind real kernels: combining values without races, shared-memory tree reductions, atomic updates and contention, and tiled matrix multiplication that reuses data from shared memory.

4 topicsUnlock with Pro
03

Practice

Problem sets that drill this plan's topics.

CUDA Basics with Numba

Apply the curriculum hands-on: 25 GPU-kernel problems that run on a real NVIDIA GPU, from vector addition and the global index to shared-memory reductions, atomics, and tiled matrix multiplication.

CUDA Execution ModelMemory & Data MovementCore Compute Kernels+2
5 topicsView problems→

A short, focused path from the GPU execution model to shared-memory reductions and tiled matrix multiplication — all in Python with Numba.