# srush/GPU-Puzzles

Solve puzzles. Learn CUDA.

Repository: https://github.com/srush/GPU-Puzzles
Canonical: https://ross.abutalabs.com/products/gpu-puzzles
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: cuda, machine-learning, puzzles
Last push: 2024-09-01T16:14:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1514, "days_push": 731, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12428, forks 950 (observed 2026-08-28T04:10:52.680123+00:00)

## What it is
An interactive Jupyter notebook of programming puzzles that teaches beginner GPU/CUDA kernel programming using Numba's Python-to-CUDA mapping. Learners implement kernels directly in the notebook, designed to be run in Google Colab with a GPU runtime.

## Use cases
- learn CUDA programming interactively
- practice writing GPU kernels in Python
- understand how deep learning GPU algorithms work
- teach parallel programming with puzzles
- get hands-on with Numba CUDA without writing C++

## When to choose
- you know Python and want to learn GPU programming from scratch
- you want a hands-on, puzzle-driven introduction to CUDA kernels
- you want to build intuition for how GPU parallelism powers deep learning

## When to avoid
- you need production CUDA code or performance tuning guidance
- you want a comprehensive CUDA reference or textbook
- you have no access to a GPU or Colab runtime

## Facets
- artifact type: learning-resource
- maturity: stable
- function: developer-tools, gpu-computing, machine-learning
- domain: gpu-computing, education, machine-learning, tutorials
- platform: python, browser
- tags: cuda, jupyter-notebook, numba, puzzles, interactive-learning, kernels, gpu

## Member repositories
- srush/GPU-Puzzles (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:52.680123+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:14:04.901115+00:00, confidence not recorded.
  - readme: https://github.com/srush/GPU-Puzzles (fetched 2026-08-28T04:10:52.680123+00:00, sha 6ef4b6cbe167)
- Data as of 2026-08-30T08:39:29.467469+00:00.