# NVIDIA/accelerated-computing-hub

NVIDIA curated collection of educational resources related to general purpose GPU programming.

Repository: https://github.com/NVIDIA/accelerated-computing-hub
Canonical: https://ross.abutalabs.com/products/accelerated-computing-hub
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2026-08-26T00:06:58+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 57
- inputs: {"age_days": 810, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1935, forks 327 (observed 2026-08-28T04:05:56.418642+00:00)

## What it is
NVIDIA's curated hub of open educational materials for GPU computing, including interactive Jupyter notebook tutorials on CUDA C++, CUDA Python, stdpar, Warp, and nvmath-python. Tutorials are deployable via Docker Compose, NVIDIA Brev, or Google Colab.

## Use cases
- learn CUDA C++ programming from scratch
- learn CUDA Python with CuPy and cuDF
- accelerate NumPy code on GPUs
- learn GPU parallel programming with standard parallelism
- run hands-on GPU tutorials on Colab or Brev
- find best practices for GPU optimization

## When to choose
- you want official NVIDIA-authored GPU programming tutorials
- you prefer interactive Jupyter notebooks runnable in the cloud
- you are teaching or self-studying CUDA or accelerated Python

## When to avoid
- you need production GPU software rather than learning materials
- you have no access to NVIDIA GPUs or cloud GPU instances
- you need a supported product with SLAs rather than open educational content

## Facets
- artifact type: learning-resource
- maturity: active
- function: gpu-computing, developer-tools
- domain: gpu-computing, tutorials, developer-tools
- platform: python, cross-platform
- tags: cuda, nvidia, jupyter-notebooks, tutorials, gpu-programming, education, gpu

## Member repositories
- NVIDIA/accelerated-computing-hub (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:56.418642+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 2026-08-30T03:08:27.014351+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA/accelerated-computing-hub (fetched 2026-08-28T04:05:56.418642+00:00, sha e98f9a652f9e)
- Data as of 2026-08-30T08:39:29.467469+00:00.