# olcf/cuda-training-series

Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)

Repository: https://github.com/olcf/cuda-training-series
Canonical: https://ross.abutalabs.com/products/cuda-training-series
Language: Cuda
License Family: other
Last push: 2024-08-19T05:03:11+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": 2431, "days_push": 744, "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 1042, forks 369 (observed 2026-08-28T04:03:20.773654+00:00)

## What it is
A repository of training materials accompanying NVIDIA's CUDA Training Series presented at ORNL and NERSC. It contains example code and exercises for learning CUDA parallel programming, with slides and recordings hosted externally.

## Use cases
- learn cuda programming from scratch
- gpu training materials for hpc
- cuda examples for parallel programming
- prepare for nvidia gpu development
- teach a cuda workshop course

## When to choose
- you want structured, tutorial-style CUDA learning with companion slides and recordings
- you are an HPC user at ORNL/NERSC or similar learning GPU offload
- you prefer hands-on example code over documentation

## When to avoid
- you need a production CUDA library or framework
- you want a maintained tool with support or a license
- you already know CUDA and need advanced optimization references

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: gpu-computing
- domain: gpu-computing, tutorials, developer-tools
- platform: cpp
- tags: cuda, nvidia, training-materials, hpc, parallel-computing, slides, examples, gpu

## Member repositories
- olcf/cuda-training-series (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.773654+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 2026-08-30T07:02:47.682683+00:00, confidence not recorded.
  - readme: https://github.com/olcf/cuda-training-series (fetched 2026-08-28T04:03:20.773654+00:00, sha f3cfb415f59e)
- Data as of 2026-08-30T08:39:29.467469+00:00.