RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/cccl

Default branch main · commit e49bdfac · scanned 5/13/2026, 5:41:32 PM

GitHub: 2,328 stars · 387 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface NVIDIA/cccl, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to emphasize its unified nature

    Why:

    CURRENT
    Welcome to the CUDA Core Compute Libraries (CCCL) where our mission is to make CUDA more delightful. This repository unifies three essential CUDA C++ libraries into a single, convenient repository:...
    COPY-PASTE FIX
    The CUDA Core Compute Libraries (CCCL) unify Thrust, CUB, and libcudacxx into a single, modern C++ foundation for high-performance GPU programming on NVIDIA hardware. Our mission is to make CUDA more delightful by providing essential building blocks for safe and efficient code.
  • mediumlicense#2
    Add a clear license statement to the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under [Specify License(s) here, e.g., 'the Apache 2.0 License and the MIT License for specific components']. See the [LICENSE](LICENSE) file for full details.
  • lowcomparison#3
    Add a 'Comparison to Alternatives' or 'FAQ' section

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

CCCL is specifically designed for high-performance C++ development on NVIDIA GPUs using CUDA. While other frameworks like Kokkos, SYCL, and DPC++ offer multi-platform GPU programming, CCCL provides a deeply optimized, unified C++ standard library-like experience tailored for the NVIDIA ecosystem, integrating Thrust, CUB, and libcudacxx.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface NVIDIA/cccl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Thrust
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Thrust · recommended 2×
  2. Kokkos · recommended 2×
  3. SYCL · recommended 2×
  4. CUDA C++ · recommended 2×
  5. DPC++ · recommended 1×
  • CATEGORY QUERY
    What C++ libraries offer high-level abstractions for efficient CUDA GPU programming?
    you: not recommended
    AI recommended (in order):
    1. Thrust
    2. Kokkos
    3. SYCL
    4. DPC++
    5. hipSYCL
    6. CUDA C++
    7. ArrayFire
    8. Raaja

    AI recommended 8 alternatives but never named NVIDIA/cccl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking C++ compute libraries for implementing parallel algorithms on GPU architectures.
    you: not recommended
    AI recommended (in order):
    1. CUDA C++
    2. HIP
    3. OpenCL
    4. SYCL
    5. Kokkos
    6. Thrust

    AI recommended 6 alternatives but never named NVIDIA/cccl. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of NVIDIA/cccl?
    pass
    AI named NVIDIA/cccl explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts NVIDIA/cccl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/cccl explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo NVIDIA/cccl solve, and who is the primary audience?
    pass
    AI named NVIDIA/cccl explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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NVIDIA/cccl — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite