CUDA Core Compute Libraries
C-Rank Grade: S (Elite) - 68/100
External PR Merge Rate: 72%
Response Time: -
First Timer Success: 64%
NVIDIA/cccl has a recorded first-timer success rate of 63.6%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in NVIDIA/cccl respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 68.2 places NVIDIA/cccl in the S tier.
The external contributor pull request merge rate for NVIDIA/cccl is 71.8%, based on public PR activity from non-core contributors.
NVIDIA/cccl currently has 8 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Collaborative maintainer environment in NVIDIA/cccl. Reviews community pull requests with focus on project quality.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/NVIDIA/cccl?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 16:00 UTC (16:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 16:00 to 20:00 UTC (16:00 - 20:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (90% approval). Large PRs (>500 lines) take ~115 hours (32% approval). Keep your diff compact for rapid review.
Create a lldb and gdb pretty printer for cuda::mr::shared_resource. The printer should live under libcudacxx/share/libcudacxx/<debugger>/shared_resource.py for each debugger. In addition, the pretty printers should be exhaustively tested, with tests living under libcudacxx/test/debugging/shared_resource. See the existing tests in that directory for inspiration.
Create a lldb and gdb pretty printer for cuda::annotated_ptr. The printer should live under libcudacxx/share/libcudacxx/<debugger>/annotated_ptr.py for each debugger. In addition, the pretty printers should be exhaustively tested, with tests living under libcudacxx/test/debugging/annotated_ptr. See the existing tests in that directory for inspiration.
Create a lldb and gdb pretty printer for cuda::std::expected. The printer should live under libcudacxx/share/libcudacxx/<debugger>/expected.py for each debugger. In addition, the pretty printers should be exhaustively tested, with tests living under libcudacxx/test/debugging/expected. See the existing tests in that directory for inspiration.
Create a lldb and gdb pretty printer for cuda::std::optional. The printer should live under libcudacxx/share/libcudacxx/<debugger>/optional.py for each debugger. In addition, the pretty printers should be exhaustively tested, with tests living under libcudacxx/test/debugging/optional. See the existing tests in that directory for inspiration.
We decided a while back on Slack that in order to solve the problem of querying the temporary storage size of CUB APIs that take an environment and have no equivalent non-env overload, we should just replace all cudaStream_t parameters in public device-scope APIs by a const Env& parameter and add the additional environment handling (excluding temporary storage allocation). This would result in every CUB API to have two flavors: struct DeviceAlg { // two-phase overload, user must handle temp storage allocation template <..., typename Env> static cudaError_t Algorithm(void* ts_ptr, size_t& ts_size, ..., const Env& env); // single-phase overload, handles temp storage allocation itself template <..., typename Env> static cudaError_t Algorithm(..., const Env& env); } This change is transparent to users, since a passed cudaStream_t (or a type convertible to that) will be handled appropriately by the stream query of the environment. This work has already begun, but was mostly dri
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When evaluating whether to contribute to NVIDIA/cccl, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of NVIDIA/cccl acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 71.8% were successfully merged into the primary branch.
NVIDIA/cccl has a recorded first-timer success rate of 63.6%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 68.2 places NVIDIA/cccl in the Elite tier.
The external pull request merge rate is 71.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, NVIDIA/cccl currently has 8 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for NVIDIA/cccl are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.