Nixpkgs Master: Slower Eval Performance With CUDA?

Is your NixOS system feeling sluggish after upgrading to the nixpkgs master branch? You're not alone! Many users have reported a significant slowdown in evaluation performance, particularly when dealing with CUDA configurations. Let's dive into the details of this issue, explore the potential causes, and discuss what it means for your NixOS workflow.

The Performance Dip: A Real-World Example

The original poster (OP) highlighted a concrete example: a system that previously took 10 seconds to evaluate on nixpkgs-25.05 now requires a whopping 23 seconds on the nixpkgs master branch. That's more than double the time! This slowdown can be a major pain point, especially for developers and users who frequently rebuild their systems or switch between different configurations. Imagine waiting an extra 13 seconds every time you make a small change to your configuration.nix file – it adds up quickly!

The Culprit: Missing GCC and CUDA Versions

So, what's causing this performance hit? The prime suspect is the absence of specific GCC and CUDA versions in the nixpkgs master branch. The OP believes that because nixpkgs master no longer contains the relevant GCC versions and CUDA 11, the tryEval for those versions takes significantly longer compared to nixpkgs-25.05. In essence, when Nix tries to evaluate configurations that depend on these older versions, it spends a considerable amount of time searching for them, ultimately leading to the slowdown.

The tryEval function is a key player here. It's used by Nix to attempt evaluation of expressions, and if an expression fails (e.g., due to missing dependencies), it catches the error and moves on. However, this process of trying and failing can be time-consuming, especially when it involves searching through a vast dependency graph.

Visualizing the Problem: Flamegraphs

To illustrate the performance bottleneck, the OP provided flamegraphs. Flamegraphs are powerful visualization tools that help identify performance hotspots in code. By comparing the flamegraphs from nixpkgs-25.05 and nixpkgs master, you can clearly see that tryEval consumes a significantly larger portion of the evaluation time on the master branch. Focusing on the “tryEval” segments, one can visually confirm the increased time spent on this function.

Analyzing the Flamegraphs

Looking at the flamegraphs, you'll notice wider blocks corresponding to tryEval in the "after.svg" (nixpkgs master) compared to "before.svg" (nixpkgs-25.05). The width of a block in a flamegraph represents the amount of time spent in that function. A wider block indicates that the function is taking longer to execute.

This visual representation reinforces the idea that the missing GCC and CUDA versions are causing Nix to spend more time attempting to evaluate configurations that rely on them. The flamegraphs provide concrete evidence of the performance regression and help pinpoint the source of the problem.

Impact on Development Workflows

The slowdown in evaluation performance can have a significant impact on development workflows, particularly for projects that rely on older CUDA versions or specific GCC compilers. Imagine you're working on a machine learning project that requires CUDA 11. If you're using the nixpkgs master branch, every time you rebuild your environment or switch between branches, you'll experience the performance hit associated with tryEval. This can disrupt your workflow and make development a frustrating experience.

Moreover, the increased evaluation time can also affect continuous integration (CI) systems. If your CI pipelines rely on Nix to build and test your projects, the slowdown can increase build times and consume more resources. This can be costly, especially for projects with frequent commits and complex dependencies.

Potential Solutions and Workarounds

While the performance slowdown is a concern, there are potential solutions and workarounds that you can explore:

• Pinning Nixpkgs: If you need to work with older CUDA or GCC versions, consider pinning your NixOS configuration to a specific nixpkgs revision that includes those versions. This will ensure that Nix doesn't waste time searching for them in the master branch. You can pin nixpkgs in your configuration.nix file using the nixpkgs.url option.
• Using nixpkgs-channels: The nixpkgs-channels input to the nix command-line tool can be used to specify the nixpkgs version used for evaluation. This allows you to evaluate expressions against older nixpkgs versions without modifying your system configuration.
• Contributing to Nixpkgs: If you have the expertise, consider contributing to nixpkgs by helping to maintain and optimize the handling of older CUDA and GCC versions. This could involve improving the tryEval function or providing better support for legacy dependencies.
• Exploring Alternatives: Investigate whether there are alternative ways to achieve your goals without relying on the specific CUDA or GCC versions that are causing the slowdown. For example, you might be able to upgrade your project to use a newer CUDA version that is supported by the nixpkgs master branch.

Community Discussion and Collaboration

The original post sparked a discussion within the NixOS community, with users sharing their experiences and suggesting potential solutions. This highlights the importance of community collaboration in addressing performance issues and improving the overall NixOS experience. By sharing your experiences and contributing to the discussion, you can help others overcome similar challenges and contribute to the ongoing development of Nixpkgs.

The NixOS community is known for its helpfulness and willingness to assist users. If you're experiencing performance issues, don't hesitate to reach out to the community for support. You can find help on the NixOS Discourse forum, the NixOS subreddit, or the NixOS Matrix channel.

Looking Ahead: Future Improvements

The NixOS community is actively working on improving the performance of Nix and Nixpkgs. There are ongoing efforts to optimize the evaluation process, reduce dependency resolution times, and improve the handling of legacy dependencies. These improvements will benefit all NixOS users, including those who are currently experiencing slowdowns due to missing CUDA or GCC versions.

As Nix and Nixpkgs continue to evolve, it's important to stay informed about the latest developments and best practices. By following the NixOS community and keeping up with the latest releases, you can ensure that you're using the most efficient and performant configurations possible.

Conclusion: Addressing the Performance Bottleneck

The slower eval performance on nixpkgs master, particularly when dealing with CUDA and GCC versions, is a real issue that can impact development workflows and CI systems. However, by understanding the root cause of the problem and exploring potential solutions, you can mitigate the impact and continue to leverage the power and flexibility of NixOS. Remember to consider pinning nixpkgs, using nixpkgs-channels, and contributing to the community to help improve the overall NixOS experience. The flamegraphs provided offer a visual diagnosis, highlighting the tryEval function as the key area needing attention.

Ultimately, addressing this performance bottleneck requires a collaborative effort from the NixOS community. By sharing experiences, contributing to the development of Nixpkgs, and exploring innovative solutions, we can ensure that NixOS remains a powerful and efficient platform for software development and deployment.

For more in-depth information on Nix and Nixpkgs, visit the official NixOS website. This website provides comprehensive documentation, tutorials, and community resources to help you get the most out of NixOS.