As already stated as a “Law of Software Evolution”, over time, successful projects will see changes in requirements and consequently in their code bases. Each change increases complexity and makes subsequent changes even more difficult - unless “software erosion” (also known as technical debt) is proactively managed and reduced. Some of the aspects of software erosion that can be mitigated are:
1. Architecture Erosion Hits your Project Plans
First and foremost, when code bases exceed a certain size, a well-defined and maintained software architecture helps developers as the most important guidance. Maintaining the architecture and verifying that the CUDA sources follow the host–device boundaries, module responsibilities, and data-flow patterns is crucial. Static analysis ensures architectural consistency before erosion affects maintainability and performance. If no explicit architecture exists as such, static analysis can assist in recovering the architecture from the existing code base and externalize otherwise lost expert knowledge.
2. Code Duplication Leads to Inconsistent Changes
Ever had a déjà vu when fixing bugs? Did unexpected behavior mysteriously re-appear like bad memories from the past? Maybe your project experiences the effects of unmanaged code duplication also known as “code cloning”. While copy & paste programming is not always a bad idea in itself (if the code worked once, why should it not be working after hitting “paste”), the risk lies in accidentally multiplying bugs across the code base. Loosing track of them means your effort of chasing them and fixing your user’s bug reports will be like groundhogs’ day movies. Automated detection of structural duplication ensures that critical fixes and improvements land everywhere they should.
3. Code Metrics Exceed Acceptable Thresholds
Whatever you consider an acceptable size e.g. of a kernel, or a simple C-style function - think a short moment about the longest function you have seen in your career and if you really liked it. Using simple metrics (as simple as possible!), paired with clear instructions for actions when thresholds are violated. And give teams a clear signal when and how to act on code that needs refactoring.
4. Coding Guideline Compliance Cannot Be Guaranteed
Safety, reliability, and maintainability rely on consistent adherence to rules. NVIDIA's official CUDA C++ Safety Guidelines that were recently published, cover CUDA-specific pitfalls that are hard to detect manually, meanwhile static analysis can help developers with enforcing these guidelines systematically and work towards a sustainable and maintainable code base. (Pro tip: Axivion is the first tool to check for these guidelines ;-) )
5. Dead Code Accumulates
“Dead code” is a more complex topic than one would think at a first glance. Yes, in theory, you can delete code from your code base and it’s still kept in your version control. No, in practice you will never find it again, because it then is out of sight! However, unused kernels, abandoned configuration paths, and obsolete utilities inflate maintenance cost. At least knowing your dead code can reduce misunderstandings and maintenance effort. Dead-code analysis identifies this noise, so engineers can clarify what truly matters in the execution pipeline.
6. Cyclic Dependencies Confuse or Even Break the System
Cyclic dependencies between modules or components complicate testing, refactoring, and architectural evolution. Dependency analysis identifies and visualizes these cycles, enabling targeted resolution. Cyclic dependencies between function calls can lead to subtle failures at runtime and pose a challenge for code changes. Again, at least knowing that you are in a risky situation will help not to accidentally break things due to changes.
7. Continuous Baselining is Missing
Consider static analysis for CUDA codebases as above - with the system under analysis being a successful but large existing codebase - without a stable baseline, improvement efforts become unmanageable. Continuous baselining and incremental views on issues identified by static analysis provides a controlled process to detect key areas of improvement, track progress, and guide incremental cleanup of the CUDA codebase.
Are we Hitting a Nerve?
If one or more of the above signs ring a bell with you or you wonder how your code stands with regards to maintenance and sustainability, get in touch with us. Axivion for CUDA is built to verify your CUDA code.
