CUDA is a parallel computing platform and programming model developed by NVIDIA. It extends C and C++ with additional keywords and APIs that allow developers to write code that executes directly on NVIDIA GPUs, alongside traditional CPU code.
CUDA powers many of today’s most performance-critical domains, including scientific computing, image processing, and most notably, artificial intelligence and deep learning. In modern software systems, CUDA plays a crucial role in areas that benefit from data parallelism, most prominently deep neural networks, inference engines, and large-scale simulations.
However, effective use of CUDA requires more than just understanding GPU hardware. It also demands a well-designed software architecture to balance performance, modularity, and long-term maintainability.
Maintaining a clear software architecture is essential for any project that extends beyond the prototype stage.
It captures the big ideas and decisions: the components of a system, their interactions, and dependencies.
Such an architectural model helps:
Adding CUDA code to a project increases the importance of having such a well-defined architecture due to the complexity of GPU programming and its interaction with CPU-side logic.
In CUDA source code, logic is divided into host code (running on the CPU) and device code (executing on the GPU). An important concept in CUDA is the kernel, a function executed on the GPU and launched from the host.
Calling a kernel involves specifying grid and block dimensions, parameters that define how threads are launched and distributed across GPU hardware. This invocation protocol must be carefully configured based on the target architecture and the kernel’s logic.
Improper configuration may lead to:
Unfortunately, many such issues cannot be detected statically by the compiler. Here, a clear architectural separation, e.g. by modeling code variants for different device configurations, can prevent misuse and encourage safe, consistent kernel launches.
Another key consideration is CUDA’s device memory hierarchy. Shared memory (accessible by all threads within a block) offers lower latency than global memory, but all data transferred from the host to the device initially resides in global memory.
A common pattern is to:
transfer data from host to device (global memory)
copy it into shared memory at the beginning of a kernel
perform computation
and copy results back to global memory
A well-designed architecture can support this by enforcing kernel wrapper functions or pre-processing steps (e.g. copyToShared()) before launching compute-intensive logic.
Developers often rely on established CUDA libraries to improve productivity and performance. These include:
As with any external dependency, it is important to explicitly model the use of these libraries in the project architecture. A proven approach is to define clear software layers, such as:
This separation improves modularity and supports portability across platforms. It can also help simplify testing and maintenance.
A software architecture is only useful if it reflects the actual structure of the codebase. Manual documentation can quickly become outdated, especially in large or fast-evolving projects. To avoid architectural drift, specialized tools for static analysis can help.
Tools that allow analyzing both C++ and CUDA source code and provide automated architecture verification are able to:
This kind of architectural conformance checking supports higher software quality, reduces integration problems, and helps development teams identify architectural erosion early.
Maintaining a clean, layered architecture (especially in CUDA-based systems) enables better scalability, faster debugging, and fewer surprises at runtime. Learn how Axivion for CUDA can help you achieve just that.
This might interest you: Read our blog post about CUDA's Impact on Static Code Analysis.
To find out how Axivion can support your specific use case, request a demo from one of our experts.