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.
Why Software Architecture Matters
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:
- onboard new developers
- document design rationale
- assess the impact of large-scale changes
- and identify or avoid architectural flaws early
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.
Separation of Concerns: Host vs Device
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:
- poor performance (e.g., due to underutilized parallelism)
- runtime errors such as out-of-bounds accesses
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:
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transfer data from host to device (global memory)
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copy it into shared memory at the beginning of a kernel
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perform computation
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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.
Leveraging Libraries
Developers often rely on established CUDA libraries to improve productivity and performance. These include:
- CUDA C++ Core Libraries (CCCL), which provide abstractions and algorithmic utilities similar to the C++ STL
- cuBLAS, offering optimized linear algebra operations
- cuDNN, designed for deep neural networks and inference tasks
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:
- Kernel Layer
Contains device code (CUDA kernels), and may use CCCL utilities. - External Libraries Layer
Contains external libraries like CCCL and cuBLAS. - Middle Layer
Offers higher-level computations based on kernels or external libraries like cuBLAS. - Application Layer
Contains business or domain logic and uses only the middle layer.
This separation improves modularity and supports portability across platforms. It can also help simplify testing and maintenance.
Figure 1: High-level Layered Architecture for a CUDA-based Project
Keeping Code and Architecture in Sync
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:
- map code to architectural components
- detect violations or inconsistencies
- and provide automated feedback to developers
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.
