Bringing Rigorous Static Analysis to Your CUDA Code

The rise of AI at the edge demands that GPU code meets the same rigorous quality standards as traditional CPU code. Discover a solution that brings unified static analysis to NVIDIA CUDA, ensuring safety and compliance across your entire embedded system.

As AI models move from the cloud to the edge, powering autonomous vehicles, advanced robotics, and real-time medical diagnostics, developers are increasingly relying on GPU acceleration to meet performance and latency demands.

NVIDIA’s CUDA platform has become the foundation for this new era of intelligent, high-performance embedded systems.

The integration of artificial intelligence and GPU acceleration is now a cornerstone of innovation across the automotive, aerospace, medical, and industrial sectors. These domains leverage CUDA to meet the intensive computational demands of AI inference and data processing. However, the very language extensions that make CUDA C/C++ so powerful have historically created a significant challenge for software quality assurance.

Traditional static analysis tools designed for standard C/C++ are unable to process CUDA-specific constructs.

This restriction has left a critical gap in the development life cycle, where some of the most performance-sensitive and safety-critical code components could not be automatically analyzed for defects, security vulnerabilities, or standards compliance.

The Challenge: Ensuring Quality Across a Heterogeneous Codebase

Development teams are tasked with embedding trained AI models directly into applications, where they often form the core of decision-making systems. The CUDA code powering these models is subject to the same, if not more stringent, requirements for reliability, security, and functional safety as traditional embedded software.

Functional safety standards like ISO 26262, DO-178C, IEC 61508, and IEC 62304 mandate a demonstrable and consistent approach to code quality across the entire system.

The inability to apply automated static analysis to CUDA modules introduces:

• Risk
• Increased manual review overhead
• Complicated compliance reporting

Floating-Point Complexity

CUDA kernels often rely heavily on floating-point or double-precision arithmetic, which is uncommon in traditional embedded systems.

Floating-point values are harder to analyze due to rounding behavior, precision limitations, and platform-specific representations. Ensuring correctness in conversions, comparisons, and arithmetic operations is essential for safety-critical AI applications.

Rules such as CERT C FLP34, FLP36-C, and FLP37-C provide guidance for safe handling of floating-point values, which static analysis tools must enforce across heterogeneous GPU architectures.

For example, in an automotive ADAS perception stack, CUDA kernels perform critical object detection and classification tasks. Ensuring these modules are free from race conditions, memory access errors, and security flaws is vital for functional safety certification.

The Solution: Unified Static Analysis for CPU and GPU Code

Parasoft addresses this quality challenge head-on in C/C++test 2025.2, which introduces static analysis support for CUDA source files (.cu). This capability allows development organizations to extend their established code quality practices seamlessly to GPU-accelerated components.

CUDA analysis is fully integrated into Parasoft’s CI/CD workflow support, enabling teams to execute unified analyses, dashboards, and reports without modifying their existing build or compliance infrastructure.

C/C++test enables teams to identify defects and security vulnerabilities in CUDA code automatically and enforce coding standards across their entire product, ensuring consistent quality and compliance—even in modules developed using the CUDA SDK.

The Result?

• Reduced risk
• Shorter development cycles
• Improved overall software robustness in AI-powered, GPU-accelerated systems

Key Capabilities

• Comprehensive standards enforcement. Apply the same set of industry-proven coding standards, including MISRA C/C++, AUTOSAR C++14, CERT C/C++, and JSF, to both host and device code with a single, unified configuration.
• Early defect detection. Identify coding errors, security flaws, and performance-impacting issues early in the development life cycle, reducing remediation costs and mitigating project risk.
• Streamlined compliance. Automate the generation of audit-ready compliance reports and traceability matrices for functional safety certifications, encompassing the entire software stack.

Integrated Support for IDE & CI/CD Environments

Parasoft’s CUDA static analysis runs seamlessly in both the IDE and CI/CD environments, providing complete flexibility for engineering teams.

In the IDE

Developers can perform CUDA static analysis directly within supported IDEs such as Eclipse, Visual Studio, or VS Code, depending on your Parasoft configuration. The IDE integration ensures developers can fix issues early, right where they code, before they propagate to the build system or test infrastructure. This allows:

• Real-time detection of coding errors, security issues, and rule violations as CUDA source code is written.
• Immediate feedback on CUDA-specific constructs.
• Consistent application of coding standards like MISRA C/C++, AUTOSAR C++14, CERT C/C++, and JSF across both host and device code.

In the CI/CD Pipeline

CUDA analysis can be executed headlessly as part of automated builds in Jenkins, GitLab, Bamboo, or Azure DevOps. Teams can continuously monitor compliance, enforce coding standards, and generate centralized reports using Parasoft DTP to ensure enterprise-level traceability and audit readiness. This enables:

• Continuous enforcement of coding standards and security checks across both CPU and GPU code.
• Batch analysis of large projects, including those that compile CUDA kernels using the NVIDIA toolchain.
• Automated report generation (compliance matrices, dashboards, and metrics) for quality gates and certification evidence.
• Seamless integration with Parasoft DTP for centralized analytics and policy management.

This dual-environment capability ensures consistent maintenance of CUDA quality and compliance checks—from local development to production-grade builds—without disrupting established workflows.

Strategic Implications for Safety-Critical Development

The extension of static analysis to CUDA C/C++ is more than a feature update. It’s a strategic enabler for the next generation of intelligent systems.

This extension provides the foundational assurance required to deploy AI and GPU-accelerated computing in environments where failure is not an option.

What Organizations Gain

• Immediate value. Reduced manual review effort, faster compliance reporting, and a more efficient path to certification.
• Long-term advantage. A unified quality framework for hybrid CPU/GPU architectures, future-proofing development processes as AI becomes increasingly pervasive.
• Stronger confidence in calculations. Maintain consistent performance and compliance across GPUs to support safety and coding standards like MISRA and CERT.

Conclusion

As the industry pivots toward AI-driven, GPU-accelerated applications, the tools for software assurance must evolve in parallel. Parasoft’s commitment to this evolution ensures that engineering teams can maintain the highest levels of quality, security, and safety across their entire technology portfolio, from the CPU to the GPU.