We present ESBMC-GPU tool, an extension to the Efficient SMT-Based Context-Bounded Model Checker (ESBMC), which is aimed at verifying Graphics Processing Unit (GPU) programs written for the Compute Unified Device Architecture (CUDA) platform. ESBMC-GPU uses an operational model, that is, an abstract representation of the standard CUDA libraries, which conservatively approximates their semantics, in order to verify CUDA-based programs. It then explicitly explores the possible interleavings (up to the given context bound), while treats each interleaving itself symbolically. Additionally, ESBMC-GPU employs the monotonic partial order reduction and the two-thread analysis to prune the state space exploration. Experimental results show that ESBMC-GPU can successfully verify 82% of all benchmarks, while keeping lower rates of false results. Going further than previous attempts, ESBMC-GPU is able to detect more properties violations than other existing GPU verifiers due to its ability to verify errors of the program execution flow and to detect array out-of-bounds and data race violations. exploration, similar to Cordeiro et al. [4]. In particular, we explicitly explore the possible interleavings (up to the given context bound), while we treat each interleaving itself symbolically w.r.t. a given property.To prune the state-space exploration, we apply Monotonic Partial Order Reduction (MPOR) [12] to CUDA programs, which eliminates redundant interleavings without missing any behavior that can be exhibited by the program. We have modified the MPOR algorithm to identify transitions between threads that accessed different memory locations in the same array. Because CUDA kernels typically produce regular and independent access to explore the benefits of the GPU execution model, the application of MPOR routinely leads to substantial performance improvements in most benchmarks. Thus, using operational models that simulate CUDA libraries, together with MPOR implementation in ESBMC-GPU, we achieve significant (correct) results of CUDA kernels verification, primarily when compared with other state-of-the-art GPU verifiers [3,[7][8][9]. Additionally, the present approach considers low-level aspects related to dynamic memory allocation, data transfer, memory deallocation, and overflow. Such violations are typically present in CUDA programs, however, they are routinely ignored by most GPU verifiers. Thus, we provide a more precise verification than other existing approaches, considering soundness of data passed by the main program to the kernel, with the drawback of leading to a higher verification time. ContributionsWe make four major contributions:we extend benefits of SMT-based context-bounded model checking for CUDA programs, in the context of parallel programming for GPUs, to detect more failures than other existing approaches, while keeping lower rates of false results; although SMT-based context-bounded model checking is not a novel technique, we have not seen in the literature its application to verify CUDA programs. this wor...

The Compute Unified Device Architecture (CUDA) is a programming model used for exploring the advantages of graphics processing unit (GPU) devices, through parallelization and specialized functions and features. Nonetheless, as in other development platforms, errors may occur, due to traditional software creation processes, which may even compromise the execution of an entire system. In order to address such a problem, ESBMC-GPU was developed, as an extension to the Efficient SMT-Based Context-Bounded Model Checker (ESBMC). In summary, ESBMC processes input code through ESBMC-GPU and an abstract representation of the standard CUDA libraries, with the goal of checking a set of desired properties. Experimental results showed that ESBMC-GPU was able to correctly verify 85% of the chosen benchmarks and it also overcame other existing GPU verifiers regarding the verification of data-race conditions, array out-of-bounds violations, assertive statements, pointer safety, and the use of specific CUDA features.