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...

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We describe the results of an industrial-academic collaboration among the Graduate Program in Electrical Engineering (PPGEE), the Electronics and Information Research Centre (CETELI), and Samsung Eletrônica da Amazônia Ltda. (Samsung), which aims at training human resources for Samsung's research and development (R&D) areas. Inspired by co-operative education systems, this collaboration offers an academic experience by means of a complementary training programme (CTP), in order to train undergraduates and graduate students in electrical and computer engineering, with especial emphasis on digital television (TV), industrial automation, and mobile devices technologies. In particular, this cooperation has provided scholarships for students and financial support for professors and coordinators in addition to the construction of a new building with new laboratories, classrooms, and staff rooms, to assist all research and development activities. Additionally, the cooperation outcomes led to applications developed for Samsung's mobile devices, digital TV, and production processes, an increase of 37% in CETELI's scientific production (i.e., conference and journal papers) as well as professional training for undergraduates and graduate students.

Este artigo apresenta uma extensão da ferramenta Efficient SMTBased Context-Bounded Model Checker (ESBMC) para verificar programas que executam em unidades de processamento gráfico (GPU), chamado de ESBMCGPU. Em especial, ESBMC-GPU é um verificador de modelos limitado baseado nas teorias do módulo da satisfatibilidade para programas desenvolvidos na arquitetura de dispositivo unificado de computação (CUDA). O ESBMC-GPU é baseado em um modelo operacional, uma representação abstrata das bibliotecas padrões do CUDA que conservadoramente aproxima suas semânticas. Com ESBMC-GPU, é possível verificar mais programas CUDA reais do que outras abordagens existentes.