Mohamed Faouzi Atig

Parosh Aziz Abdulla, Mohamed Faouzi Atig, Govind Rajanbabu et al.

Remote Direct Memory Access (RDMA) is a technology that allows direct memory access from the memory of one computer into that of another without involving either one's operating system. This enables high-throughput, low-latency networking, which is especially useful in massively parallel computer clusters. In this paper, we study the reachability and robustness problems for RDMA programs. We show that reachability is undecidable in general, even for a restricted fragment of the model. We then focus on robustness, which asks whether a program exhibits the same behaviours under the RDMA and sequential consistency (SC) semantics, and prove that this problem is decidable. Our central technical result establishes a normal form for robustness violations, showing that any non-robust program admits a violating execution of a specific form. We then leverage this normal form to obtain a decision procedure that reduces robustness to reachability in finite-state programs with counters, yielding an EXPSPACE upper bound in the general case, and a PSPACE upper bound in the absence of poll operations. Finally, we also show that both of these bounds are optimal.

Parosh Aziz Abdulla, Mohamed Faouzi Atig, Julie Cailler et al.

This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point of the tree significantly impacts solving time, motivating the use of Graph Neural Networks (GNNs) for efficient split decision-making. Split decisions are encoded as multi-classification tasks, and five graph representations of word equations are introduced to encode their structural information for GNNs. The algorithm is implemented as a solver named DragonLi. Experiments are conducted on artificial and real-world benchmarks. The algorithm performs particularly well on satisfiable problems. For single word \mbox{equations}, DragonLi can solve significantly more problems than well-established string solvers. For the conjunction of multiple word equations, DragonLi is competitive with state-of-the-art string solvers.

Parosh Aziz Abdulla, Mohamed Faouzi Atig, Julie Cailler et al.

Nielsen transformation is a standard approach for solving word equations: by repeatedly splitting equations and applying simplification steps, equations are rewritten until a solution is reached. When solving a conjunction of word equations in this way, the performance of the solver will depend considerably on the order in which equations are processed. In this work, the use of Graph Neural Networks (GNNs) for ranking word equations before and during the solving process is explored. For this, a novel graph-based representation for word equations is presented, preserving global information across conjuncts, enabling the GNN to have a holistic view during ranking. To handle the variable number of conjuncts, three approaches to adapt a multi-classification task to the problem of ranking equations are proposed. The training of the GNN is done with the help of minimum unsatisfiable subsets (MUSes) of word equations. The experimental results show that, compared to state-of-the-art string solvers, the new framework solves more problems in benchmarks where each variable appears at most once in each equation.