Parallel algorithms for reducing derivation time of distinguishing experiments for nondeterministic finite state machines

Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce

“…A variety of studies have recently focused on the use of parallel processing techniques in order to derive CSs in a large-scale context: UIO sequences [17], harmonized state identifiers, and characterizing sets [18], synchronizing sequences [59,60]. For DSs generation, Hierons and Türker [61] and El-Fakih et al [62] introduced independently parallel multithreading implementations of the EA over the Central Processing Unit (CPU) and Graphics Processing Unit (GPU) architectures [63]. They conducted extensive experiments when considering a large variety of FSM classes, using different CPU-GPU architectures and workloads.…”

“…In order to reduce the execution time of constructing successors' table of all pairs of states for a given nondeterministic FSM, El-fakih et al [62] proposed different multithreading parallel approaches based on multicore CPU and GPU architecture. They considered two options: Thrust software platforms and GPU implementations using the CUDA platform.…”

“…Notice that our approach produces a truncated successor tree, also called successor table, for all pairs of states of a complete observable nondeterministic FSM. The conducted experiments in [62] show that when deriving distinguishing sequences, the construction time of the successor tree takes 96% of the whole EA's time. That is why three methods will be presented later in this section for the construction of the truncated successor tree.…”