Saraç, N. EgeIST Austria; Altun, Ömer Faruk; Atam, Kamil Tolga; Karahoda, Sertac; Kaya, Kamer; Yenigün, Hüsnü
For automata, synchronization, the problem of bringing an automaton to a particular state regardless of its initial state, is important. It has several applications in practice and is related to a fifty-year-old conjecture on the length of the shortest synchronizing word. Although using shorter words increases the effectiveness in practice, finding a shortest one (which is not necessarily unique) is NP-hard. For this reason, there exist various heuristics in the literature. However, high-quality heuristics such as SynchroP producing relatively shorter sequences are very expensive and can take hours when the automaton has tens of thousands of states. The SynchroP heuristic has been frequently used as a benchmark to evaluate the performance of the new heuristics. In this work, we first improve the runtime of SynchroP and its variants by using algorithmic techniques. We then focus on adapting SynchroP for many-core architectures, and overall, we obtain more than 1000× speedup on GPUs compared to naive sequential implementation that has been frequently used as a benchmark to evaluate new heuristics in the literature. We also propose two SynchroP variants and evaluate their performance.
Expert Systems with Applications
This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) [grant number 114E569]. This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). We would like to thank the authors of (Roman & Szykula, 2015) for providing their heuristics implementations, which we used to compare our SynchroP implementation as given in Table 11.
Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. Boosting expensive synchronizing heuristics. Expert Systems with Applications. 2021;167(4). doi:10.1016/j.eswa.2020.114203
Sarac, N. E., Altun, Ö. F., Atam, K. T., Karahoda, S., Kaya, K., & Yenigün, H. (2021). Boosting expensive synchronizing heuristics. Expert Systems with Applications. Elsevier. https://doi.org/10.1016/j.eswa.2020.114203
Sarac, Naci E, Ömer Faruk Altun, Kamil Tolga Atam, Sertac Karahoda, Kamer Kaya, and Hüsnü Yenigün. “Boosting Expensive Synchronizing Heuristics.” Expert Systems with Applications. Elsevier, 2021. https://doi.org/10.1016/j.eswa.2020.114203.
N. E. Sarac, Ö. F. Altun, K. T. Atam, S. Karahoda, K. Kaya, and H. Yenigün, “Boosting expensive synchronizing heuristics,” Expert Systems with Applications, vol. 167, no. 4. Elsevier, 2021.
Sarac NE, Altun ÖF, Atam KT, Karahoda S, Kaya K, Yenigün H. 2021. Boosting expensive synchronizing heuristics. Expert Systems with Applications. 167(4), 114203.
Sarac, Naci E., et al. “Boosting Expensive Synchronizing Heuristics.” Expert Systems with Applications, vol. 167, no. 4, 114203, Elsevier, 2021, doi:10.1016/j.eswa.2020.114203.