Register Allocation Based on Boolean Satisfiability

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)


Graph Coloring is an effective method which is used to solve the register allocation problem, it is also an NP-complete problem, heuristic algorithms and various evolutionary algorithms have been proposed in order to improve the performance of register allocation, in this paper, we propose to solve this problem by converting the graph coloring problem into Boolean Satisfiability problem (SAT), the experiments show that our algorithm can use fewer number of registers, which can improve the execution efficiency of the generated codes.


Register Allocation Graph Coloring SAT Problem 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chaitin, G.J., et al.: Register Allocation via Coloring. Computer Languages 6(1), 47–57 (1981)CrossRefGoogle Scholar
  2. 2.
    Gend, L.P., Amit, M., Neha, B.: An Efficient Colouring of Graphs Using Less Number of Colours. In: World Congress on Information and Communication Technologies, pp. 666–669 (2012)Google Scholar
  3. 3.
    Chen, W.D.: New Algorithms for Graph Coloring Problem. Microcomputer Applications 25(4), 391–395 (2004)Google Scholar
  4. 4.
    Carla, N.L., Mauro, H.M., Anderson, F.: Register Allocation with Graph Coloring by Ant Colony Optimization. In: International Conference of the Chilean Computer Science Society, pp. 247–255 (2011)Google Scholar
  5. 5.
    Carla, N.L., Mauro, H.M., Anderson, F.: Register Allocation by Evolutionary Algorithm. In: International Conference of the Chilean Computer Science Society, pp. 207–215 (2012)Google Scholar
  6. 6.
    Raja, M., Gopalakrishnan, S.: A New Genetic Algorithm for Graph Coloring. In: International Conference on Computational Intelligence, Modelling and Simulation, pp. 49–54 (2013)Google Scholar
  7. 7.
    Dorrigiv, M., Markib, H.Y.: Algorithms for the graph coloring problem based on swarm intelligence. In: CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 473–478 (2012)Google Scholar
  8. 8.
    Ge, F.Z., Wei, Z., Tian, Y.M., Huang, Z.J.: Chaotic ant swarm for graph coloring. In: IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), pp. 512–516 (2010)Google Scholar
  9. 9.
    Yesil, C., Yilmaz, B., Korkmaz, E.E.: Hybrid local search algorithms on Graph Coloring Problem. In: International Conference on Hybrid Intelligent Systems (HIS), pp. 468–473 (2011)Google Scholar
  10. 10.
    Consoli, P., Collera, A., Pavone, M.: Swarm Intelligence heuristics for Graph Coloring Problem. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1909–1916 (2013)Google Scholar
  11. 11.
    Fister, I., Brest, J.: Using differential evolution for the graph coloring. In: IEEE Symposium on Differential Evolution (SDE), pp. 1–7 (2011)Google Scholar
  12. 12.
    Falk, H.: WCET-aware register allocation based on graph coloring. In: ACM/IEEE Conference on Design Automation, pp. 726–731 (2009)Google Scholar
  13. 13.
    Mittal, A., Jain, P., Mathur, S., Bhatt, P.: Graph Coloring with Minimum Colors: An Easy Approach. In: International Conference on Communication Systems and Network Technologies (CSNT), pp. 638–641 (2011)Google Scholar
  14. 14.
    Wang, X.H., Qiao, Q.G.: Solving Graph Coloring Problems Based on a Chaos Nueral Network with Non-monotonous Activation Function. In: Fifth International Conference on Natural Computation, ICNC, vol. 1, pp. 414–417 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Software CollegeShenyang Normal UniversityShenyangChina

Personalised recommendations