Dynamic Message Processing and Transactional Memory in the Actor Model

  • Yaroslav HaydukEmail author
  • Anita Sobe
  • Pascal Felber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9038)


With the trend of ever growing data centers and scaling core counts, simple programming models for efficient distributed and concurrent programming are required. One of the successful principles for scalable computing is the actor model, which is based on message passing. Actors are objects that hold local state that can only be modified by the exchange of messages. To avoid typical concurrency hazards, each actor processes messages sequentially. However, this limits the scalability of the model. We have shown in former work that concurrent message processing can be implemented with the help of transactional memory, ensuring sequential processing, when required. This approach is advantageous in low contention phases, however, does not scale for high contention phases. In this paper we introduce a combination of dynamic resource allocation and non-transactional message processing to overcome this limitation. This allows for efficient resource utilization as these two mechanisms can be handled in parallel. We show that we can substantially reduce the execution time of high-contention workloads in a micro-benchmark as well as in a real-world application.


Actor Model Training Image List Actor Transactional Memory Simulation Grid 
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  1. 1.
    Hewitt, C., Bishop, P., Steiger, R.: A universal modular actor formalism for artificial intelligence. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 235–245 (1973)Google Scholar
  2. 2.
    Karmani, R.K., Shali, A., Agha, G.: Actor frameworks for the jvm platform: A comparative analysis. In: International Conference on Principles and Practice of Programming in Java (PPPJ), pp. 11–20 (2009)Google Scholar
  3. 3.
    Hayduk, Y., Sobe, A., Harmanci, D., Marlier, P., Felber, P.: Speculative concurrent processing with transactional memory in the actor model. In: Baldoni, R., Nisse, N., van Steen, M. (eds.) OPODIS 2013. LNCS, vol. 8304, pp. 160–175. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Didona, D., Felber, P., Harmanci, D., Romano, P., Schenker, J.: Identifying the optimal level of parallelism in transactional memory systems. In: Gramoli, V., Guerraoui, R. (eds.) NETYS 2013. LNCS, vol. 7853, pp. 233–247. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Heiss, H.U., Wagner, R.: Adaptive load control in transaction processing systems. Universität Karlsruhe (1991)Google Scholar
  6. 6.
    Herlihy, M., Luchangco, V., Moir, M., Scherer III, W.N.: Software transactional memory for dynamic-sized data structures. In: Annual Symposium on Principles of Distributed Computing (PODC), pp. 92–101. ACM (2003)Google Scholar
  7. 7.
    Moravan, M.J., Bobba, J., Moore, K.E., Yen, L., Hill, M.D., Liblit, B., Swift, M.M., Wood, D.A.: Supporting nested transactional memory in logtm. ACM SIGPLAN Notices 41(11), 359–370 (2006)CrossRefGoogle Scholar
  8. 8.
    Bronson, N.G., Chafi, H., Olukotun, K.: CCSTM: A library-based STM for Scala. In: Annual Scala Workshop at Scala Days (2010)Google Scholar
  9. 9.
    Agha, G.A., Mason, I.A., Smith, S.F., Talcott, C.L.: A foundation for actor computation. Journal of Functional Programming, Cambridge University Press 7(1), 1–72 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Karmani, R.K., Agha, G.: Actors. In: Padua, D. (ed.) Encyclopedia of Parallel Computing, pp. 1–11. Springer (2011)Google Scholar
  11. 11.
    Haller, P.: On the integration of the actor model in mainstream technologies: A Scala perspective. In: Programming Systems, Languages and Applications based on Actors, Agents, and Decentralized Control Abstractions (AGERE), pp. 1–6 (2012)Google Scholar
  12. 12.
    Goodman, D., Khan, B., Khan, S., Luján, M., Watson, I.: Software transactional memories for scala. Journal of Parallel and Distributed Computing, Elsevier 73(2), 150–163 (2013)CrossRefGoogle Scholar
  13. 13.
    Harris, T., Larus, J., Rajwar, R.: Transactional Memory. 2nd edn. Morgan and Claypool Publishers (2010)Google Scholar
  14. 14.
    Imam, S.M., Sarkar, V.: Integrating task parallelism with actors. In: International Conference on Object Oriented Programming Systems Languages and Applications (OOPSLA), pp. 753–772. ACM (2012)Google Scholar
  15. 15.
    Scholliers, C., Tanter, E., Meuter, W.D.: Parallel actor monitors. In: Brazilian Symposium on Programming Languages (SBLP) (2010)Google Scholar
  16. 16.
  17. 17.
    Dragojević, A., Guerraoui, R., Kapalka, M.: Stretching transactional memory. ACM Sigplan Notices 44(6), 155–165 (2009)CrossRefGoogle Scholar
  18. 18.
    Guardiano, F.B., Srivastava, R.M.: Multivariate geostatistics: Beyond bivariate moments. Geostatistics Tróia 1, 133–144 (1993)CrossRefGoogle Scholar
  19. 19.
    Mariethoz, G., Renard, P., Straubhaar, J.: The Direct Sampling method to perform multiple-point geostatistical simulations. Water Resources Research, American Geophysical Union 46(11), 1–14 (2010)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  1. 1.University of NeuchatelNeuchatelSwitzerland

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