Abstract
The uniting idea of both parallel computing and multi-robot systems is that having multiple processors or robots working on a task decreases the processing time. Typically we desire a linear speedup, that is, doubling the number of processing units halves the execution time. Sometimes superlinear scalability is observed in parallel computing systems and more frequently in multi-robot and swarm systems. Superlinearity means each individual processing unit gets more efficient by increasing the system size—a desired and rather counterintuitive phenomenon.
In an interdisciplinary approach, we compare abstract models of system performance from three different fields of research: parallel computing, multi-robot systems, and network science. We find agreement in the modeled universal properties of scalability and summarize our findings by formulating more generic interpretations of the observed phenomena. Our result is that scalability across fields can be interpreted as a tradeoff in three dimensions between too competitive and too cooperative processing schemes, too little information sharing and too much information sharing, while finding a balance between neither underusing nor depleting shared resources. We successfully verify our claims by two simple simulations of a multi-robot and a network system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ingham, A.G., Levinger, G., Graves, J., Peckham, V.: The Ringelmann effect: studies of group size and group performance. J. Exp. Soc. Psychol. 10(4), 371–384 (1974)
Gustafson, J.L.: Fixed time, tiered memory, and superlinear speedup. In: Proceedings of the Fifth Distributed Memory Computing Conference (DMCC5), pp. 1255–1260 (1990)
Helmbold, D.P., McDowell, C.E.: Modelling speedup (n) greater than n. IEEE Trans. Parallel Distrib. Syst. 1(2), 250–256 (1990)
Faber, V., Lubeck, O.M., White Jr., A.B.: Superlinear speedup of an efficient sequential algorithm is not possible. Parallel Comput. 3(3), 259–260 (1986)
Gunther, N.J., Puglia, P., Tomasette, K.: Hadoop super-linear scalability: the perpetual motion of parallel performance. ACM Queue 13(5), 46–55 (2015)
Ijspeert, A.J., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the exploitation of local interactions in autonomous collective robotics: the stick pulling experiment. Auton. Robots 11, 149–171 (2001)
Lein, A., Vaughan, R.T.: Adaptive multi-robot bucket brigade foraging. Artif. Life 11, 337 (2008)
Pini, G., Brutschy, A., Birattari, M., Dorigo, M.: Interference reduction through task partitioning in a robotic swarm. In: Sixth International Conference on Informatics in Control, Automation and Robotics-ICINCO, pp. 52–59 (2009)
Mondada, F., Bonani, M., Guignard, A., Magnenat, S., Studer, C., Floreano, D.: Superlinear physical performances in a SWARM-BOT. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 282–291. Springer, Heidelberg (2005). https://doi.org/10.1007/11553090_29
Hamann, H.: Towards swarm calculus: universal properties of swarm performance and collective decisions. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 168–179. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32650-9_15
Hamann, H.: Towards swarm calculus: urn models of collective decisions and universal properties of swarm performance. Swarm Intell. 7(2–3), 145–172 (2013)
Schneider-Fontán, M., Matarić, M.J.: A study of territoriality: The role of critical mass in adaptive task division. In: Maes, P., Wilson, S.W., Matarić, M.J., (eds.) From animals to animats IV, pp. 553–561. MIT Press (1996)
Arkin, R.C., Balch, T., Nitz, E.: Communication of behavioral state in multi-agent retrieval tasks. In: Book, W., Luh, J. (eds.) IEEE Conference on Robotics and Automation, vol. 3, pp. 588–594. IEEE Press, Los Alamitos (1993)
Lerman, K., Galstyan, A.: Mathematical model of foraging in a group of robots: effect of interference. Auton. Robots 13, 127–141 (2002)
Goldberg, D., Matarić, M.J.: Interference as a tool for designing and evaluating multi-robot controllers. In: Kuipers, B.J., Webber, B., (eds.) Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI 1997), pp. 637–642. MIT Press, Cambridge (1997)
Østergaard, E.H., Sukhatme, G.S., Matarić, M.J.: Emergent bucket brigading: a simple mechanisms for improving performance in multi-robot constrained-space foraging tasks. In: André, E., Sen, S., Frasson, C., Müller, J.P., (eds.) Proceedings of the Fifth International Conference on Autonomous Agents (AGENTS 2001), pp. 29–35. ACM, New York (2001)
Beckers, R., Holland, O.E., Deneubourg, J.L.: From local actions to global tasks: stigmergy and collective robotics. Artificial Life IV, pp. 189–197 (1994)
Lerman, K., Martinoli, A., Galstyan, A.: A review of probabilistic macroscopic models for swarm robotic systems. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 143–152. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_12
Khaluf, Y., Birattari, M., Rammig, F.: Probabilistic analysis of long-term swarm performance under spatial interferences. In: Dediu, A.-H., Martín-Vide, C., Truthe, B., Vega-Rodríguez, M.A. (eds.) TPNC 2013. LNCS, vol. 8273, pp. 121–132. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45008-2_10
Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., Dorigo, M.: Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton. Agents Multi Agent Syst. 28(1), 101–125 (2014)
Hamann, H., Schmickl, T., Wörn, H., Crailsheim, K.: Analysis of emergent symmetry breaking in collective decision making. Neural Comput. Appl. 21(2), 207–218 (2012)
Nembrini, J., Winfield, A.F.T., Melhuish, C.: Minimalist coherent swarming of wireless networked autonomous mobile robots. In: Hallam, B., Floreano, D., Hallam, J., Hayes, G., Meyer, J.A., (eds.) Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pp. 373–382. MIT Press, Cambridge (2002)
Bjerknes, J.D., Winfield, A., Melhuish, C.: An analysis of emergent taxis in a wireless connected swarm of mobile robots. In: Shi, Y., Dorigo, M. (eds.) IEEE Swarm Intelligence Symposium, pp. 45–52. IEEE Press, Los Alamitos (2007)
Meister, T., Thenius, R., Kengyel, D., Schmickl, T.: Cooperation of two different swarms controlled by BEECLUST algorithm. In: Mathematical Models for the Living Systems and Life Sciences (ECAL), pp. 1124–1125 (2013)
Hamann, H.: Modeling and investigation of robot swarms. Master’s thesis, University of Stuttgart, Germany (2006)
Jeanne, R.L., Nordheim, E.V.: Productivity in a social wasp: per capita output increases with swarm size. Behav. Ecol. 7(1), 43–48 (1996)
Lighthill, M.J., Whitham, G.B.: On kinematic waves II. A theory of traffic flow on long crowded roads. Proc. Royal Soc. London A229(1178), 317–345 (1955)
Gunther, N.J.: A simple capacity model of massively parallel transaction systems. In: CMG National Conference, pp. 1035–1044 (1993)
Lazer, D., Friedman, A.: The network structure of exploration and exploitation. Adm. Sci. Q. 52, 667–694 (2007)
Kauffman, S.A., Levin, S.: Towards a general theory of adaptive walks on rugged landscapes. J. Theor. Biol. 128(1), 11–45 (1987)
Eiben, Á.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-662-44874-8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hamann, H. (2018). Superlinear Scalability in Parallel Computing and Multi-robot Systems: Shared Resources, Collaboration, and Network Topology. In: Berekovic, M., Buchty, R., Hamann, H., Koch, D., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2018. ARCS 2018. Lecture Notes in Computer Science(), vol 10793. Springer, Cham. https://doi.org/10.1007/978-3-319-77610-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-77610-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77609-5
Online ISBN: 978-3-319-77610-1
eBook Packages: Computer ScienceComputer Science (R0)