Abstract
We regard the MapReduce mechanism as a unifying principle in the domain of computer science. Going back to the roots of AI and circuits, we show that the MapReduce mechanism is consistent with the basic mechanisms acting at all the levels, from circuits to Hadoop. At the circuit level, the elementary circuit is the smallest and simplest MapReduce circuit—the elementary multiplexer. On the structural and informational chain, starting from circuits and up to Big Data processing, we have the same behavioral pattern: the MapReduce basic rule. For a unified parallel computing perspective, we propose a novel starting point: Kleene’s partial recursive functions model. In this model, the composition rule is a true MapReduce mechanism. The functional forms, in the functional programming paradigm defined by Backus, are also MapReduce type actions. We propose an abstract model for parallel engines which embodies various forms of MapReduce. These engines are represented as a hierarchy of recursive MapReduce modules. Finally, we claim that the MapReduce paradigm is ubiquitous, at all computational levels.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
The lowest level of the generic engine was implemented as BA1024 SoC for HDTV applications [12].
- 4.
GOPS stands for Giga Operations Per Second.
References
J. Backus: “Can Programming Be Liberated from the von Neumann Style? A Functional Style and Its Algebra of Programs”. Communications of the ACM, August 1978, 613–641.
A. Church: “An Unsolvable Problem of Elementary Number Theory”, in American Journal of Mathematics, vol. 58, p. 345–363, 1936.
B. B. Cohen: Howard Aiken: Portrait of a Computer Pioneer. Cambridge, MA, USA: MIT Press, 2000.
J. Dean, J. Ghemawat: “MapReduce: Simplified Data Processing on Large Clusters”, Proceedings of the 6th Symp. on Operating Systems Design and Implementation, Dec. 2004.
J. Flatow: “What exactly is the Disco distributed computing framework?”. http://www.quora.com/What-exactly-is-the-Disco-distributed-computing-framework
T. Hoefler, A. Lumsdaine, J. Dongarra: “Towards Efficient MapReduce Using MPI”, Proceedings of the 16th European PVM/MPI Users’ Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface, 2009, pp. 240–249. http://htor.inf.ethz.ch/publications/img/hoefler-map-reduce-mpi.pdf
S. C. Kleene: “General Recursive Functions of Natural Numbers”, in Math. Ann., 112, 1936.
M. Maliţa, G. M. Ştefan: “On the Many-Processor Paradigm”, Proceedings of the 2008 World Congress in Computer Science, Computer Engineering and Applied Computing (PDPTA’08), 2008.
M. Maliţa, G. M. Ştefan, D. Thiébaut: “Not multi-, but many-core: designing integral parallel architectures for embedded computation”, SIGARCH Comput. Archit. News, 35(5):3238, 2007.
D. Patterson: “The trouble with multi-core”, Spectrum, IEEE, 47(7):28–32, 2010.
B. T. Rao, L. S. Reddy: “Survey on improved scheduling in Hadoop MapReduce in cloud environments”, CoRR, 2012.
G. M. Ştefan: “One-Chip TeraArchitecture”, in Proceedings of the 8th Applications and Principles of Information Science Conference, Okinawa, Japan on 11–12 January 2009. http://arh.pub.ro/gstefan/teraArchitecture.pdf
G. M. Ştefan, M. Maliţa: “Can One-Chip Parallel Computing Be Liberated From Ad Hoc Solutions? A Computation Model Based Approach and Its Implementation”, 18th International Conference on Circuits, Systems, Communications and Computers (CSCC 2014), Santorini Island, Greece, July 17–21, 2014, 582–597.
J. Talbot, R. M. Yoo, C. Kozyrakis: “Phoenix++: Modular MapReduce for Shared-memory Systems”, Proceedings of the Second International Workshop on MapReduce and Its Applications, New York, NY, USA, 2011. http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf
J. von Neumann: “First Draft of a Report on the EDVAC”, reprinted in IEEE Annals of the History of Computing, Vol. 5, No. 4, 1993.
T. White: Hadoop: The Definitive Guide, O’Reilly, 2009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Andonie, R., Maliţa, M., Ştefan, G.M. (2017). MapReduce: From Elementary Circuits to Cloud. In: Kreinovich, V. (eds) Uncertainty Modeling. Studies in Computational Intelligence, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-51052-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-51052-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51051-4
Online ISBN: 978-3-319-51052-1
eBook Packages: EngineeringEngineering (R0)