Abstract
Networks of Evolutionary Processors (NEP) is a bio-inspired computational model defining theoretical computing devices able to solve NP-complete problems in an efficient manner. Networks of Polarized Evolutionary Processors (NPEP) is an evolution of the NEP model that presents a simpler and more natural filtering strategy to simulate the communication between cells. Up to now, it has not been possible to have implementations neither in vivo nor in vitro of these models. Therefore, the only way to analyze and execute NPEP devices is by means of ultra-scalable simulators able to encapsulate the inherent parallelism in their computations. Nowadays, there is a lack of such simulators able to handle the size of non trivial problems in a massively distributed computing environment. We propose as novelty NPEPE, a high scalability engine that runs NPEP descriptions using Apache Giraph on top of Hadoop platforms. Giraph is the open source counterpart of Google Pregel, an iterative graph processing system built for high scalability. NPEPE takes advantage of the inherent Giraph and Hadoop parallelism and scalablity to be able to deploy and run massive networks of NPEPs. We show several experiments to demonstrate that NPEP descriptions can be easily deployed and run using a NPEPE engine on a Giraph+Hadoop platform. To this end, the well known 3-colorability NP complete problem is described as a network of NPEPs and run on a 10 nodes cluster.
The research leading to these results has been developed within the ONTIC project, which has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2011) under grant agreement no. 619633.
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Gómez Canaval, S., Ordozgoiti Rubio, B., Mozo, A. (2015). NPEPE: Massive Natural Computing Engine for Optimally Solving NP-complete Problems in Big Data Scenarios. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds) New Trends in Databases and Information Systems. ADBIS 2015. Communications in Computer and Information Science, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-23201-0_23
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DOI: https://doi.org/10.1007/978-3-319-23201-0_23
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