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Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.
MapReduce, distributed life simulations, cloud computing, large-scale life models, distributed software simulations, MPI, strip partitioning
Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing | Theory and Algorithms
Radenski, A. Using MapReduce Streaming for Distributed Life Simulation on the Cloud, In Advances in Artificial Life, ECAL 2013, MIT Press, 2013, 284-291.
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