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Forum MP. Mpi: a message-passing interface standard. Technical report, Knoxville; 1994.
Ghoting A, Krishnamurthy R, Pednault E, Reinwald B, Sindhwani V, Tatikonda S, Tian Y, Vaithyanathan S. Systemml: declarative machine learning on mapreduce. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering; 2011. p. 231–42.
Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data. IEEE Intell Syst. 2009;24(2): 8–12.
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Konda P, Kumar A, Ré C, Sashikanth V. Feature selection in enterprise analytics: a demonstration using an r-based data analytics system. Proc VLDB Endow. 2013;6(12):1306–9.
Kraska T, Talwalkar A, Duchi JC, Griffith R, Franklin MJ, Jordan MI. Mlbase: a distributed machine-learning system. In: Proceedings of the 6th Biennial Conference on Innovative Data Systems Research; 2013.
Niu F, Recht B, Ré C, Wright SJ. Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Advances in Neural Information Proceedings of the Systems 24, Proceedings of the 25th Annual Conference on Neural Information Proceedings of the Systems; 2011.
Sujeeth AK, Lee H, Brown KJ, Chafi H, Wu M, Atreya AR, Olukotun K, Rompf T, Odersky M. Optiml: an implicitly parallel domainspecific language for machine learning. In: Proceedings of the 28th International Conference on Machine Learning; 2011.
Yu Y, Isard M, Fetterly D, Budiu M, Erlingsson U, Gunda PK, Currey J. Dryadlinq: a system for general-purpose distributed data-parallel computing using a high-level language. In: Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation; 2008. p. 1–14.
Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation; 2012. p. 2.
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Galakatos, A., Crotty, A., Kraska, T. (2018). Distributed Machine Learning. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80647
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