The Efficiency of MapReduce in Parallel External Memory
Since its introduction in 2004, the MapReduce framework has become one of the standard approaches in massive distributed and parallel computation. In contrast to its intensive use in practise, theoretical footing is still limited and only little work has been done yet to put MapReduce on a par with the major computational models. Following pioneer work that relates the MapReduce framework with PRAM and BSP in their macroscopic structure, we focus on the functionality provided by the framework itself, considered in the parallel external memory model (PEM). In this, we present upper and lower bounds on the parallel I/O-complexity that are matching up to constant factors for the shuffle step. The shuffle step is the single communication phase where all information of one MapReduce invocation gets transferred from map workers to reduce workers. Hence, we move the focus towards the internal communication step in contrast to previous work. The results we obtain further carry over to the BSP* model. On the one hand, this shows how much complexity can be “hidden” for an algorithm expressed in MapReduce compared to PEM. On the other hand, our results bound the worst-case performance loss of the MapReduce approach in terms of I/O-efficiency.
Unable to display preview. Download preview PDF.
- 2.Arge, L., Goodrich, M.T., Nelson, M., Sitchinava, N.: Fundamental parallel algorithms for private-cache chip multiprocessors. In: Proceedings of SPAA 2008, pp. 197–206. ACM (2008)Google Scholar
- 5.Brodal, G.S., Fagerberg, R.: On the limits of cache-obliviousness. In: Proceedings of STOC 2003, pp. 307–315. ACM, New York (2003) ISBN:1-58113-674-9Google Scholar
- 6.Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings OSDI 2004, pp. 137–150 (2004)Google Scholar
- 9.Goodrich, M.T., Sitchinava, N., Zhang, Q.: Sorting, searching, and simulation in the mapreduce framework. CoRR, abs/1101.1902 (2011)Google Scholar
- 11.Karloff, H., Suri, S., Vassilvitskii, S.: A model of computation for MapReduce. In: Proceedings of SODA 2010, pp. 938–948. SIAM (2010)Google Scholar
- 12.Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S., Stonebraker, M.: A comparison of approaches to large-scale data analysis. In: Proceedings of SIGMOD 2009, pp. 165–178. ACM (2009)Google Scholar
- 13.Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyrakis, C.: Evaluating mapreduce for multi-core and multiprocessor systems. In: Proceedings of HPCA 2007, pp. 13–24. IEEE (February 2007)Google Scholar
- 16.White, T.: Hadoop: The Definitive Guide, 1st edn. O’Reilly (June 2009)Google Scholar