Towards a collective layer in the big data stack. Gunarathne, T., Qiu, J., & Gannon, D. In Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on, pages 236-245, 2014. IEEE. doi abstract bibtex We generalize MapReduce, Iterative MapReduce and data intensive MPI runtime as a layered Map-Collective architecture with Map-All Gather, Map-All Reduce, MapReduce Merge Broadcast and Map-Reduce Scatter patterns as the initial focus. Map-collectives improve the performance and efficiency of the computations while at the same time facilitating ease of use for the users. These collective primitives can be applied to multiple runtimes and we propose building high performance robust implementations that cross cluster and cloud systems. Here we present results for two collectives shared between Hadoop (where we term our extension H-Collectives) on clusters and the Twister4Azure Iterative MapReduce for the Azure Cloud. Our prototype implementations of Map-All Gather and Map-All Reduce primitives achieved up to 33% performance improvement for K-means Clustering and up to 50% improvement for Multi-Dimensional Scaling, while also improving the user friendliness. In some cases, use of Map-collectives virtually eliminated almost all the overheads of the computations. © 2014 IEEE.
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abstract = {We generalize MapReduce, Iterative MapReduce and data intensive MPI runtime as a layered Map-Collective architecture with Map-All Gather, Map-All Reduce, MapReduce Merge Broadcast and Map-Reduce Scatter patterns as the initial focus. Map-collectives improve the performance and efficiency of the computations while at the same time facilitating ease of use for the users. These collective primitives can be applied to multiple runtimes and we propose building high performance robust implementations that cross cluster and cloud systems. Here we present results for two collectives shared between Hadoop (where we term our extension H-Collectives) on clusters and the Twister4Azure Iterative MapReduce for the Azure Cloud. Our prototype implementations of Map-All Gather and Map-All Reduce primitives achieved up to 33% performance improvement for K-means Clustering and up to 50% improvement for Multi-Dimensional Scaling, while also improving the user friendliness. In some cases, use of Map-collectives virtually eliminated almost all the overheads of the computations. © 2014 IEEE.},
bibtype = {inproceedings},
author = {Gunarathne, Thilina and Qiu, Judy and Gannon, Dennis},
doi = {10.1109/CCGrid.2014.123},
booktitle = {Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on}
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