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arXiv · 1810.04146

Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks

Sergio Rivas-Gomez·Sai Narasimhamurthy·Keeran Brabazon·Oliver Perks·Erwin Laure·Stefano Markidis
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Abstract

In this work, we consider the integration of MPI one-sided communication and non-blocking I/O in HPC-centric MapReduce frameworks. Using a decoupled strategy, we aim to overlap the Map and Reduce phases of the algorithm by allowing processes to communicate and synchronize using solely one-sided operations. Hence, we effectively increase the performance in situations where the workload per process is unexpectedly unbalanced. Using a Word-Count implementation and a large dataset from the Purdue MapReduce Benchmarks Suite (PUMA), we demonstrate that our approach can provide up to 23% performance improvement on average compared to a reference MapReduce implementation that uses state-of-the-art MPI collective communication and I/O.

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Sergio Rivas-Gomez, Sai Narasimhamurthy, Keeran Brabazon, Oliver Perks, Erwin Laure, Stefano Markidis. 2018-10-09. Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks. https://arxiv.org/abs/1810.04146

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