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.
@article{arxiv.1810.04146,
title = {Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks},
author = {Sergio Rivas-Gomez and Sai Narasimhamurthy and Keeran Brabazon and Oliver Perks and Erwin Laure and Stefano Markidis},
journal= {arXiv preprint arXiv:1810.04146},
year = {2018}
}