Topology-Aware Data Aggregation for Intensive I/O on Large-Scale Supercomputers

• Published in: 2016 First International Workshop on Communication Optimizations in HPC (COMHPC) pp. 73 - 81
• Main Authors: Tessier, Francois, Malakar, Preeti, Vishwanath, Venkatram, Jeannot, Emmanuel, Isaila, Florin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2016
• Subjects: Bandwidth; Computational modeling; Data aggregation; Network topology; Supercomputers; Topology; Writing
• DOI: 10.1109/COMHPC.2016.013

Reading and writing data efficiently from storage systems is critical for high performance data-centric applications. These I/O systems are being increasingly characterized by complex topologies and deeper memory hierarchies. Effective parallel I/O solutions are needed to scale applications on current and future supercomputers. Data aggregation is an efficient approach consisting of electing some processes in charge of aggregating data from a set of neighbors and writing the aggregated data into storage. Thus, the bandwidth use can be optimized while the contention is reduced. In this work, we take into account the network topology for mapping aggregators and we propose an optimized buffering system in order to reduce the aggregation cost. We validate our approach using micro-benchmarks and the I/O kernel of a large-scale cosmology simulation. We show improvements up to 15× faster for I/O operations compared to a standard implementation of MPI I/O.