D. Elia, S. Fiore, A. D’Anca, C. Palazzo, I. Foster, D. N. Williams, G. Aloisio
In: ACM International Conference on Computing Frontiers (CF’ 16), May 16-19, 2016, Como, Italy
This work presents the I/O in-memory server implemented in the context of the Ophidia framework, a big data analytics stack addressing scientific data analysis of n-dimensional datasets. The provided I/O server represents a key component in the Ophidia 2.0 architecture proposed in this paper. It exploits (i) a NoSQL approach to manage scientific data at the storage level, (ii) user-defined functions to perform array-based analytics, (iii) the Ophidia Storage API to manage heterogeneous back-ends through a plugin-based approach, and (iv) an in-memory and parallel analytics engine to address high scalability and performance. Preliminary performance results about a statistical analytics kernel benchmark performed on a HPC cluster running at the CMCC SuperComputing Centre are provided in this paper.