Abstract
Today’s data-intensive applications require access to multiple types of storage platforms, such as parallel file systems, distributed file systems, and in-memory data systems. In addition, many applications are demanding the processing of data streams. The goal is to develop mechanisms to integrate and hide the diversity of data sources from applications and improve data access performance.
In this work, we propose the implementation of a data container-based solution for data-intensive applications, which provides a high-level programming interface to different storage systems, commonly used in both HPC and HPDA environments. Our approach, DICE, hides the complexity of dealing with data from multiple sources, reducing the effort to access items transparently to end users and developers. The abstraction is based on a series of plugins that facilitate extension to other existing file systems.
This work was supported by the EU project “ASPIDE: Exascale Programming Models for Extreme Data Processing” under grant 801091. This research was partially supported by Madrid regional Government (Spain) under the grant “Convergencia Big Data-HPC: de los sensores a las Aplicaciones. (CABAHLA-CM)”. Finally, this work was partially supported by the Spanish Ministry of Science and Innovation Project “New Data Intensive Computing Methods for High-End and Edge Computing Platforms (DECIDE)” Ref. PID2019-107858GB-I00.
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Source code and examples available at https://gitlab.arcos.inf.uc3m.es/pbrox/dice.git.
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Brox, P., Garcia-Blas, J., Singh, D.E., Carretero, J. (2022). DICE: Generic Data Abstraction for Enhancing the Convergence of HPC and Big Data. In: Gitler, I., Barrios Hernández, C.J., Meneses, E. (eds) High Performance Computing. CARLA 2021. Communications in Computer and Information Science, vol 1540. Springer, Cham. https://doi.org/10.1007/978-3-031-04209-6_8
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