d2o: a distributed data object for parallel high-performance computing in Python

• Published in: Journal of big data Vol. 3; no. 1; pp. 1 - 34
• Main Authors: Steininger, Theo, Greiner, Maksim, Beaujean, Frederik, Enßlin, Torsten
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 15.09.2016; Springer Nature B.V
• Subjects: Big Data; Clusters; Communications Engineering; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; High performance computing; Information Storage and Retrieval; Mathematical Applications in Computer Science; Methodology; Networks; Open source software; Source code; Open source; Parallelization; Numpy; MPI; Numerics; Python
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-016-0052-5

We introduce d2o , a Python module for cluster-distributed multi-dimensional numerical arrays. It acts as a layer of abstraction between the algorithm code and the data-distribution logic. The main goal is to achieve usability without losing numerical performance and scalability. d2o ’s global interface is similar to the one of a numpy.ndarray, whereas the cluster node’s local data is directly accessible for use in customized high-performance modules. d2o is written in pure Python which makes it portable and easy to use and modify. Expensive operations are carried out by dedicated external libraries like numpy and mpi4py . The performance of d2o is on a par with numpy for serial applications and scales well when moving to an MPI cluster. d2o is open-source software available under the GNU General Public License v3 (GPL-3) at https://gitlab.mpcdf.mpg.de/ift/D2O .