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

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 . [ABSTRACT FROM AUTHOR]

Copyright of Journal of Big Data is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)