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Presentation . 2026
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2026
License: CC BY
Data sources: Datacite
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DDC, a Performance Portable Library Abstracting Computation on Discrete Domains

Authors: Morvany, Trévis; Padioleau, Thomas; The CExA team;

DDC, a Performance Portable Library Abstracting Computation on Discrete Domains

Abstract

The Discrete Domain Computation (DDC) library is a modern C++ library that aims to offer to the C++ world an equivalent to the xarray.DataArray Python environment. The Xarray library introduces labeled multidimensional arrays, enabling more intuitive data manipulation by associating dimensions with user-provided names rather than relying on positional indexing. This approach simplifies indexing, slicing, and broadcasting while reducing common indexing errors. Inspired by these ideas, DDC extends the Kokkos library by providing zero-overhead dimension labeling for multidimensional arrays along with performance-portable multidimensional algorithms. This labeling mechanism enables compile-time detection of indexing and slicing errors, ensuring safer and more expressive array operations in C++. In this presentation, we will introduce the core concepts of DDC and demonstrate its usage through a simple example that highlights some of its key features.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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