Productivity, Portability, Performance: Data-Centric Python

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Summary

This work presents a workflow that retains Python's high productivity while achieving portable performance across different architectures and includes HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation.

Type
preprint
Published
2018-04-16
Cited by
122
References
1,820
Access
Open access

Keywords

Computer science, SPARK (programming language), Deep learning, Big data, Pipeline (software)

References

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