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Presentation . 2020
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CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling

descriptionPublicationkeyboard_double_arrow_right Presentation , Other literature type 26 Mar 2020 English Publisher:Zenodo
Authors: Ehrig, Simeon; Huebl, Axel;

doi: 10.5281/zenodo.3713753 , 10.5281/zenodo.3713752

CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling

Abstract

Jupyter Notebooks are omnipresent in the modern scientist's and engineer's toolbox just as CUDA C++ is in accelerated computing. We present the first implementation of a CUDA C++ enabled read-eval-print-loop (REPL) that allows to interactively "script" the popular CUDA C++ runtime syntax in Notebooks. With our novel implementation, based on CERN's C++ interpreter Cling, the modern CUDA C++ developer can work as interactively and productively as (I)Python developers while keeping all the benefits of the vast C++ computing and library ecosystem coupled with first-class performance.

Keywords

Cling, GPU, Interactive Computing, JIT, Programming Languages, CUDA, Compilers & Tools, Compilers & Tools, Jupyter, C++

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selected citations
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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).
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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.
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