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000902212 0247_ $$2doi$$a10.1109/BigData50022.2020.9378050
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000902212 037__ $$aFZJ-2021-04100
000902212 1001_ $$0P:(DE-Juel1)162390$$aGötz, Markus$$b0$$eCorresponding author
000902212 1112_ $$a2020 IEEE International Conference on Big Data (Big Data)$$cAtlanta$$d2020-12-10 - 2020-12-13$$wGA
000902212 245__ $$aHeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics
000902212 260__ $$bIEEE$$c2020
000902212 300__ $$a276-287
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000902212 520__ $$aTo cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.
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000902212 7001_ $$0P:(DE-HGF)0$$aDebus, Charlotte$$b1
000902212 7001_ $$0P:(DE-Juel1)177671$$aCoquelin, Daniel$$b2
000902212 7001_ $$0P:(DE-Juel1)129347$$aKrajsek, Kai$$b3
000902212 7001_ $$0P:(DE-Juel1)174573$$aComito, Claudia$$b4
000902212 7001_ $$0P:(DE-HGF)0$$aKnechtges, Philipp$$b5
000902212 7001_ $$0P:(DE-Juel1)132123$$aHagemeier, Bjorn$$b6
000902212 7001_ $$0P:(DE-Juel1)178977$$aTarnawa, Michael$$b7
000902212 7001_ $$0P:(DE-HGF)0$$aHanselmann, Simon$$b8
000902212 7001_ $$0P:(DE-HGF)0$$aSiggel, Martin$$b9
000902212 7001_ $$0P:(DE-HGF)0$$aBasermann, Achim$$b10
000902212 7001_ $$0P:(DE-HGF)0$$aStreit, Achim$$b11
000902212 773__ $$a10.1109/BigData50022.2020.9378050
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