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WORKS@SC 2019: Denver, CO, USA

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2019 IEEE/ACM Workflows in Support of Large-Scale Science, WORKS@SC 2019, Denver, CO, USA, November 17, 2019. IEEE 2019, ISBN 978-1-7281-5997-3

Technical Papers

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  - conf/sc/SouzaVMCNALSTBC19
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Renan Souza, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco Aurélio Stelmar Netto, Leonardo Azevedo, Vítor Lourenço, Elton F. de Souza Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Márcio Ferreira Moreno:
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. 1-10
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  - conf/sc/MehtaFKAWLSCTYM19
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Kshitij Mehta, Ian T. Foster, Scott Klasky, Bryce Allen, Matthew Wolf, Jeremy Logan, Eric Suchyta, Jong Choi, Keichi Takahashi, Igor Yakushin, Todd S. Munson:
A Codesign Framework for Online Data Analysis and Reduction. 11-20
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  - conf/sc/TschuterHWK019
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Ronny Tschüter, Christian Herold, William R. Williams, Maximilian Knespel, Matthias Weber:
A Top-Down Performance Analysis Methodology for Workflows: Tracking Performance Issues from Overview to Individual Operations. 21-30
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  - conf/sc/PatkiFBNGIR19
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Tapasya Patki, Zachary Frye, Harsh Bhatia, Francesco Di Natale, James N. Glosli, Helgi I. Ingólfsson, Barry Rountree:
Comparing GPU Power and Frequency Capping: A Case Study with the MuMMI Workflow. 31-39
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Mathieu Dugré, Valérie Hayot-Sasson, Tristan Glatard:
A Performance Comparison of Dask and Apache Spark for Data-Intensive Neuroimaging Pipelines. 40-49
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Xinzheng Niu, Mideng Qian, Chase Q. Wu, Aiqin Hou:
On a Parallel Spark Workflow for Frequent Itemset Mining Based on Array Prefix-Tree. 50-59

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