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WORKS@SC 2019: Denver, CO, USA
- 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
- 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 - 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 - Ronny Tschüter, Christian Herold, William Williams
, Maximilian Knespel, Matthias Weber:
A Top-Down Performance Analysis Methodology for Workflows: Tracking Performance Issues from Overview to Individual Operations. 21-30 - 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 - Mathieu Dugré, Valérie Hayot-Sasson, Tristan Glatard:
A Performance Comparison of Dask and Apache Spark for Data-Intensive Neuroimaging Pipelines. 40-49 - 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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