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10 results on '"Lüttgau, Jakob"'

1. Workflows Community Summit 2022: A Roadmap Revolution

Author

da Silva, Rafael Ferreira, Badia, Rosa M., Bala, Venkat, Bard, Debbie, Bremer, Peer-Timo, Buckley, Ian, Caino-Lores, Silvina, Chard, Kyle, Goble, Carole, Jha, Shantenu, Katz, Daniel S., Laney, Daniel, Parashar, Manish, Suter, Frederic, Tyler, Nick, Uram, Thomas, Altintas, Ilkay, Andersson, Stefan, Arndt, William, Aznar, Juan, Bader, Jonathan, Balis, Bartosz, Blanton, Chris, Braghetto, Kelly Rosa, Brodutch, Aharon, Brunk, Paul, Casanova, Henri, Lierta, Alba Cervera, Chigu, Justin, Coleman, Taina, Collier, Nick, Colonnelli, Iacopo, Coppens, Frederik, Crusoe, Michael, Cunningham, Will, Kinoshita, Bruno de Paula, Di Tommaso, Paolo, Doutriaux, Charles, Downton, Matthew, Elwasif, Wael, Enders, Bjoern, Erdmann, Chris, Fahringer, Thomas, Figueiredo, Ludmilla, Filgueira, Rosa, Foltin, Martin, Fouilloux, Anne, Gadelha, Luiz, Gallo, Andy, Saez, Artur Garcia, Garijo, Daniel, Gerlach, Roman, Grant, Ryan, Grayson, Samuel, Grubel, Patricia, Gustafsson, Johan, Hayot-Sasson, Valerie, Hernandez, Oscar, Hilbrich, Marcus, Justine, AnnMary, Laflotte, Ian, Lehmann, Fabian, Luckow, Andre, Luettgau, Jakob, Maheshwari, Ketan, Matsuda, Motohiko, Medic, Doriana, Mendygral, Pete, Michalewicz, Marek, Nonaka, Jorji, Pawlik, Maciej, Pottier, Loic, Pouchard, Line, Putz, Mathias, Radha, Santosh Kumar, Ramakrishnan, Lavanya, Ristov, Sashko, Romano, Paul, Rosendo, Daniel, Ruefenacht, Martin, Rycerz, Katarzyna, Saurabh, Nishant, Savchenko, Volodymyr, Schulz, Martin, Simpson, Christine, Sirvent, Raul, Skluzacek, Tyler, Soiland-Reyes, Stian, Souza, Renan, Sukumar, Sreenivas Rangan, Sun, Ziheng, Sussman, Alan, Thain, Douglas, Titov, Mikhail, Tovar, Benjamin, Tripathy, Aalap, Turilli, Matteo, Tuznik, Bartosz, van Dam, Hubertus, Vivas, Aurelio, Ward, Logan, Widener, Patrick, Wilkinson, Sean, Zawalska, Justyna, and Zulfiqar, Mahnoor

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enabling the publication of workflows and their associated products according to the FAIR principles. This document reports on discussions and findings from the 2022 international edition of the Workflows Community Summit that took place on November 29 and 30, 2022.

Published
2023
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2. Development of Large-Scale Scientific Cyberinfrastructure and the Growing Opportunity to Democratize Access to Platforms and Data

Author

Luettgau, Jakob, Scorzelli, Giorgio, Pascucci, Valerio, Taufer, Michela, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Streitz, Norbert A., editor, and Konomi, Shin'ichi, editor

Published
2023
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3. Cost and Performance Modeling for Earth System Data Management and Beyond

Author

Lüttgau, Jakob, Kunkel, Julian, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Yokota, Rio, editor, Weiland, Michèle, editor, Shalf, John, editor, and Alam, Sadaf, editor

Published
2018
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4. Thicket: Seeing the Performance Experiment Forest for the Individual Run Trees

Author

Brink, Stephanie, primary, McKinsey, Michael, additional, Boehme, David, additional, Scully-Allison, Connor, additional, Lumsden, Ian, additional, Hawkins, Daryl, additional, Burgess, Treece, additional, Lama, Vanessa, additional, Lüttgau, Jakob, additional, Isaacs, Katherine E., additional, Taufer, Michela, additional, and Pearce, Olga, additional

Published
2023
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5. Simulation of Hierarchical Storage Systems for TCO and QoS

Author

Luettgau, Jakob, Kunkel, Julian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kunkel, Julian M., editor, Yokota, Rio, editor, Taufer, Michela, editor, and Shalf, John, editor

Published
2017
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6. The SIOX Architecture – Coupling Automatic Monitoring and Optimization of Parallel I/O

Author

Kunkel, Julian M., Zimmer, Michaela, Hübbe, Nathanael, Aguilera, Alvaro, Mickler, Holger, Wang, Xuan, Chut, Andriy, Bönisch, Thomas, Lüttgau, Jakob, Michel, Roman, Weging, Johann, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Kunkel, Julian Martin, editor, Ludwig, Thomas, editor, and Meuer, Hans Werner, editor

Published
2014
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8. To the brave scientists: Aren't we strong enough to stand (and profit from) uncertainty in Earth system measurement and modelling?

Author

Paasche, Hendrik, Gross, Matthias, Lüttgau, Jakob, Greenberg, David S., and Weigel, Tobias

Subjects
EARTH scientists, PREDICTION models, PROBLEM solving
Abstract

The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty. We think that Earth scientists are generally aware of the importance of linking data to quantitative uncertainty measures. But we also think that uncertainty quantification of Earth observation data too often fails at very early stages. We claim that data acquisition without uncertainty quantification is not sustainable and machine learning and computational modelling cannot unfold their potential when analysing complex natural systems like the Earth. Current approaches such as stochastic perturbation of parameters or initial conditions cannot quantify uncertainty or bias arising from the choice of model, limiting scientific progress. We need incentives stimulating the honest treatment of uncertainty starting during data acquisition, continuing through analysis methodology and prediction results. Computational modellers and machine learning experts have a critical role, since they enjoy high esteem from stakeholders and their methodologies and their results critically depend on data uncertainty. If both want to advance their uncertainty assessment of models and predictions of complex systems like the Earth, they have a common problem to solve. Together, computational modellers and machine learners could develop new strategies for bias identification and uncertainty quantification offering a more all‐embracing uncertainty quantification than any known methodology. But since it starts for computational modellers and machine learners with data and their uncertainty, the fundamental first step in such a development would be leveraging shareholder esteem to insistently advocate for reduction of ignorance when it comes to uncertainty quantification of data. [ABSTRACT FROM AUTHOR]

Published
2022
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