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The increasing availability of cloud computing services for science has changed the way scientific code can be developed, deployed, and run. Many modern scientific workflows are capable of running on cloud computing resources. Consequently,…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-06-11 Peter Vaillancourt , Bennett Wineholt , Brandon Barker , Plato Deliyannis , Jackie Zheng , Akshay Suresh , Adam Brazier , Rich Knepper , Rich Wolski

Cloud platforms allow users to execute tasks directly from their web browser and are a key enabling technology not only for commerce but also for computational science. Research software is often developed by scientists with limited…

As Large Language Models (LLMs) become ubiquitous across various scientific domains, their lack of ability to perform complex tasks like running simulations or to make complex decisions limits their utility. LLM-based agents bridge this gap…

Computation and Language · Computer Science 2026-01-21 Anurag Acharya , Timothy Vega , Rizwan A. Ashraf , Anshu Sharma , Derek Parker , Robert Rallo

Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-06-15 Rosa M Badia , Jorge Ejarque , Francesc Lordan , Daniele Lezzi , Javier Conejero , Javier Álvarez Cid-Fuentes , Yolanda Becerra , Anna Queralt

Cloud platforms are increasingly being used to run HPC workloads. Major cloud providers offer a wide variety of virtual machine (VM) types, enabling users to find the optimal balance between performance and cost. However, this extensive…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-11-26 Marco A. S. Netto

Cloud computing provides a great opportunity for scientists, as it enables large-scale experiments that cannot are too long to run on local desktop machines. Cloud-based computations can be highly parallel, long running and data-intensive,…

Software Engineering · Computer Science 2016-12-07 Maria Spichkova , Heinz W. Schmidt , Ian E. Thomas , Iman I. Yusuf , Steve Androulakis , Grischa R. Meyer

Scientific computing applications usually need huge amounts of computational power. The cloud provides interesting high-performance computing solutions, with its promise of virtually infinite resources on demand. However, migrating…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-11-26 Satish Narayana Srirama , Pelle Jakovits , Vladislav Ivaništšev

Scientific workflow is a powerful tool to streamline and organize computational steps of scientific application. This paper presents Emerald, a system that adds sophisticated cloud offloading capabilities to scientific workflows. Emerald…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-10-05 Hao Qian

Scientific computing applications have benefited greatly from high performance computing infrastructure such as supercomputers. However, we are seeing a paradigm shift in the computational structure, design, and requirements of these…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-11-15 Prateek Sharma , Vikram Jadhao

We investigate the feasibility of high performance scientific computation using cloud computers as an alternative to traditional computational tools. The availability of these large, virtualized pools of compute resources raises the…

Materials Science · Physics 2009-01-05 J. J. Rehr , J. P. Gardner , M. Prange , L. Svec , F. Vila

In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable computing resources and automated workflow management on…

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…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-04-04 Rafael Ferreira da Silva , Rosa M. Badia , Venkat Bala , Debbie Bard , Peer-Timo Bremer , Ian Buckley , Silvina Caino-Lores , Kyle Chard , Carole Goble , Shantenu Jha , Daniel S. Katz , Daniel Laney , Manish Parashar , Frederic Suter , Nick Tyler , Thomas Uram , Ilkay Altintas , Stefan Andersson , William Arndt , Juan Aznar , Jonathan Bader , Bartosz Balis , Chris Blanton , Kelly Rosa Braghetto , Aharon Brodutch , Paul Brunk , Henri Casanova , Alba Cervera Lierta , Justin Chigu , Taina Coleman , Nick Collier , Iacopo Colonnelli , Frederik Coppens , Michael Crusoe , Will Cunningham , Bruno de Paula Kinoshita , Paolo Di Tommaso , Charles Doutriaux , Matthew Downton , Wael Elwasif , Bjoern Enders , Chris Erdmann , Thomas Fahringer , Ludmilla Figueiredo , Rosa Filgueira , Martin Foltin , Anne Fouilloux , Luiz Gadelha , Andy Gallo , Artur Garcia Saez , Daniel Garijo , Roman Gerlach , Ryan Grant , Samuel Grayson , Patricia Grubel , Johan Gustafsson , Valerie Hayot-Sasson , Oscar Hernandez , Marcus Hilbrich , AnnMary Justine , Ian Laflotte , Fabian Lehmann , Andre Luckow , Jakob Luettgau , Ketan Maheshwari , Motohiko Matsuda , Doriana Medic , Pete Mendygral , Marek Michalewicz , Jorji Nonaka , Maciej Pawlik , Loic Pottier , Line Pouchard , Mathias Putz , Santosh Kumar Radha , Lavanya Ramakrishnan , Sashko Ristov , Paul Romano , Daniel Rosendo , Martin Ruefenacht , Katarzyna Rycerz , Nishant Saurabh , Volodymyr Savchenko , Martin Schulz , Christine Simpson , Raul Sirvent , Tyler Skluzacek , Stian Soiland-Reyes , Renan Souza , Sreenivas Rangan Sukumar , Ziheng Sun , Alan Sussman , Douglas Thain , Mikhail Titov , Benjamin Tovar , Aalap Tripathy , Matteo Turilli , Bartosz Tuznik , Hubertus van Dam , Aurelio Vivas , Logan Ward , Patrick Widener , Sean Wilkinson , Justyna Zawalska , Mahnoor Zulfiqar

Several scientific and industry applications require High Performance Computing (HPC) resources to process and/or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-07-21 Kiran Mantripragada , Leonardo P. Tizzei , Alecio P. D. Binotto , Marco A. S. Netto

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-11-15 Christian Vecchiola , Suraj Pandey , Rajkumar Buyya

In a new effort to make our research transparent and reproducible by others, we developed a workflow to run and share computational studies on the public cloud Microsoft Azure. It uses Docker containers to create an image of the application…

Computational Engineering, Finance, and Science · Computer Science 2020-07-24 Olivier Mesnard , Lorena A. Barba

Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by research infrastructures, NRENs, and commercial cloud providers,…

In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-11-01 Gregor von Laszewski , J. P. Fleischer , Geoffrey C. Fox

The availability of quantum hardware via the cloud offers opportunities for new approaches to computing optimization problems in an industrial environment. However, selecting the right quantum hardware is difficult for non-experts due to…

Quantum Physics · Physics 2025-09-17 Djamel Laps-Bouraba , Markus Zajac , Uta Störl

Cloud computing offers the potential to help scientists to process massive number of computing resources often required in machine learning application such as computer vision problems. This proposal would like to show that which benefits…

Computer Vision and Pattern Recognition · Computer Science 2013-02-07 Yu Zhou
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