17 citations · 40 across the 11 of their papers we have counts for
13 papers
Augur: Pre-Execution Energy Prediction for Workflow Tasks in Heterogeneous Clusters
Kathleen West, Vasilis Bountris, Philipp Thamm +3
Scientific workflows are widely used to process large quantities of data, leading to significant energy consumption and carbon emissions. To reduce this environmental impact, energ…
Nf-PEAK: Process-Based Energy Attribution for Nextflow Workflows on Kubernetes Clusters
Philipp Thamm, Somayeh Mohammadi, Kathleen West +3
Scientific workflows are pipelines of interdependent tasks. They are increasingly executed on shared Kubernetes clusters via workflow engines such as Nextflow. Their energy consump…
HyProv: Hybrid Provenance Management for Scientific Workflows
Vasilis Bountris, Lauritz Thamsen, Ulf Leser
Provenance plays a crucial role in scientific workflow execution, for instance by providing data for failure analysis, real-time monitoring, or statistics on resource utilization f…
A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows
Kathleen West, Youssef Moawad, Fabian Lehmann +4
Scientific workflows are critical to scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. As such, their execution…
Strategies to Measure Energy Consumption Using RAPL During Workflow Execution on Commodity Clusters
Philipp Thamm, Ulf Leser
In science, problems in many fields can be solved by processing datasets using a series of computationally expensive algorithms, sometimes referred to as workflows. Traditionally,…
WOW: Workflow-Aware Data Movement and Task Scheduling for Dynamic Scientific Workflows
Fabian Lehmann, Jonathan Bader, Friedrich Tschirpke +6
Scientific workflows process extensive data sets over clusters of independent nodes, which requires a complex stack of infrastructure components, especially a resource manager (RM)…
Exploring the Potential of Carbon-Aware Execution for Scientific Workflows
Kathleen West, Fabian Lehmann, Vasilis Bountris +3
Scientific workflows are widely used to automate scientific data analysis and often involve processing large quantities of data on compute clusters. As such, their execution tends…
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…
Ponder: Online Prediction of Task Memory Requirements for Scientific Workflows
Fabian Lehmann, Jonathan Bader, Ninon De Mecquenem +5
Scientific workflows are used to analyze large amounts of data. These workflows comprise numerous tasks, many of which are executed repeatedly, running the same custom program on d…
Scaling on Frontier: Uncertainty Quantification Workflow Applications using ExaWorks to Enable Full System Utilization
Mikhail Titov, Robert Carson, Matthew Rolchigo +6
When running at scale, modern scientific workflows require middleware to handle allocated resources, distribute computing payloads and guarantee a resilient execution. While indivi…
The Common Workflow Scheduler Interface: Status Quo and Future Plans
Fabian Lehmann, Jonathan Bader, Lauritz Thamsen +1
Nowadays, many scientific workflows from different domains, such as Remote Sensing, Astronomy, and Bioinformatics, are executed on large computing infrastructures managed by resour…
Large Language Models to the Rescue: Reducing the Complexity in Scientific Workflow Development Using ChatGPT
Mario Sänger, Ninon De Mecquenem, Katarzyna Ewa Lewińska +4
Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability,…
Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures
Jonathan Bader, Fabian Lehmann, Lauritz Thamsen +2
Many resource management techniques for task scheduling, energy and carbon efficiency, and cost optimization in workflows rely on a-priori task runtime knowledge. Building runtime…