activity
20032026
most citedFAIR for AI: An interdisciplinary and international community building perspective

92 citations · 685 across the 84 of their papers we have counts for

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Showing 2024 · cs.DCShow all

6 papers · 2 filters

cs.DC2024★ 1 cited

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…

cs.DC2024

Hydra: Brokering Cloud and HPC Resources to Support the Execution of Heterogeneous Workloads at Scale

Aymen Alsaadi, Shantenu Jha, Matteo Turilli

Scientific discovery increasingly depends on middleware that enables the execution of heterogeneous workflows on heterogeneous platforms One of the main challenges is to design sof…

cs.DC2024★ 2 cited

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…

cs.DC2024

AI-coupled HPC Workflow Applications, Middleware and Performance

Wes Brewer, Ana Gainaru, Frédéric Suter +3

AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-driven HPC workflows. This paper surveys the diverse and ra…

cs.DC2024

Design and Implementation of an Analysis Pipeline for Heterogeneous Data

Arup Kumar Sarker, Aymen Alsaadi, Niranda Perera +8

Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficult…

cs.DC2024

Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows

Ozgur Ozan Kilic, Tianle Wang, Matteo Turilli +4

Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challen…