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View article: uw-mad-dash/PGT_Index: SC_25_AD_AE_v1.0
uw-mad-dash/PGT_Index: SC_25_AD_AE_v1.0 Open
Code associated with the 2025 SC paper "PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training"
View article: Automatic Metadata Capture and Processing for High-Performance Workflows
Automatic Metadata Capture and Processing for High-Performance Workflows Open
Modern workflows run on increasingly heterogeneous computing architectures and with this heterogeneity comes additional complexity. We aim to apply the FAIR principles for research reproducibility by developing software to collect metadata…
View article: Scalable GPU Performance Variability Analysis framework
Scalable GPU Performance Variability Analysis framework Open
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insight and hinder the integration of performance analytics into …
View article: An Ecosystem of Services for FAIR Computational Workflows
An Ecosystem of Services for FAIR Computational Workflows Open
Computational workflows, regardless of their portability or maturity, represent major investments of both effort and expertise. They are first class, publishable research objects in their own right. They are key to sharing methodological k…
View article: Performance analysis and data reduction for exascale scientific workflows
Performance analysis and data reduction for exascale scientific workflows Open
Chimbuko is the first in situ, scalable, workflow-level performance analysis tool for trace-level analysis and visualization of application performance. This tool was developed by the Co-design Center for Online Data Analysis and Reduction…
View article: An Overview of Decentralized Web Technologies as a Foundation for Future IPFS-Centric FDOs
An Overview of Decentralized Web Technologies as a Foundation for Future IPFS-Centric FDOs Open
dPIDs are an emerging PID technology based on decentralized architectures and self-sovereign identity [1]. dPIDs are PID containers, forming persistent storage systems where each object is identified by a unique PID. dPIDs are immune to co…
View article: Applying the FAIR Principles to computational workflows
Applying the FAIR Principles to computational workflows Open
Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As…
View article: Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows
Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows Open
Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challenging. Furthermore, their complexity and heterogeneity make …
View article: Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP
Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP Open
-Scientific computing communities increasingly run their experiments using complex data- and compute-intensive workflows that utilize distributed and heterogeneous architectures targeting numerical simulations and machine learning, often e…
View article: Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows
Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows Open
This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded sc…
View article: FAIR Enabling Re-Use of Data-Intensive Workflows and Scientific Reproducibility
FAIR Enabling Re-Use of Data-Intensive Workflows and Scientific Reproducibility Open
Keynote presentation at ICPE 23, First Practical FAIR workshop, Coimbra, Portugal, April 15, 2023.
View article: Workflows Community Summit 2022: A Roadmap Revolution
Workflows Community Summit 2022: A Roadmap Revolution Open
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-…
View article: Workflows Community Summit 2022: A Roadmap Revolution
Workflows Community Summit 2022: A Roadmap Revolution Open
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 the execution of a cl…
View article: Earthquake Aftershocks Pattern Prediction
Earthquake Aftershocks Pattern Prediction Open
Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars…
View article: Development of Big Seismic Data Processing Tools
Development of Big Seismic Data Processing Tools Open
Seismology is a data-driven science with a huge amount of data gathered for over a century. Though seismic data recording started in 1900, the growth of seismic data has obviously been exponentially in the last three decades. This data gro…
View article: Earthquake Aftershocks Pattern Prediction
Earthquake Aftershocks Pattern Prediction Open
Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars…
View article: A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows Open
The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is driven by numerous factors. An uncertainty-aware metric tha…
View article: A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows Open
The capability to replicate the predictions by machine learning (ML) or artificial intelligence (AI) models and the results in scientific workflows that incorporate such ML/AI predictions is driven by a variety of factors.
View article: Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows
Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows Open
Funding: This work is partly funded by NSF award OAC-1839900. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. libEnsemble was developed as part of the…
View article: Challenges for Implementing FAIR Digital Objects with High Performance Workflows
Challenges for Implementing FAIR Digital Objects with High Performance Workflows Open
New types of workflows are being used in science that couple traditional distributed and high-performance computing (HPC) with data-intensive approaches, and orchestrate ensembles of numerical simulations and artificial intelligence (AI) m…
View article: Figure Descriptive Text Extraction using Ontological Representation
Figure Descriptive Text Extraction using Ontological Representation Open
Experimental research publications provide figure form resources including graphs, charts, and any type of images to effectively support and convey methods and results. To describe figures, authors add captions, which are often incomplete,…
View article: Reproducibility Practice in High-Performance Computing: Community Survey Results
Reproducibility Practice in High-Performance Computing: Community Survey Results Open
The integrity of science and engineering research is grounded in assumptions of rigor and transparency on the part of those engaging in such research. HPC community effort to strengthen rigor and transparency take the form of reproducibili…
View article: Reproducibility Practice in High Performance Computing: Community Survey Results
Reproducibility Practice in High Performance Computing: Community Survey Results Open
The integrity of science and engineering research is grounded in assumptions of rigor and transparency on the part of those engaging in such research. HPC community effort to strengthen rigor and transparency take the form of reproducibili…
View article: SC Transparency and Reproducibilty Community Survey
SC Transparency and Reproducibilty Community Survey Open
Results of a survey administered to the SC conference community in August 2020 to all those who had participated in SC17, SC18, or SC19 technical programs. The survey participants were self-selected among 9,949 unique individuals. 204 indi…