Enabling Interactive Supercomputing at JSC Lessons Learned

Göbbert, Jens Henrik; Kreuzer, Tim; Grosch, Alice; Lintermann, Andreas; Riedel, Morris

doi:10.1007/978-3-030-02465-9_48

Cited by 9 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also to mention is the usefulness of the C++ ODE-solver "ODEINT" [61], and support provided by Andreas Winter concerning the plotting software QtGrace. Credit also goes to the team that provides Jupyter-JSC as a dedicated interface to the supercomputers at JSC, which served well for part of the workflow and data analysis [62]. Computations were mostly conducted using the workflow environment JUBE [63].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Quasi-particle functional renormalisation group calculations in the two-dimensional t-t'-Hubbard model

Rohe

2023

SciPost Phys.

View full text Add to dashboard Cite

We extend and apply a recently introduced quasi-particle functional renormalisation group scheme to the two-dimensional Hubbard model with next-nearest-neighbour hopping and away from half filling. We confirm the generation of superconducting correlations in some regions of the phase diagram, but also find that the inclusion of self-energy feedback by means of a decreasing quasi-particle weight can suppress superconducting tendencies more than anti-ferromagnetic correlations by which they are generated. As a supplement, we provide sample results for the self-energy in second-order perturbation theory and address some conceptual matters.

show abstract

Section: Acknowledgmentsmentioning

confidence: 99%

Quasi-particle functional renormalisation group calculations in the two-dimensional t-t'-Hubbard model

Rohe

2023

SciPost Phys.

View full text Add to dashboard Cite

show abstract

“…The emerging pattern of data-oriented research is to use in-situ analysis and visualisation on the HPC system. This enables researchers to act quickly based on outputs and apply modification and restart their workflows [10]. Jupyter notebook is a tool that researchers increasingly rely on [28].…”

Section: Notebook and Gitmentioning

confidence: 99%

“…Our suggested CWFR is built around the JupyterHub as a platform that glues notebooks, FDO and RO together. Jülich Supercomputing Centre (JSC) implemented an instance of JupyterHub called Jupyter-JSC  that provides a suitable platform for our proposed framework [10]. Jupyter-JSC has access to a wide range of data storage, to CPU and GPU on the JUWELS [37] and other HPC systems, and provides an easily accessible integration with git.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

“…CWFR relies on identifying recurring patterns across disciplines and breaking down workflows into smaller modular components that can be reused and reassembled for other use cases. In this paper, we suggest to use Jupyter notebooks on JuypterHub with connection to an HPC system [10] as a platform to develop a concept for bringing the components of the CWFR together for the application of Machine Learning (ML) in Earth System Sciences (ESS). To develop a functioning CWFR, identifying the challenges and practices of that particular community is essential.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

et al. 2022

View full text Add to dashboard Cite

Machine learning (ML) applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing (HPC) power are paving the way. Ensuring FAIR data and reproducible ML practices are significant challenges for Earth system researchers. Even though the FAIR principle is well known to many scientists, research communities are slow to adopt them. Canonical Workflow Framework for Research (CWFR) provides a platform to ensure the FAIRness and reproducibility of these practices without overwhelming researchers. This conceptual paper envisions a holistic CWFR approach towards ML applications in weather and climate, focusing on HPC and big data. Specifically, we discuss Fair Digital Object (FDO) and Research Object (RO) in the DeepRain project to achieve granular reproducibility. DeepRain is a project that aims to improve precipitation forecast in Germany by using ML. Our concept envisages the raster datacube to provide data harmonization and fast and scalable data access. We suggest the Juypter notebook as a single reproducible experiment. In addition, we envision JuypterHub as a scalable and distributed central platform that connects all these elements and the HPC resources to the researchers via an easy-to-use graphical interface.

show abstract

“…We wish to mention the usefulness of the C++ ODE-solver "ODEINT" [46] and communication with the developers in certain stages of code development. We are grateful to the team which provides the specific Jupyter interface to the supercomputers in Jülich, which served well for part of the workflow and data analysis [47]. Computations were mostly conducted using the workflow environment JUBE provided by JSC [48].…”

Section: Acknowledgementsmentioning

confidence: 99%

Quasi-particle functional Renormalisation Group calculations in the two-dimensional half-filled Hubbard model at finite temperatures

Rohe

Honerkamp

2020

SciPost Phys.

View full text Add to dashboard Cite

We present a highly parallelisable scheme for treating functional Renormalisation Group equations which incorporates a quasi-particle-based feedback on the flow and provides direct access to real-frequency self-energy data. This allows to map out the boundaries of Fermi-liquid regimes and to study the effect of quasi-particle degradation near Fermi liquid instabilities. As a first application, selected results for the two-dimensional half-filled perfectly nested Hubbard model are shown.

show abstract

Enabling Interactive Supercomputing at JSC Lessons Learned

Cited by 9 publications

References 5 publications

Quasi-particle functional renormalisation group calculations in the two-dimensional t-t'-Hubbard model

Quasi-particle functional renormalisation group calculations in the two-dimensional t-t'-Hubbard model

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

Quasi-particle functional Renormalisation Group calculations in the two-dimensional half-filled Hubbard model at finite temperatures

Contact Info

Product

Resources

About