DiRAC-2.5 - the pathway to DiRAC Phase 3
Lead Research Organisation:
Durham University
Department Name: Physics
Abstract
We request funding to relocate the Blue Wonder HPC cluster and associated storage, currently at the Hartree Centre at Daresbury, to Durham, together with installation and hardware maintenance costs. This move would enable DiRAC to expand the current DiRAC-2 Data centric service, managed by Durham, by a factor of two in both computing power and data storage capacity. The new service would be called the DiRAC-2.5 Data Centric service.
Planned Impact
DiRAC would seek to continue to engage with industry at various levels, from the
provision of computing cycles for industrial applications to the
exchange of technical knowledge and shared training programmes. The
facility will serve to train young scientists in the most advanced
techniques for supercomputing. These have extensive applications beyond
academia, for example in industry and finance. Finally, output from Dirac-based
projects will be used for science outreach activities.
provision of computing cycles for industrial applications to the
exchange of technical knowledge and shared training programmes. The
facility will serve to train young scientists in the most advanced
techniques for supercomputing. These have extensive applications beyond
academia, for example in industry and finance. Finally, output from Dirac-based
projects will be used for science outreach activities.
Organisations
Publications
Aurrekoetxea JC
(2024)
Effect of Wave Dark Matter on Equal Mass Black Hole Mergers.
in Physical review letters
Aylett-Bullock J
(2021)
June: open-source individual-based epidemiology simulation.
in Royal Society open science
Aylett-Bullock J
(2021)
Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement.
in PLoS computational biology
Badger S
(2023)
Isolated photon production in association with a jet pair through next-to-next-to-leading order in QCD
in Journal of High Energy Physics
Baes M
(2019)
The cosmic spectral energy distribution in the EAGLE simulation
in Monthly Notices of the Royal Astronomical Society
Bahé Y
(2019)
Disruption of satellite galaxies in simulated groups and clusters: the roles of accretion time, baryons, and pre-processing
in Monthly Notices of the Royal Astronomical Society
Bahé Y
(2021)
Strongly lensed cluster substructures are not in tension with ?CDM
in Monthly Notices of the Royal Astronomical Society
Bahé Y
(2022)
The importance of black hole repositioning for galaxy formation simulations
in Monthly Notices of the Royal Astronomical Society
Bahé Y
(2021)
Strongly lensed cluster substructures are not in tension with ?CDM
in Monthly Notices of the Royal Astronomical Society
Ballard D
(2024)
Gravitational imaging through a triple source plane lens: revisiting the ?CDM-defying dark subhalo in SDSSJ0946+1006
in Monthly Notices of the Royal Astronomical Society
Bamber J
(2021)
Quasinormal modes of growing dirty black holes
in Physical Review D
Bamber J
(2023)
Black hole merger simulations in wave dark matter environments
in Physical Review D
Bamber J
(2022)
Black hole merger simulations in wave dark matter environments
Bamber J
(2021)
Growth of accretion driven scalar hair around Kerr black holes
in Physical Review D
Banfi A
(2024)
Higgs interference effects in top-quark pair production in the 1HSM
in Journal of High Energy Physics
Banfi A
(2024)
A POWHEG generator for deep inelastic scattering
in Journal of High Energy Physics
Bantilan H
(2021)
Cauchy evolution of asymptotically global AdS spacetimes with no symmetries
in Physical Review D
Bantilan H
(2020)
Real-Time Dynamics of Plasma Balls from Holography.
in Physical review letters
Baraffe I
(2023)
A study of convective core overshooting as a function of stellar mass based on two-dimensional hydrodynamical simulations
in Monthly Notices of the Royal Astronomical Society
Barber C
(2018)
Calibrated, cosmological hydrodynamical simulations with variable IMFs I: Method and effect on global galaxy scaling relations
in Monthly Notices of the Royal Astronomical Society
Barmentloo S
(2023)
Determining satellite infall times using machine learning
in Monthly Notices of the Royal Astronomical Society
Barmentloo S
(2022)
Determining Satellite Infall Times Using Machine Learning
Barnes D
(2021)
Characterizing hydrostatic mass bias with mock-X
in Monthly Notices of the Royal Astronomical Society
Barnes D
(2021)
Characterizing hydrostatic mass bias with mock-X
in Monthly Notices of the Royal Astronomical Society
| Description | See Dirac annual report https://dirac.ac.uk |
| Exploitation Route | See Dirac annual report https://dirac.ac.uk |
| Sectors | Digital/Communication/Information Technologies (including Software) Education |
| URL | https://dirac.ac.uk |