Dirac 2.5 Operations
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Physics and Astronomy
Abstract
Physicists across the astronomy, nuclear and particle physics communities are focussed
on understanding how the Universe works at a very fundamental level. The distance scales
with which they work vary by 50 orders of magnitude from the smallest distances probed
by experiments at the Large Hadron Collider, deep within the atomic
nucleus, to the largest scale galaxy clusters discovered out in space. The Science challenges,
however, are linked through questions such as: How did the Universe begin and how is it evolving?
and What are the fundamental constituents and fabric of the Universe and how do they interact?
Progress requires new astronomical observations and experimental data but also
new theoretical insights. Theoretical understanding comes increasingly from large-scale
computations that allow us to confront the consequences of our theories very accurately
with the data or allow us to interrogate the data in detail to extract information that has
impact on our theories. These computations test the fastest computers that we have and
push the boundaries of technology in this sector. They also provide an excellent
environment for training students in state-of-the-art techniques for code optimisation and
data mining and visualisation.
The DiRAC-2.5 project builds on the success of the DiRAC HPC facility and will provide the resources needed
to support cutting edge research during 2017 in all areas of science supported by STFC.
DiRAC-2.5 will provide maintain the existing DiRAC-2 services from April 2017, and also provide and increase in computational
resources at Durham, Cambridge and Leicester.
This grant will support the operation of the Edinburgh DiRAC services, which presently comprise
98384 operational computing cores serving around 80% of DiRAC computing cycles. The system is made up
from both the original 1.26PFlop/s DiRAC BlueGene/Q system and, following a recent transfer
to Edinburgh by STFC, six racks of the Hartree BlueJoule supercomputer.
The DiRAC project also will offer a team of three research software engineers who will help DiRAC researchers to ensure their scientific codes to extract
the best possible performance from the hardware components of the DiRAC clusters. These highly skilled programmers will
increase the effective computational power of the DiRAC facility during 2017.
on understanding how the Universe works at a very fundamental level. The distance scales
with which they work vary by 50 orders of magnitude from the smallest distances probed
by experiments at the Large Hadron Collider, deep within the atomic
nucleus, to the largest scale galaxy clusters discovered out in space. The Science challenges,
however, are linked through questions such as: How did the Universe begin and how is it evolving?
and What are the fundamental constituents and fabric of the Universe and how do they interact?
Progress requires new astronomical observations and experimental data but also
new theoretical insights. Theoretical understanding comes increasingly from large-scale
computations that allow us to confront the consequences of our theories very accurately
with the data or allow us to interrogate the data in detail to extract information that has
impact on our theories. These computations test the fastest computers that we have and
push the boundaries of technology in this sector. They also provide an excellent
environment for training students in state-of-the-art techniques for code optimisation and
data mining and visualisation.
The DiRAC-2.5 project builds on the success of the DiRAC HPC facility and will provide the resources needed
to support cutting edge research during 2017 in all areas of science supported by STFC.
DiRAC-2.5 will provide maintain the existing DiRAC-2 services from April 2017, and also provide and increase in computational
resources at Durham, Cambridge and Leicester.
This grant will support the operation of the Edinburgh DiRAC services, which presently comprise
98384 operational computing cores serving around 80% of DiRAC computing cycles. The system is made up
from both the original 1.26PFlop/s DiRAC BlueGene/Q system and, following a recent transfer
to Edinburgh by STFC, six racks of the Hartree BlueJoule supercomputer.
The DiRAC project also will offer a team of three research software engineers who will help DiRAC researchers to ensure their scientific codes to extract
the best possible performance from the hardware components of the DiRAC clusters. These highly skilled programmers will
increase the effective computational power of the DiRAC facility during 2017.
Planned Impact
The expected impact of the DiRAC 2.5 HPC facility is fully described in the attached pathways to impact document and includes:
1) Disseminating best practice in High Performance Computing software engineering throughout the theoretical Particle Physics, Astronomy and Nuclear physics communities in the UK as well as to industry partners.
2) Working on co-design projects with industry partners to improve future generations of hardware and software.
3) Development of new techniques in the area of High Performance Data Analytics which will benefit industry partners and researchers in other fields such as biomedicine, biology, engineering, economics and social science, and the natural environment who can use this new technology to improve research outcomes in their areas.
4) Share best practice on the design and operation of distributed HPC facilities with UK National e-Infrastructure partners.
5) Training of the next generation of research scientists of physical scientists to tackle problems effectively on state-of-the-art of High Performance Computing facilities. Such skills are much in demand from high-tech industry.
6) Engagement with the general public to promote interest in science, and to explain how our ability to solve complex problems using the latest computer technology leads to new scientific capabilities/insights. Engagement of this kind also naturally encourages the uptake of STEM subjects in schools.
1) Disseminating best practice in High Performance Computing software engineering throughout the theoretical Particle Physics, Astronomy and Nuclear physics communities in the UK as well as to industry partners.
2) Working on co-design projects with industry partners to improve future generations of hardware and software.
3) Development of new techniques in the area of High Performance Data Analytics which will benefit industry partners and researchers in other fields such as biomedicine, biology, engineering, economics and social science, and the natural environment who can use this new technology to improve research outcomes in their areas.
4) Share best practice on the design and operation of distributed HPC facilities with UK National e-Infrastructure partners.
5) Training of the next generation of research scientists of physical scientists to tackle problems effectively on state-of-the-art of High Performance Computing facilities. Such skills are much in demand from high-tech industry.
6) Engagement with the general public to promote interest in science, and to explain how our ability to solve complex problems using the latest computer technology leads to new scientific capabilities/insights. Engagement of this kind also naturally encourages the uptake of STEM subjects in schools.
Publications
Hillier A
(2019)
Coronal Cooling as a Result of Mixing by the Nonlinear Kelvin-Helmholtz Instability
in The Astrophysical Journal
Wang E
(2019)
The impact of black hole seeding in cosmological simulations
in Monthly Notices of the Royal Astronomical Society
Barbieri C
(2019)
Lepton scattering from Ar 40 and Ti 48 in the quasielastic peak region
in Physical Review C
Bena I
(2019)
Holographic dual of hot Polchinski-Strassler quark-gluon plasma
in Journal of High Energy Physics
Young A
(2019)
Synthetic molecular line observations of the first hydrostatic core from chemical calculations
in Monthly Notices of the Royal Astronomical Society
McNally C
(2019)
Migrating super-Earths in low-viscosity discs: unveiling the roles of feedback, vortices, and laminar accretion flows
in Monthly Notices of the Royal Astronomical Society
Shao S
(2019)
Evolution of galactic planes of satellites in the eagle simulation
in Monthly Notices of the Royal Astronomical Society
Debras F
(2019)
Acceleration of superrotation in simulated hot Jupiter atmospheres
in Astronomy & Astrophysics
Debras F
(2019)
Eigenvectors, Circulation, and Linear Instabilities for Planetary Science in 3 Dimensions (ECLIPS3D)
in Astronomy & Astrophysics
Shao S
(2019)
Screening maps of the local Universe I - Methodology
in Monthly Notices of the Royal Astronomical Society
Booth R
(2019)
Characterizing gravito-turbulence in 3D: turbulent properties and stability against fragmentation
in Monthly Notices of the Royal Astronomical Society
Schönrich R
(2019)
The chemical evolution of r-process elements from neutron star mergers: the role of a 2-phase interstellar medium
in Monthly Notices of the Royal Astronomical Society
Pfeffer J
(2019)
Young star cluster populations in the E-MOSAICS simulations
in Monthly Notices of the Royal Astronomical Society
Dowdall R
(2019)
Neutral B -meson mixing from full lattice QCD at the physical point
in Physical Review D
Kessar M
(2019)
Scale Selection in the Stratified Convection of the Solar Photosphere
in The Astrophysical Journal
Sykes C
(2019)
Fluorescent rings in star-free dark matter haloes
in Monthly Notices of the Royal Astronomical Society
Jackson R
(2019)
Massive spheroids can form in single minor mergers
in Monthly Notices of the Royal Astronomical Society
Cautun M
(2019)
The aftermath of the Great Collision between our Galaxy and the Large Magellanic Cloud
in Monthly Notices of the Royal Astronomical Society
MacFarlane B
(2019)
Observational signatures of outbursting protostars - I: From hydrodynamic simulations to observations
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
Lovell M
(2019)
The signal of decaying dark matter with hydrodynamical simulations
in Monthly Notices of the Royal Astronomical Society
Correa C
(2019)
The origin of the red-sequence galaxy population in the EAGLE simulation
in Monthly Notices of the Royal Astronomical Society
Du M
(2019)
The Formation of Compact Elliptical Galaxies in the Vicinity of a Massive Galaxy: The Role of Ram-pressure Confinement
in The Astrophysical Journal
Idini A
(2019)
Ab Initio Optical Potentials and Nucleon Scattering on Medium Mass Nuclei.
in Physical review letters
Baugh C
(2019)
Galaxy formation in the Planck Millennium: the atomic hydrogen content of dark matter haloes
in Monthly Notices of the Royal Astronomical Society
Rosca-Mead R
(2019)
Inverse-chirp signals and spontaneous scalarisation with self-interacting potentials in stellar collapse
in Classical and Quantum Gravity
Bennett E
(2019)
Sp (4) gauge theories on the lattice: Nf = 2 dynamical fundamental fermions
in Journal of High Energy Physics
Merson A
(2019)
Linear bias forecasts for emission line cosmological surveys
in Monthly Notices of the Royal Astronomical Society
Navarro J
(2019)
Baryon-induced dark matter cores in the eagle simulations
in Monthly Notices of the Royal Astronomical Society
Currie L
(2020)
Convection with misaligned gravity and rotation: simulations and rotating mixing length theory
in Monthly Notices of the Royal Astronomical Society
Lee J
(2020)
Dual Effects of Ram Pressure on Star Formation in Multiphase Disk Galaxies with Strong Stellar Feedback
in The Astrophysical Journal
Reid J
(2020)
Coronal energy release by MHD avalanches: Heating mechanisms
in Astronomy & Astrophysics
Coulton W
(2020)
Weak lensing minima and peaks: Cosmological constraints and the impact of baryons
in Monthly Notices of the Royal Astronomical Society
Currie L
(2020)
Generation of shear flows and vortices in rotating anelastic convection
in Physical Review Fluids
Clark V
(2020)
The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)
in Journal of Quantitative Spectroscopy and Radiative Transfer
Cooke R
(2020)
The ACCELERATION programme: I. Cosmology with the redshift drift
in Monthly Notices of the Royal Astronomical Society
Trayford J
(2020)
Fade to grey: systematic variation of galaxy attenuation curves with galaxy properties in the eagle simulations
in Monthly Notices of the Royal Astronomical Society
Borrow J
(2020)
Cosmological baryon transfer in the simba simulations
in Monthly Notices of the Royal Astronomical Society
Cuesta-Lazaro C
(2020)
Towards a non-Gaussian model of redshift space distortions
in Monthly Notices of the Royal Astronomical Society
Davé R
(2020)
Galaxy cold gas contents in modern cosmological hydrodynamic simulations
in Monthly Notices of the Royal Astronomical Society
Katsianis A
(2020)
The high-redshift SFR-M* relation is sensitive to the employed star formation rate and stellar mass indicators: towards addressing the tension between observations and simulations
in Monthly Notices of the Royal Astronomical Society
Duguid C
(2020)
Convective turbulent viscosity acting on equilibrium tidal flows: new frequency scaling of the effective viscosity
in Monthly Notices of the Royal Astronomical Society
Bulla M
(2020)
White dwarf deflagrations for Type Iax supernovae: polarisation signatures from the explosion and companion interaction
in Astronomy & Astrophysics
Bastian N
(2020)
The globular cluster system mass-halo mass relation in the E-MOSAICS simulations
in Monthly Notices of the Royal Astronomical Society
Mitchell P
(2020)
Galactic outflow rates in the EAGLE simulations
in Monthly Notices of the Royal Astronomical Society
Halbesma T
(2020)
The globular cluster system of the Auriga simulations
in Monthly Notices of the Royal Astronomical Society
Tress R
(2020)
Simulations of the star-forming molecular gas in an interacting M51-like galaxy
in Monthly Notices of the Royal Astronomical Society
Hernández-Aguayo C
(2020)
Measuring the baryon acoustic oscillation peak position with different galaxy selections
in Monthly Notices of the Royal Astronomical Society
Agertz O
(2020)
EDGE: the mass-metallicity relation as a critical test of galaxy formation physics
in Monthly Notices of the Royal Astronomical Society
Beraldo e Silva L
(2020)
Geometric properties of galactic discs with clumpy episodes
in Monthly Notices of the Royal Astronomical Society
| Description | DiRAC 2.5 is a facility to support leading-edge computational astronomy and particle physics in the UK. This has resulted in over 1000 peer-reviewed publications. Many new discoveries about the formation and evolution of galaxies, star formation, planet formation and particle physics theory have been made possible by the award |
| Exploitation Route | Build on the scientific knowledge and computational techniques developed. Many international collaborative projects are supported by the HPC resources provided by DiRAC. |
| Sectors | Aerospace Defence and Marine Creative Economy Digital/Communication/Information Technologies (including Software) Education Healthcare |
| URL | http://www.dirac.ac.uk |
| Description | A close working relationship on co-design of hardware and software. |
| First Year Of Impact | 2015 |
| Sector | Digital/Communication/Information Technologies (including Software),Education |
| Impact Types | Economic |
| Title | Lattice dataset for the paper arXiv:2202.08795 "Simulating rare kaon decays using domain wall lattice QCD with physical light quark masses" |
| Description | Release for https://arxiv.org/abs/2202.08795 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/6369178 |
| Title | Symplectic lattice gauge theories on Grid: approaching the conformal window---data release |
| Description | This is the data release relative to the paper "Symplectic lattice gauge theories on Grid: approaching the conformal window" (arXiv:2306.11649). It contains pre-analysed data that can be plotted, and raw data that can be analysed and plotted through the analysis code in doi:10.5281/zenodo.8136514. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/8136452 |
| Title | Symplectic lattice gauge theories on Grid: approaching the conformal window---data release |
| Description | This is the data release relative to the paper "Symplectic lattice gauge theories on Grid: approaching the conformal window" (arXiv:2306.11649). It contains pre-analysed data that can be plotted, and raw data that can be analysed and plotted through the analysis code in doi:10.5281/zenodo.8136514. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/8136451 |
| Description | Intel IPAG QCD codesign project |
| Organisation | Intel Corporation |
| Department | Intel Corporation (Jones Farm) |
| Country | United States |
| Sector | Private |
| PI Contribution | We have collaborated with Intel corporation since 2014 with $720k of total direct funding, starting initially as an Intel parallel computing centre, and expanding to direct close collaboration with Intel Pathfinding and Architecture Group. |
| Collaborator Contribution | We have performed detailed optimisation of QCD codes (Wilson, Domain Wall, Staggered) on Intel many core architectures. We have investigated the memory system and interconnect performance, particularly on Intel's latest interconnect hardware called Omnipath. We found serious performance issues and worked with Intel to plan a solution and this has been verified and is available as beta software. It will reach general availability in the Intel MPI 2019 release, and allow threaded concurrent communications in MPI for the first time. A joint paper on the resolution to this was written with the Intel MPI team, and the application of the same QCD programming techniques to machine learning gradient reduction was applied in the paper to the Baidu Research all reduce library, demonstrating a 10x gain for this critical step in machine learning in clustered environments. We are also working with Intel verifying future architectures that will deliver the exascale performance in 2021. |
| Impact | We have performed detailed optimisation of QCD codes (Wilson, Domain Wall, Staggered) on Intel many core architectures. We have investigated the memory system and interconnect performance, particularly on Intel's latest interconnect hardware called Omnipath. We found serious performance issues and worked with Intel to plan a solution and this has been verified and is available as beta software. It will reach general availability in the Intel MPI 2019 release, and allow threaded concurrent communications in MPI for the first time. A joint paper on the resolution to this was written with the Intel MPI team, and the application of the same QCD programming techniques to machine learning gradient reduction was applied in the paper to the Baidu Research all reduce library, demonstrating a 10x gain for this critical step in machine learning in clustered environments. This collaboration has been renewed annually in 2018, 2019, 2020. Two DiRAC RSE's were hired by Intel to work on the Turing collaboration. |
| Start Year | 2016 |
| Title | FP16-S7E8 MIXED PRECISION FOR DEEP LEARNING AND OTHER ALGORITHMS |
| Description | We demonstrated that a new non-IEEE 16 bit floating point format is the optimal choice for machine learning training and proposed instructions. |
| IP Reference | US20190042544 |
| Protection | Patent application published |
| Year Protection Granted | 2019 |
| Licensed | Yes |
| Impact | We demonstrated that a new non-IEEE 16 bit floating point format is the optimal choice for machine learning training and proposed instructions. Intel filed this with US patent office. This IP is owned by Intel under the terms of the Intel Turing strategic partnership contract. As a co-inventor I have been named on the patent application. The proposed format has been announced as planned for use in future Intel architectures. This collaboration with Turing emerged out of an investment in Edinburgh by Intel Pathfinding and Architecture Group in codesign with lattice gauge theory simulations. Intel hired DiRAC RSE's Kashyap and Lepper and placed them in Edinburgh to work with me on Machine Learning codesign through the Turing programme. |
| Title | Symplectic lattice gauge theories on Grid: approaching the conformal window-analysis code |
| Description | This is the analysis code that has been used to analyse and plot the data for the paper 'Symplectic lattice gauge theories on Grid: approaching the conformal window' (arXiv:2306.11649). |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://zenodo.org/record/8136513 |