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
Goyal J
(2020)
A library of self-consistent simulated exoplanet atmospheres
in Monthly Notices of the Royal Astronomical Society
Yoo T
(2020)
On the origin of low escape fractions of ionizing radiation from massive star-forming galaxies at high redshift
in Monthly Notices of the Royal Astronomical Society
Can K
(2020)
Lattice QCD evaluation of the Compton amplitude employing the Feynman-Hellmann theorem
in Physical Review D
Aurrekoetxea J
(2020)
Coherent gravitational waveforms and memory from cosmic string loops
in Classical and Quantum Gravity
Pichon C
(2020)
Why do extremely massive disc galaxies exist today?
in Monthly Notices of the Royal Astronomical Society
Appleby S
(2020)
The impact of quenching on galaxy profiles in the simba simulation
in Monthly Notices of the Royal Astronomical Society
Anisman L
(2020)
WASP-117 b: An Eccentric Hot Saturn as a Future Complex Chemistry Laboratory
in The Astronomical Journal
Buividovich P
(2020)
Electric conductivity in finite-density S U ( 2 ) lattice gauge theory with dynamical fermions
in Physical Review D
Campargue A
(2020)
Observation of electric-quadrupole infrared transitions in water vapor
in Physical Review Research
Sergeev D
(2020)
Atmospheric Convection Plays a Key Role in the Climate of Tidally Locked Terrestrial Exoplanets: Insights from High-resolution Simulations
in The Astrophysical Journal
Hildebrandt H
(2020)
KiDS+VIKING-450: Cosmic shear tomography with optical and infrared data
in Astronomy & Astrophysics
Figueras P
(2020)
Gravitational collapse in cubic Horndeski theories
in Classical and Quantum Gravity
Cooper L
(2020)
$B_c \to B_{s(d)}$ form factors
Nealon R
(2020)
Spirals, shadows & precession in HD 100453 - II. The hidden companion
in Monthly Notices of the Royal Astronomical Society
Yurchenko S
(2020)
ExoMol molecular line lists - XXXVII. Spectra of acetylene
in Monthly Notices of the Royal Astronomical Society
Tress R
(2020)
Simulations of the Milky Way's central molecular zone - I. Gas dynamics
in Monthly Notices of the Royal Astronomical Society
Rouillard A
(2020)
Models and data analysis tools for the Solar Orbiter mission
in Astronomy & Astrophysics
Vidal J
(2020)
Efficiency of tidal dissipation in slowly rotating fully convective stars or planets
in Monthly Notices of the Royal Astronomical Society
McLean E
(2020)
B s ? D s l ? form factors for the full q 2 range from lattice QCD with nonperturbatively normalized currents
in Physical Review D
Haworth T
(2020)
The observational anatomy of externally photoevaporating planet-forming discs - I. Atomic carbon
in Monthly Notices of the Royal Astronomical Society
Brown S
(2020)
Connecting the structure of dark matter haloes to the primordial power spectrum
in Monthly Notices of the Royal Astronomical Society
Stafford S
(2020)
Exploring extensions to the standard cosmological model and the impact of baryons on small scales
in Monthly Notices of the Royal Astronomical Society
Antolin P
(2020)
Reconnection nanojets in the solar corona
in Nature Astronomy
Robertson A
(2020)
Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data
in Monthly Notices of the Royal Astronomical Society
Franci L
(2020)
Modeling MMS Observations at the Earth's Magnetopause with Hybrid Simulations of Alfvénic Turbulence
in The Astrophysical Journal
Mercer A
(2020)
Planet formation around M dwarfs via disc instability Fragmentation conditions and protoplanet properties
in Astronomy & Astrophysics
Rosca-Mead R
(2020)
Core collapse in massive scalar-tensor gravity
in Physical Review D
Deason A
(2020)
The edge of the Galaxy
in Monthly Notices of the Royal Astronomical Society
Ho S
(2020)
Morphological and Rotation Structures of Circumgalactic Mg ii Gas in the EAGLE Simulation and the Dependence on Galaxy Properties
in The Astrophysical Journal
Vandenbroucke B
(2020)
Infrared luminosity functions and dust mass functions in the EAGLE simulation
in Monthly Notices of the Royal Astronomical Society
Gratton S
(2020)
Understanding parameter differences between analyses employing nested data subsets
in Monthly Notices of the Royal Astronomical Society
Iyer K
(2020)
The diversity and variability of star formation histories in models of galaxy evolution
in Monthly Notices of the Royal Astronomical Society
Owen J
(2020)
Testing exoplanet evaporation with multitransiting systems
in Monthly Notices of the Royal Astronomical Society
Tam S
(2020)
The distribution of dark matter and gas spanning 6 Mpc around the post-merger galaxy cluster MS 0451-03
in Monthly Notices of the Royal Astronomical Society
Zenocratti L
(2020)
Correlations between mass, stellar kinematics, and gas metallicity in eagle galaxies
in Monthly Notices of the Royal Astronomical Society: Letters
Joudaki S
(2020)
KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear
in Astronomy & Astrophysics
Guo Y
(2020)
Metal Enrichment in the Circumgalactic Medium and Lya Halos around Quasars at z ~ 3
in The Astrophysical Journal
Clarke C
(2020)
Forbidden line diagnostics of photoevaporative disc winds
in Monthly Notices of the Royal Astronomical Society
Benitez-Llambay A
(2020)
The detailed structure and the onset of galaxy formation in low-mass gaseous dark matter haloes
in Monthly Notices of the Royal Astronomical Society
Ilee J
(2020)
Observing protoplanetary discs with the Square Kilometre Array - I. Characterizing pebble substructure caused by forming planets
in Monthly Notices of the Royal Astronomical Society
Gómez-Guijarro C
(2020)
How primordial magnetic fields shrink galaxies
in Monthly Notices of the Royal Astronomical Society
Adamek J
(2020)
Numerical solutions to Einstein's equations in a shearing-dust universe: a code comparison
in Classical and Quantum Gravity
Stafford S
(2020)
The bahamas project: effects of a running scalar spectral index on large-scale structure
in Monthly Notices of the Royal Astronomical Society
Trayford J
(2020)
Massive low-surface-brightness galaxies in the eagle simulation
in Monthly Notices of the Royal Astronomical Society
Poole-McKenzie R
(2020)
Informing dark matter direct detection limits with the ARTEMIS simulations
in Journal of Cosmology and Astroparticle Physics
Pimpanuwat B
(2020)
Maser flares driven by variations in pumping and background radiation
in Monthly Notices of the Royal Astronomical Society
Grand R
(2020)
The biggest splash
in Monthly Notices of the Royal Astronomical Society
Genina A
(2020)
To ß or not to ß: can higher order Jeans analysis break the mass-anisotropy degeneracy in simulated dwarfs?
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 |