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
Kukstas E
(2020)
Environment from cross-correlations: connecting hot gas and the quenching of galaxies
in Monthly Notices of the Royal Astronomical Society
Elbakyan V
(2023)
Episodic accretion and mergers during growth of massive protostars
in Monthly Notices of the Royal Astronomical Society
Attanasio F
(2022)
Equation of state from complex Langevin simulations
in EPJ Web of Conferences
Attanasio F
(2022)
Equation of state from complex Langevin simulations
in EPJ Web of Conferences
Blum T
(2017)
Erratum to: Lattice calculation of the leading strange quark-connected contribution to the muon g - 2
in Journal of High Energy Physics
Changeat Q
(2023)
ESA-Ariel Data Challenge NeurIPS 2022: introduction to exo-atmospheric studies and presentation of the Atmospheric Big Challenge (ABC) Database
in RAS Techniques and Instruments
Hardy F
(2023)
Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning
in International Journal of Data Science and Analytics
Andrade T
(2022)
Evidence for violations of Weak Cosmic Censorship in black hole collisions in higher dimensions
in Journal of High Energy Physics
Witzke V
(2019)
Evolution and characteristics of forced shear flows in polytropic atmospheres: large and small Péclet number regimes
in Monthly Notices of the Royal Astronomical Society
Creci G
(2020)
Evolution of black hole shadows from superradiance
in Physical Review D
Rodrigues L
(2019)
Evolution of galactic magnetic fields
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
Davis T
(2019)
Evolution of the cold gas properties of simulated post-starburst galaxies
in Monthly Notices of the Royal Astronomical Society
Beg R
(2022)
Evolution, Structure, and Topology of Self-generated Turbulent Reconnection Layers
in The Astrophysical Journal
Beg R
(2022)
Evolution, Structure, and Topology of Self-generated Turbulent Reconnection Layers
in The Astrophysical Journal
Bertulani C
(2021)
Examination of the sensitivity of quasifree reactions to details of the bound-state overlap functions
in Physical Review C
Bertulani C
(2021)
Examination of the sensitivity of quasifree reactions to details of the bound-state overlap functions
in Physical Review C
Ryan S
(2021)
Excited and exotic bottomonium spectroscopy from lattice QCD
in Journal of High Energy Physics
Johnson C
(2021)
Excited J - - meson resonances at the SU(3) flavor point from lattice QCD
in Physical Review D
Flynn J
(2023)
Exclusive semileptonic B s ? K l ? decays on the lattice
in Physical Review D
Bartlett D
(2024)
Exhaustive Symbolic Regression
in IEEE Transactions on Evolutionary Computation
Langleben J
(2019)
ExoMol line list - XXXIV. A rovibrational line list for phosphinidene (PH) in its $X\, {}^3\Sigma ^-$ and $a\, {}^1\Delta$ electronic states
in Monthly Notices of the Royal Astronomical Society
Owens A
(2024)
ExoMol line lists - LI. Molecular line lists for lithium hydroxide (LiOH)
in Monthly Notices of the Royal Astronomical Society
Yurchenko S
(2024)
ExoMol line lists - LIII: empirical rovibronic spectra of yttrium oxide
in Monthly Notices of the Royal Astronomical Society
Yurchenko S
(2024)
ExoMol line lists - LIV. Empirical line lists for AlH and AlD and experimental emission spectroscopy of AlD in A1? ( v = 0, 1, 2)
in Monthly Notices of the Royal Astronomical Society
Yurchenko S
(2020)
ExoMol line lists - XL. Rovibrational molecular line list for the hydronium ion (H3O+)
in Monthly Notices of the Royal Astronomical Society
Owens A
(2021)
ExoMol line lists - XLI. High-temperature molecular line lists for the alkali metal hydroxides KOH and NaOH
in Monthly Notices of the Royal Astronomical Society
Owens A
(2022)
ExoMol line lists - XLVII. Rovibronic molecular line list of the calcium monohydroxide radical (CaOH)
in Monthly Notices of the Royal Astronomical Society
Owens A
(2022)
ExoMol line lists - XLVII. Rovibronic molecular line list of the calcium monohydroxide radical (CaOH)
in Monthly Notices of the Royal Astronomical Society
Li H
(2019)
ExoMol line lists - XXXII. The rovibronic spectrum of MgO
in Monthly Notices of the Royal Astronomical Society
Yurchenko S
(2020)
ExoMol line lists - XXXIX. Ro-vibrational molecular line list for CO2
in Monthly Notices of the Royal Astronomical Society
Owens A
(2020)
ExoMol line lists - XXXVIII. High-temperature molecular line list of silicon dioxide (SiO2)
in Monthly Notices of the Royal Astronomical Society
Coles P
(2019)
ExoMol molecular line lists - XXXV. A rotation-vibration line list for hot ammonia
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
Gorman M
(2019)
ExoMol molecular line lists XXXVI: X 2? - X 2? and A 2S+ - X 2? transitions of SH
in Monthly Notices of the Royal Astronomical Society
Rogers J
(2023)
Exoplanet atmosphere evolution: emulation with neural networks
in Monthly Notices of the Royal Astronomical Society
Van Loon M
(2021)
Explaining the scatter in the galaxy mass-metallicity relation with gas flows
in Monthly Notices of the Royal Astronomical Society
Joswig F
(2023)
Exploring distillation at the SU(3) flavour symmetric point
Erben F
(2022)
Exploring distillation at the SU(3) flavour symmetric point
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
Edwards B
(2023)
Exploring the Ability of Hubble Space Telescope WFC3 G141 to Uncover Trends in Populations of Exoplanet Atmospheres through a Homogeneous Transmission Survey of 70 Gaseous Planets
in The Astrophysical Journal Supplement Series
Orkney M
(2023)
Exploring the diversity and similarity of radially anisotropic Milky Way-like stellar haloes: implications for disrupted dwarf galaxy searches
in Monthly Notices of the Royal Astronomical Society
Van Daalen M
(2020)
Exploring the effects of galaxy formation on matter clustering through a library of simulation power spectra
in Monthly Notices of the Royal Astronomical Society
Garver B
(2023)
Exploring the Evolution of Massive Clumps in Simulations That Reproduce the Observed Milky Way a-element Abundance Bimodality
in The Astrophysical Journal
Cummins D
(2022)
Extreme pebble accretion in ringed protoplanetary discs
in Monthly Notices of the Royal Astronomical Society
Cummins D
(2022)
Extreme pebble accretion in ringed protoplanetary discs
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
Miles P
(2020)
Fallback Rates from Partial Tidal Disruption Events
in The Astrophysical Journal
Trotta D
(2020)
Fast Acceleration of Transrelativistic Electrons in Astrophysical Turbulence
in The Astrophysical Journal
| 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 |