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
Forzano N
(2023)
Lattice studies of Sp(2N) gauge theories using GRID
Fossati M
(2021)
MUSE analysis of gas around galaxies (MAGG) - III. The gas and galaxy environment of z = 3-4.5 quasars
in Monthly Notices of the Royal Astronomical Society
Fossati M
(2019)
The MUSE Ultra Deep Field (MUDF). II. Survey design and the gaseous properties of galaxy groups at 0.5 < z < 1.5
in Monthly Notices of the Royal Astronomical Society
Foster C
(2021)
The MAGPI survey: Science goals, design, observing strategy, early results and theoretical framework
in Publications of the Astronomical Society of Australia
Fowlie A
(2022)
Nested Sampling for Frequentist Computation: Fast Estimation of Small p-Values.
in Physical review letters
Franci L
(2022)
Anisotropic Electron Heating in Turbulence-driven Magnetic Reconnection in the Near-Sun Solar Wind
in The Astrophysical Journal
Franci L
(2022)
Anisotropic Electron Heating in Turbulence-driven Magnetic Reconnection in the Near-Sun Solar Wind
in The Astrophysical Journal
Franci L
(2020)
Modeling MMS Observations at the Earth's Magnetopause with Hybrid Simulations of Alfvénic Turbulence
in The Astrophysical Journal
Francis A
(2020)
Master-field simulations of O( a )-improved lattice QCD: Algorithms, stability and exactness
in Computer Physics Communications
Frenk C
(2020)
The little things matter: relating the abundance of ultrafaint satellites to the hosts' assembly history
in Monthly Notices of the Royal Astronomical Society
Frenk C
(2020)
The missing dwarf galaxies of the Local Group
in Monthly Notices of the Royal Astronomical Society
Fumagalli M
(2020)
Detecting neutral hydrogen at z ? 3 in large spectroscopic surveys of quasars
in Monthly Notices of the Royal Astronomical Society
Fyfe L
(2021)
Forward modelling of heating within a coronal arcade
in Astronomy & Astrophysics
Gaikwad P
(2023)
Measuring the photoionization rate, neutral fraction, and mean free path of H i ionizing photons at 4.9 = z = 6.0 from a large sample of XShooter and ESI spectra
in Monthly Notices of the Royal Astronomical Society
Gaikwad P
(2020)
Probing the thermal state of the intergalactic medium at z > 5 with the transmission spikes in high-resolution Ly a forest spectra
in Monthly Notices of the Royal Astronomical Society
García-Mascaraque S.C.
(2022)
Meson thermal masses at different temperatures
in Proceedings of Science
Gargiulo I
(2019)
The prevalence of pseudo-bulges in the Auriga simulations
in Monthly Notices of the Royal Astronomical Society
Garratt-Smithson L
(2019)
Galactic chimney sweeping: the effect of 'gradual' stellar feedback mechanisms on the evolution of dwarf galaxies
in Monthly Notices of the Royal Astronomical Society
Garron N
(2023)
Nonperturbative renormalization with interpolating momentum schemes
in Physical Review D
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
Garzilli A
(2020)
Measuring the temperature and profiles of Ly a absorbers
in Monthly Notices of the Royal Astronomical Society
Garzilli A
(2021)
How to constrain warm dark matter with the Lyman-a forest
in Monthly Notices of the Royal Astronomical Society
Garzilli A
(2019)
The Lyman-a forest as a diagnostic of the nature of the dark matter
in Monthly Notices of the Royal Astronomical Society
Gavardi A
(2023)
NNLO+PS W+W- production using jet veto resummation at NNLL'
in Journal of High Energy Physics
Genina A
(2022)
Can tides explain the low dark matter density in Fornax?
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
Genina A
(2022)
Can tides explain the low dark matter density in Fornax?
in Monthly Notices of the Royal Astronomical Society
Genina A
(2023)
On the edge: the relation between stellar and dark matter haloes of Milky Way-mass galaxies
in Monthly Notices of the Royal Astronomical Society
Gerosa D
(2022)
The irreducible mass and the horizon area of LIGO's black holes
in Classical and Quantum Gravity
Gerosa D
(2022)
The irreducible mass and the horizon area of LIGO's black holes
in Classical and Quantum Gravity
Ghosh S
(2024)
First frequency-domain phenomenological model of the multipole asymmetry in gravitational-wave signals from binary-black-hole coalescence
in Physical Review D
Ghosh S
(2022)
Age dissection of the vertical breathing motions in Gaia DR2: evidence for spiral driving
in Monthly Notices of the Royal Astronomical Society
Ghosh S
(2022)
Age dissection of the vertical breathing motions in Gaia DR2: evidence for spiral driving
in Monthly Notices of the Royal Astronomical Society
Givans J
(2022)
Non-linearities in the Lyman-a forest and in its cross-correlation with dark matter halos
in Journal of Cosmology and Astroparticle Physics
Givans J
(2022)
Non-linearities in the Lyman-a forest and in its cross-correlation with dark matter halos
in Journal of Cosmology and Astroparticle Physics
Glesaaen J
(2019)
Hadronic spectrum calculations in the quark-gluon plasma
Glesaaen J.
(2018)
Hadronic spectrum calculations in the quark-gluon plasma
in Proceedings of Science
Glowacki M
(2022)
ASymba: H i global profile asymmetries in the simba simulation
in Monthly Notices of the Royal Astronomical Society
Glowacki M
(2020)
The baryonic Tully-Fisher relation in the simba simulation
in Monthly Notices of the Royal Astronomical Society
Goater A
(2024)
EDGE: The direct link between mass growth history and the extended stellar haloes of the faintest dwarf galaxies
in Monthly Notices of the Royal Astronomical Society
Goater A
(2024)
EDGE: The direct link between mass growth history and the extended stellar haloes of the faintest dwarf galaxies
in Monthly Notices of the Royal Astronomical Society
Golightly E
(2019)
On the Diversity of Fallback Rates from Tidal Disruption Events with Accurate Stellar Structure
in The Astrophysical Journal
Golightly E
(2019)
Tidal Disruption Events: The Role of Stellar Spin
in The Astrophysical Journal
Gonzalez-Perez V
(2020)
Do model emission line galaxies live in filaments at z ~ 1?
in Monthly Notices of the Royal Astronomical Society
Gonzalo T
(2024)
PEANUTS: a software for the automatic computation of solar neutrino flux and its propagation within Earth
in The European Physical Journal C
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
Gourgouliatos K
(2019)
Nonaxisymmetric Hall instability: A key to understanding magnetars
in Physical Review Research
Description | DiRAC 2.5 is a facility to support leading-edge computational astronomy and particle physics in the UK. This has resulted in over 500 peer-reviewed publications. |
Exploitation Route | Build on the scientific knowledge and computational techniques developed. |
Sectors | Digital/Communication/Information Technologies (including Software),Education |
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. |