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
Deason A
(2022)
Dwarf stellar haloes: a powerful probe of small-scale galaxy formation and the nature of dark matter
in Monthly Notices of the Royal Astronomical Society
Deason A
(2019)
The total stellar halo mass of the Milky Way
in Monthly Notices of the Royal Astronomical Society
Deason A
(2021)
Stellar splashback: the edge of the intracluster light
in Monthly Notices of the Royal Astronomical Society
Deason A
(2019)
The local high-velocity tail and the Galactic escape speed
in Monthly Notices of the Royal Astronomical Society
Deason A
(2023)
Unravelling the mass spectrum of destroyed dwarf galaxies with the metallicity distribution function
in Monthly Notices of the Royal Astronomical Society
Deason A
(2021)
The mass of the Milky Way out to 100 kpc using halo stars
in Monthly Notices of the Royal Astronomical Society
Debattista V
(2023)
The Imprint of Clump Formation at High Redshift. II. The Chemistry of the Bulge
in The Astrophysical Journal
Debattista V
(2020)
Box/peanut-shaped bulges in action space
in Monthly Notices of the Royal Astronomical Society
Debras F
(2019)
Eigenvectors, Circulation, and Linear Instabilities for Planetary Science in 3 Dimensions (ECLIPS3D)
in Astronomy & Astrophysics
Debras F
(2019)
Acceleration of superrotation in simulated hot Jupiter atmospheres
in Astronomy & Astrophysics
DeGraf C
(2021)
Morphological evolution of supermassive black hole merger hosts and multimessenger signatures
in Monthly Notices of the Royal Astronomical Society
Del Debbio L
(2021)
Renormalization of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow
in Physical Review D
Delgado A
(2023)
The MillenniumTNG project: intrinsic alignments of galaxies and haloes
in Monthly Notices of the Royal Astronomical Society
Desmond H
(2022)
Catalogues of voids as antihaloes in the local Universe
in Monthly Notices of the Royal Astronomical Society: Letters
Desmond H
(2023)
On the functional form of the radial acceleration relation
in Monthly Notices of the Royal Astronomical Society
Desmond H
(2021)
Five percent measurement of the gravitational constant in the Large Magellanic Cloud
in Physical Review D
Despali G
(2020)
The lensing properties of subhaloes in massive elliptical galaxies in sterile neutrino cosmologies
in Monthly Notices of the Royal Astronomical Society
De Beer S
(2023)
Resolving the physics of quasar Ly a nebulae (RePhyNe): I. Constraining quasar host halo masses through circumgalactic medium kinematics
in Monthly Notices of the Royal Astronomical Society
De Belsunce R
(2022)
Testing for spectral index variations in polarized CMB foregrounds
in Monthly Notices of the Royal Astronomical Society
De Belsunce R
(2022)
Testing for spectral index variations in polarized CMB foregrounds
in Monthly Notices of the Royal Astronomical Society
De Ceuster F
(2023)
Radiative transfer as a Bayesian linear regression problem
in Monthly Notices of the Royal Astronomical Society
De Vries N
(2023)
Tidal dissipation due to the elliptical instability and turbulent viscosity in convection zones in rotating giant planets and stars
in Monthly Notices of the Royal Astronomical Society
Di Carlo M
(2022)
Electromagnetic finite-size effects beyond the point-like approximation
in EPJ Web of Conferences
Dickey C
(2021)
IQ Collaboratory. II. The Quiescent Fraction of Isolated, Low-mass Galaxies across Simulations and Observations
in The Astrophysical Journal
Digby R
(2019)
The star formation histories of dwarf galaxies in Local Group cosmological simulations
in Monthly Notices of the Royal Astronomical Society
Dillamore A
(2022)
Merger-induced galaxy transformations in the artemis simulations
in Monthly Notices of the Royal Astronomical Society
Dimmock A
(2023)
Backstreaming ions at a high Mach number interplanetary shock Solar Orbiter measurements during the nominal mission phase
in Astronomy & Astrophysics
Dobbs C
(2022)
The formation of massive stellar clusters in converging galactic flows with photoionization
in Monthly Notices of the Royal Astronomical Society
Dobbs C
(2022)
The formation of massive stellar clusters in converging galactic flows with photoionization
in Monthly Notices of the Royal Astronomical Society
Dobbs C
(2021)
The properties of clusters, and the orientation of magnetic fields relative to filaments, in magnetohydrodynamic simulations of colliding clouds
in Monthly Notices of the Royal Astronomical Society
Dobbs C
(2022)
The formation of clusters and OB associations in different density spiral arm environments
in Monthly Notices of the Royal Astronomical Society
Dobbs C
(2019)
Comparing the properties of GMCs in M33 from simulations and observations
in Monthly Notices of the Royal Astronomical Society
Dobbs C
(2022)
The formation of clusters and OB associations in different density spiral arm environments
in Monthly Notices of the Royal Astronomical Society
Dobbs C
(2020)
The formation of young massive clusters by colliding flows
in Monthly Notices of the Royal Astronomical Society: Letters
Dome T
(2023)
On the cosmic web elongation in fuzzy dark matter cosmologies: Effects on density profiles, shapes, and alignments of haloes
in Monthly Notices of the Royal Astronomical Society
Dome T
(2023)
On the cosmic web elongation in fuzzy dark matter cosmologies: Effects on density profiles, shapes, and alignments of haloes
in Monthly Notices of the Royal Astronomical Society
Dome T
(2023)
Cosmic web dissection in fuzzy dark matter cosmologies
in Monthly Notices of the Royal Astronomical Society
Doneva D
(2023)
Testing the limits of scalar-Gauss-Bonnet gravity through nonlinear evolutions of spin-induced scalarization
in Physical Review D
Donevski D
(2020)
In pursuit of giants I. The evolution of the dust-to-stellar mass ratio in distant dusty galaxies
in Astronomy & Astrophysics
Dowdall R
(2019)
Neutral B -meson mixing from full lattice QCD at the physical point
in Physical Review D
Downing E
(2023)
The many reasons that the rotation curves of low-mass galaxies can fail as tracers of their matter distributions
in Monthly Notices of the Royal Astronomical Society
Drach V
(2022)
Singlet channel scattering in a composite Higgs model on the lattice
in The European Physical Journal C
Drach V
(2021)
Scattering of Goldstone bosons and resonance production in a composite Higgs model on the lattice
in Journal of High Energy Physics
Drach V
(2022)
Singlet channel scattering in a composite Higgs model on the lattice
in The European Physical Journal C
Drach V
(2020)
Composite electroweak sectors on the lattice
Drew A
(2022)
Radiation from global topological strings using adaptive mesh refinement: Methodology and massless modes
in Physical Review D
Drew A
(2022)
Radiation from global topological strings using adaptive mesh refinement: Methodology and massless modes
in Physical Review D
Drewes N
(2021)
On the Dynamics of Low-viscosity Warped Disks around Black Holes
in The Astrophysical Journal
Drummond B
(2020)
Implications of three-dimensional chemical transport in hot Jupiter atmospheres: Results from a consistently coupled chemistry-radiation-hydrodynamics model
in Astronomy & Astrophysics
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. |