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
McAlpine S
(2020)
Galaxy mergers in eagle do not induce a significant amount of black hole growth yet do increase the rate of luminous AGN
in Monthly Notices of the Royal Astronomical Society
Raste J
(2021)
Implications of the z > 5 Lyman-a forest for the 21-cm power spectrum from the epoch of reionization
in Monthly Notices of the Royal Astronomical Society
Jennings F
(2023)
Halo scaling relations and hydrostatic mass bias in the simba simulation from realistic mock X-ray catalogues
in Monthly Notices of the Royal Astronomical Society
Desmond H
(2023)
On the functional form of the radial acceleration relation
in Monthly Notices of the Royal Astronomical Society
Barrera M
(2023)
The MillenniumTNG Project: semi-analytic galaxy formation models on the past lightcone
in Monthly Notices of the Royal Astronomical Society
Wright S
(2022)
Non-local thermal equilibrium spectra of atmospheric molecules for exoplanets
in Monthly Notices of the Royal Astronomical Society
Hill A
(2021)
The morphology of star-forming gas and its alignment with galaxies and dark matter haloes in the EAGLE simulations
in Monthly Notices of the Royal Astronomical Society
Solar M
(2020)
Azimuthal variations of oxygen abundance profiles in star-forming regions of disc galaxies in EAGLE simulations
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
Ramírez-Galeano L
(2022)
Why most molecular clouds are gravitationally dominated
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
Puchwein E
(2023)
The Sherwood-Relics simulations: overview and impact of patchy reionization and pressure smoothing on the intergalactic medium
in Monthly Notices of the Royal Astronomical Society
Jackson R
(2021)
Dark matter-deficient dwarf galaxies form via tidal stripping of dark matter in interactions with massive companions
in Monthly Notices of the Royal Astronomical Society
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
Trotta D
(2023)
Three-dimensional modelling of the shock-turbulence interaction
in Monthly Notices of the Royal Astronomical Society
Huško F
(2023)
The buildup of galaxies and their spheroids: The contributions of mergers, disc instabilities, and star formation
in Monthly Notices of the Royal Astronomical Society
Borrow J
(2022)
Sphenix : smoothed particle hydrodynamics for the next generation of galaxy formation simulations
in Monthly Notices of the Royal Astronomical Society
Sawala T
(2023)
The Local Group's mass: probably no more than the sum of its parts
in Monthly Notices of the Royal Astronomical Society
Gargiulo I
(2019)
The prevalence of pseudo-bulges in the Auriga simulations
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
Roper W
(2022)
First Light And Reionisation Epoch Simulations ( flares ) - IV. The size evolution of galaxies at z = 5
in Monthly Notices of the Royal Astronomical Society
Molaro M
(2023)
Possible evidence for a large-scale enhancement in the Lyman-a forest power spectrum at redshift z = 4
in Monthly Notices of the Royal Astronomical Society
Hill A
(2022)
Intrinsic alignments of the extended radio continuum emission of galaxies in the EAGLE simulations
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
Elbakyan V
(2023)
Episodic accretion and mergers during growth of massive protostars
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
Hassan S
(2020)
Testing galaxy formation simulations with damped Lyman-a abundance and metallicity evolution
in Monthly Notices of the Royal Astronomical Society
Mukherjee S
(2021)
SEAGLE - II. Constraints on feedback models in galaxy formation from massive early-type strong-lens galaxies
in Monthly Notices of the Royal Astronomical Society
Barrera-Hinojosa C
(2021)
Vector modes in ?CDM: the gravitomagnetic potential in dark matter haloes from relativistic N -body simulations
in Monthly Notices of the Royal Astronomical Society
Nightingale J
(2023)
Abell 1201: detection of an ultramassive black hole in a strong gravitational lens
in Monthly Notices of the Royal Astronomical Society
Coleman G
(2022)
Dusty circumbinary discs: inner cavity structures and stopping locations of migrating planets
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
Coulton W
(2020)
Weak lensing minima and peaks: Cosmological constraints and the impact of baryons
in Monthly Notices of the Royal Astronomical Society
Wilson B
(2022)
A measurement of the Ly ß forest power spectrum and its cross with the Ly a forest in X-Shooter XQ-100
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
Cataneo M
(2019)
On the road to percent accuracy: non-linear reaction of the matter power spectrum to dark energy and modified gravity
in Monthly Notices of the Royal Astronomical Society
Nealon R
(2020)
Spirals, shadows & precession in HD 100453 - II. The hidden companion
in Monthly Notices of the Royal Astronomical Society
Thomas N
(2022)
The environments of the radio galaxy population in simba
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
Lovell M
(2020)
Local group star formation in warm and self-interacting dark matter cosmologies
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
Trayford J
(2019)
The star formation rate and stellar content contributions of morphological components in the EAGLE simulations
in Monthly Notices of the Royal Astronomical Society
Griffin A
(2020)
AGNs at the cosmic dawn: predictions for future surveys from a ?CDM cosmological model
in Monthly Notices of the Royal Astronomical Society
Porth L
(2020)
Fast estimation of aperture mass statistics - I. Aperture mass variance and an application to the CFHTLenS data
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
Mocz P
(2023)
Cosmological structure formation and soliton phase transition in fuzzy dark matter with axion self-interactions
in Monthly Notices of the Royal Astronomical Society
Dutta R
(2021)
Metal-enriched halo gas across galaxy overdensities over the last 10 billion years
in Monthly Notices of the Royal Astronomical Society
Hou J
(2019)
A comparison between semi-analytical gas cooling models and cosmological hydrodynamical simulations
in Monthly Notices of the Royal Astronomical Society
Wijers N
(2022)
The warm-hot circumgalactic medium around EAGLE-simulation galaxies and its detection prospects with X-ray-line emission
in Monthly Notices of the Royal Astronomical Society
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
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. |