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
Upadhyay A
(2021)
Star formation histories of Coma cluster galaxies matched to simulated orbits hint at quenching around first pericenter
in Astronomy & Astrophysics
Vandenbroucke B
(2020)
Infrared luminosity functions and dust mass functions in the EAGLE simulation
in Monthly Notices of the Royal Astronomical Society
Vandenbroucke B
(2020)
CMACIONIZE 2.0: a novel task-based approach to Monte Carlo radiation transfer
in Astronomy & Astrophysics
Vandenbroucke B
(2019)
Testing the stability of supersonic ionized Bondi accretion flows with radiation hydrodynamics
in Monthly Notices of the Royal Astronomical Society
Vanon R
(2023)
Three-dimensional Simulations of Massive Stars. II. Age Dependence
in The Astrophysical Journal
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
Van der Werf P
(2020)
An ALMA survey of the SCUBA-2 CLS UDS field: physical properties of 707 sub-millimetre galaxies
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
Varghese A
(2023)
Chemical Mixing Induced by Internal Gravity Waves in Intermediate-mass Stars
in The Astrophysical Journal
Vera-Casanova A
(2022)
Linking the brightest stellar streams with the accretion history of Milky Way like galaxies
in Monthly Notices of the Royal Astronomical Society
Vera-Casanova A
(2022)
Linking the brightest stellar streams with the accretion history of Milky Way like galaxies
in Monthly Notices of the Royal Astronomical Society
Vidal J
(2020)
Efficiency of tidal dissipation in slowly rotating fully convective stars or planets
in Monthly Notices of the Royal Astronomical Society
Vidal J
(2020)
Turbulent Viscosity Acting on the Equilibrium Tidal Flow in Convective Stars
in The Astrophysical Journal Letters
Vijayan A
(2022)
First Light And Reionisation Epoch Simulations (FLARES) - III. The properties of massive dusty galaxies at cosmic dawn
in Monthly Notices of the Royal Astronomical Society
Vijayan A
(2020)
First Light And Reionisation Epoch Simulations (FLARES) II: The Photometric Properties of High-Redshift Galaxies
in Monthly Notices of the Royal Astronomical Society
Vincenzo F
(2019)
He abundances in disc galaxies I. Predictions from cosmological chemodynamical simulations
in Astronomy & Astrophysics
Vincenzo F
(2019)
Zoom-in cosmological hydrodynamical simulation of a star-forming barred, spiral galaxy at redshift z = 2
in Monthly Notices of the Royal Astronomical Society
Vizgan D
(2022)
Investigating the [C ii]-to-H i Conversion Factor and the H i Gas Budget of Galaxies at z ˜ 6 with Hydrodynamic Simulations
in The Astrophysical Journal Letters
Vizgan D
(2022)
Investigating the [C ii]-to-H i Conversion Factor and the H i Gas Budget of Galaxies at z ˜ 6 with Hydrodynamic Simulations
in The Astrophysical Journal Letters
Vlaykov D
(2022)
Impact of radial truncation on global 2D hydrodynamic simulations for a Sun-like model
in Monthly Notices of the Royal Astronomical Society
Vlaykov D
(2022)
Impact of radial truncation on global 2D hydrodynamic simulations for a Sun-like model
in Monthly Notices of the Royal Astronomical Society
Wakita S
(2022)
Effect of Impact Velocity and Angle on Deformational Heating and Postimpact Temperature
in Journal of Geophysical Research: Planets
Wakita S
(2022)
Effect of Impact Velocity and Angle on Deformational Heating and Postimpact Temperature
in Journal of Geophysical Research: Planets
Wang C
(2023)
Ghostly Galaxies: Accretion-dominated Stellar Systems in Low-mass Dark Matter Halos
in The Astrophysical Journal
Wang J
(2020)
Universal structure of dark matter haloes over a mass range of 20 orders of magnitude.
in Nature
Wang Y
(2020)
Iterative removal of redshift-space distortions from galaxy clustering
in Monthly Notices of the Royal Astronomical Society
Wang Z
(2022)
Superradiance in massive vector fields with spatially varying mass
in Physical Review D
Wareing C
(2021)
Striations, integrals, hourglasses, and collapse - thermal instability driven magnetic simulations of molecular clouds
in Monthly Notices of the Royal Astronomical Society
Wareing C
(2019)
Sheets, filaments, and clumps - high-resolution simulations of how the thermal instability can form molecular clouds
in Monthly Notices of the Royal Astronomical Society
Waterfall C
(2022)
Modeling the Transport of Relativistic Solar Protons along a Heliospheric Current Sheet during Historic GLE Events
in The Astrophysical Journal
Waterfall C
(2022)
Modeling the Transport of Relativistic Solar Protons along a Heliospheric Current Sheet during Historic GLE Events
in The Astrophysical Journal
Welsh L
(2021)
The stochastic enrichment of Population II stars
in Monthly Notices of the Royal Astronomical Society
Welsh L
(2022)
Oxygen-enhanced Extremely Metal-poor Damped Lya Systems: A Signpost of the First Stars?
in The Astrophysical Journal
Welsh L
(2022)
Oxygen-enhanced Extremely Metal-poor Damped Lya Systems: A Signpost of the First Stars?
in The Astrophysical Journal
Wen K
(2019)
Dissipation Dynamics of Nuclear Fusion Reactions
in Acta Physica Polonica B
White S
(2020)
The globular cluster system of the Auriga simulations
in Monthly Notices of the Royal Astronomical Society
Whitworth D
(2022)
Is the molecular KS relationship universal down to low metallicities?
in Monthly Notices of the Royal Astronomical Society
Whitworth D
(2022)
Is the molecular KS relationship universal down to low metallicities?
in Monthly Notices of the Royal Astronomical Society
Widdicombe J
(2020)
Black hole formation in relativistic Oscillaton collisions
in Journal of Cosmology and Astroparticle Physics
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
Wijers N
(2020)
The warm-hot circumgalactic medium around EAGLE-simulation galaxies and its detection prospects with X-ray and UV line absorption
in Monthly Notices of the Royal Astronomical Society
Wilkins S
(2023)
First light and reionization epoch simulations (FLARES) XI: [O iii ] emitting galaxies at 5 < z < 10
in Monthly Notices of the Royal Astronomical Society
Wilkins S
(2022)
First Light and Reionisation Epoch Simulations (FLARES) - VI. The colour evolution of galaxies z = 5-15
in Monthly Notices of the Royal Astronomical Society
Wilkins S
(2023)
First light and reionization epoch simulations (FLARES) V: the redshift frontier
in Monthly Notices of the Royal Astronomical Society
Wilkins S
(2023)
First Light And Reionization Epoch Simulations (FLARES) VII: The star formation and metal enrichment histories of galaxies in the early Universe
in Monthly Notices of the Royal Astronomical Society
Williams C
(2021)
ALMA Measures Rapidly Depleted Molecular Gas Reservoirs in Massive Quiescent Galaxies at z ~ 1.5
in The Astrophysical Journal
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
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
Witek H
(2020)
Towards numerical relativity in scalar Gauss-Bonnet gravity: 3 + 1 decomposition beyond the small-coupling limit
in Physical Review D
Witstok J
(2021)
Prospects for observing the low-density cosmic web in Lyman- a emission
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. |