Software development support for DiRAC
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Physics and Astronomy
Abstract
We propose to partially mitigate the shortfall in projected DiRAC computing resources over the next year, arising from the delay to DiRAC-3, by continuing a code optimisation effort to accelerate DiRAC's scientific codes on current architectures, and which will also give a sustained long term benefit to scientific throughput on future machines. The effort will be used to provide profiling, benchmarking and optimisation services to maximise scientific return from the DiRAC by ensuring the most efficient use of computing resources possible. This represents continuation of a successful first year of such effort supported by the DiRAC Technical Working Group grant and with additional support from the University of Edinburgh. The first year of effort focused on the representation of the DiRAC workload as benchmarks to assess future supercomputing needs, and there have been significant benefits to the community arising from this activity. These included discovering vectorisation, caching optimisation, and MPI parallelisation strategies for codes of the VIRGO, UKQCD, UKMHD and Planck cosmology collaborations as examples.
DiRAC contains some areas of key international strength in high performance software engineering. DiRAC's expertise has been the foundation upon which the Alan Turing Institute has secured an internationally unique strategic partnership with Intel's HPC architecture group. There is an good opportunity for STFC to enable the rest of the DiRAC community to exploit the knowledge base.
DiRAC contains some areas of key international strength in high performance software engineering. DiRAC's expertise has been the foundation upon which the Alan Turing Institute has secured an internationally unique strategic partnership with Intel's HPC architecture group. There is an good opportunity for STFC to enable the rest of the DiRAC community to exploit the knowledge base.
Planned Impact
The proposed work will disseminate best practice in High Performance Computing software engineering throughout the theoretical Particle Physics, Astronomy and Nuclear physics communities in the UK. This will encourage the development of skills in the research community that are highly transferrable to the computations required for simulations in advanced manufacturing and computer aided design. Some level of the young researchers in the community leave academia each year as a natural part of the turnover that occurs in university sector research, especially given the limited number of research positions. The transfer of a portion of the research communities to other jobs carrying these skills is a key element that feeds UK industry from the space and satellite sector, through hard engineering and the financial sectors.
The DIRAC project has close ties with the highest levels of research and development in the computing industry. We designed a key component on the IBM BlueGene/Q system, as part of a unique Academic-Industrial joint project with IBM Watson Laboratory. The design has powered leading scientific computing installations around the world, from laboratories in the USA, Japan, Italy and Germany to our own national Hartree centre.
More recently DiRAC has been competitively awarded three Intel Parallel Computing Centres, won several international supercomputing awards, and developed a close codesign project with Intel on future HPC architectures. This latter effort promises the ability to influence a vast swathe of modern computing over the next five years. Such improvements in computing would impact all consumers of computional hardware, and in particular those doing numerical simulation such as the advanced manufacturing sector (e.g. Rolls Royce, BAE, Mclaren, Shell, Jaguar Land Rover), in addition to much of academic research in the physical sciences.
The Intel-Alan Turing Institute strategic partnership, with an HPC architecture team embedded in the ATI is built on this foundation of a deep collaboration with theoretical particle physicists. DiRAC's technical director is the codesign leader for the ATI and will transfer DiRAC's best practice codesign techniques to a number of other subject domains covering HPC and Data Science.
The Alan Turing Insitute, with DiRAC's codesign leader playing a central role is actively engaging with external partners, such as Intel, the MET office, Shell, and even Mclaren Racing, propagating vectorisation techniques developed in QCD codes to the important area of Finite Elements Modelling.
The present proposal provides resources for the theoretical particle physics, astronomy and nuclear physics communities to interact with the codesign knowledge centre. The opportunity to influence with sensible engineering decisions that optimise codes for products and products for codes is real. One example is a recurring element of computer architecture involves the tradeoff between throughput and accuracy of vectorised reciprocal square root instructions. These are a key element of the inverse square law that dominates the gravitational element of astronomy simulations. We have the opportunity to give definitive statements about the right balance to Intel.
Similarly we hope to provide useful information on the requirements for data motion, from the complexities of cache organisation and algorithms to interconnect requirements. Nowhere is this more pressing than addressing the enormous challenges presented by the Square Kilometre Array, perhaps a leading Big Data problem in the near future of scientific endeavour.
Our work can also lead to cross fertilisation into the nascent fields of machine learning and data science through the Alan Turing Institute.
The DIRAC project has close ties with the highest levels of research and development in the computing industry. We designed a key component on the IBM BlueGene/Q system, as part of a unique Academic-Industrial joint project with IBM Watson Laboratory. The design has powered leading scientific computing installations around the world, from laboratories in the USA, Japan, Italy and Germany to our own national Hartree centre.
More recently DiRAC has been competitively awarded three Intel Parallel Computing Centres, won several international supercomputing awards, and developed a close codesign project with Intel on future HPC architectures. This latter effort promises the ability to influence a vast swathe of modern computing over the next five years. Such improvements in computing would impact all consumers of computional hardware, and in particular those doing numerical simulation such as the advanced manufacturing sector (e.g. Rolls Royce, BAE, Mclaren, Shell, Jaguar Land Rover), in addition to much of academic research in the physical sciences.
The Intel-Alan Turing Institute strategic partnership, with an HPC architecture team embedded in the ATI is built on this foundation of a deep collaboration with theoretical particle physicists. DiRAC's technical director is the codesign leader for the ATI and will transfer DiRAC's best practice codesign techniques to a number of other subject domains covering HPC and Data Science.
The Alan Turing Insitute, with DiRAC's codesign leader playing a central role is actively engaging with external partners, such as Intel, the MET office, Shell, and even Mclaren Racing, propagating vectorisation techniques developed in QCD codes to the important area of Finite Elements Modelling.
The present proposal provides resources for the theoretical particle physics, astronomy and nuclear physics communities to interact with the codesign knowledge centre. The opportunity to influence with sensible engineering decisions that optimise codes for products and products for codes is real. One example is a recurring element of computer architecture involves the tradeoff between throughput and accuracy of vectorised reciprocal square root instructions. These are a key element of the inverse square law that dominates the gravitational element of astronomy simulations. We have the opportunity to give definitive statements about the right balance to Intel.
Similarly we hope to provide useful information on the requirements for data motion, from the complexities of cache organisation and algorithms to interconnect requirements. Nowhere is this more pressing than addressing the enormous challenges presented by the Square Kilometre Array, perhaps a leading Big Data problem in the near future of scientific endeavour.
Our work can also lead to cross fertilisation into the nascent fields of machine learning and data science through the Alan Turing Institute.
Publications
Zhu Y
(2022)
Long Dark Gaps in the Lyß Forest at z < 6: Evidence of Ultra-late Reionization from XQR-30 Spectra
in The Astrophysical Journal
Keating L
(2020)
Long troughs in the Lyman-a forest below redshift 6 due to islands of neutral hydrogen
in Monthly Notices of the Royal Astronomical Society
Barrera-Hinojosa C
(2022)
Looking for a twist: probing the cosmological gravitomagnetic effect via weak lensing-kSZ cross-correlations
in Monthly Notices of the Royal Astronomical Society
Gurung-López S
(2019)
Lya emitters in a cosmological volume II: the impact of the intergalactic medium
in Monthly Notices of the Royal Astronomical Society
Betts J
(2023)
Machine learning and structure formation in modified gravity
in Monthly Notices of the Royal Astronomical Society
Cossu G
(2019)
Machine learning determination of dynamical parameters: The Ising model case
in Physical Review B
Santos-Santos I
(2021)
Magellanic satellites in ?CDM cosmological hydrodynamical simulations of the Local Group
in Monthly Notices of the Royal Astronomical Society
Matteini L
(2020)
Magnetic Field Turbulence in the Solar Wind at Sub-ion Scales: In Situ Observations and Numerical Simulations
in Frontiers in Astronomy and Space Sciences
Matsumoto J
(2021)
Magnetic inhibition of the recollimation instability in relativistic jets
in Monthly Notices of the Royal Astronomical Society
Howson T
(2021)
Magnetic reconnection and the Kelvin-Helmholtz instability in the solar corona
in Astronomy & Astrophysics
Mason S
(2022)
Magnetoconvection in a rotating spherical shell in the presence of a uniform axial magnetic field
in Geophysical & Astrophysical Fluid Dynamics
Armijo J
(2022)
Making use of sub-resolution haloes in N -body simulations
in Monthly Notices of the Royal Astronomical Society: Letters
Helfer T
(2022)
Malaise and remedy of binary boson-star initial data
in Classical and Quantum Gravity
Appleby S
(2023)
Mapping circumgalactic medium observations to theory using machine learning
in Monthly Notices of the Royal Astronomical Society
Robertson A
(2020)
Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data
in Monthly Notices of the Royal Astronomical Society
Bartlett D
(2023)
Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors
in The Open Journal of Astrophysics
Aviles A
(2020)
Marked correlation functions in perturbation theory
in Journal of Cosmology and Astroparticle Physics
Mellor T
(2023)
MARVEL analysis of high-resolution spectra of thioformaldehyde (H 2 CS)
in Journal of Molecular Spectroscopy
Pimpanuwat B
(2020)
Maser flares driven by variations in pumping and background radiation
in Monthly Notices of the Royal Astronomical Society
Owen J
(2020)
Massive discs around low-mass stars
in Monthly Notices of the Royal Astronomical Society
Trayford J
(2020)
Massive low-surface-brightness galaxies in the eagle simulation
in Monthly Notices of the Royal Astronomical Society
Pedersen C
(2020)
Massive neutrinos and degeneracies in Lyman-alpha forest simulations
in Journal of Cosmology and Astroparticle Physics
Stevenson P
(2022)
Mean-field simulations of Es-254 + Ca-48 heavy-ion reactions
in Frontiers in Physics
Maitra S
(2022)
Measurement of redshift-space two- and three-point correlation of Lya absorbers at 1.7 < z < 3.5: implications on evolution of the physical properties of IGM
in Monthly Notices of the Royal Astronomical Society
Elson E
(2023)
Measurements of the angular momentum-mass relations in the Simba simulation
in New Astronomy
Hernández-Aguayo C
(2020)
Measuring the baryon acoustic oscillation peak position with different galaxy selections
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
Garzilli A
(2020)
Measuring the temperature and profiles of Ly a absorbers
in Monthly Notices of the Royal Astronomical Society
Edwards B
(2024)
Measuring Tracers of Planet Formation in the Atmosphere of WASP-77A b: Substellar O/H and C/H Ratios, with a Stellar C/O Ratio and a Potentially Superstellar Ti/H Ratio
in The Astrophysical Journal Letters
Srisawat C
(2020)
MEGA: Merger graphs of structure formation
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
Nikolaev A
(2020)
Mesonic correlators at non-zero baryon chemical potential
Guo Y
(2020)
Metal Enrichment in the Circumgalactic Medium and Lya Halos around Quasars at z ~ 3
in The Astrophysical Journal
Dutta R
(2021)
Metal-enriched halo gas across galaxy overdensities over the last 10 billion years
in Monthly Notices of the Royal Astronomical Society
Gronow S
(2021)
Metallicity-dependent nucleosynthetic yields of Type Ia supernovae originating from double detonations of sub- M Ch white dwarfs
in Astronomy & Astrophysics
Lega E
(2022)
Migration of Jupiter mass planets in discs with laminar accretion flows
in Astronomy & Astrophysics
Lega E
(2021)
Migration of Jupiter-mass planets in low-viscosity discs
in Astronomy & Astrophysics
Salvioni G
(2020)
Model nuclear energy density functionals derived from ab initio calculations
in Journal of Physics G: Nuclear and Particle Physics
Hamilton E
(2021)
Model of gravitational waves from precessing black-hole binaries through merger and ringdown
in Physical Review D
Franci L
(2020)
Modeling MMS Observations at the Earth's Magnetopause with Hybrid Simulations of Alfvénic Turbulence
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
Baugh C
(2022)
Modelling emission lines in star-forming galaxies
in Monthly Notices of the Royal Astronomical Society
Suarez T
(2021)
Modelling intergalactic low ionization metal absorption line systems near the epoch of reionization
in Monthly Notices of the Royal Astronomical Society
Hutchinson A
(2023)
Modelling shock-like injections of solar energetic particles with 3D test particle simulations
in Astronomy & Astrophysics
Shingles L
(2022)
Modelling the ionization state of Type Ia supernovae in the nebular phase
in Monthly Notices of the Royal Astronomical Society
Clark VHJ
(2021)
Modelling the non-local thermodynamic equilibrium spectra of silylene (SiH2).
in Physical chemistry chemical physics : PCCP
Manzoni G
(2021)
Modelling the quenching of star formation activity from the evolution of the colour-magnitude relation in VIPERS
in New Astronomy
He J
(2020)
Modelling the tightest relation between galaxy properties and dark matter halo properties from hydrodynamical simulations of galaxy formation
in Monthly Notices of the Royal Astronomical Society
Rouillard A
(2020)
Models and data analysis tools for the Solar Orbiter mission
in Astronomy & Astrophysics
| Description | DiRAC RSE's were hired by Intel and work with DiRAC team members to analyse the requirements of machine learning. We discovered the IEEE FP16 is not optimal for machine learning and that a new floating point format Bfloat16 is more effective. We coauthored a patent application with Intel |
| Exploitation Route | We have demonstrated to Intel and coauthored a patent. It is being released as part of Intel's Cooperlake architecture for broad use. |
| Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Electronics Financial Services and Management Consultancy Transport Other |
| URL | http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=0&f=S&l=50&TERM1=Boyle&FIELD1=IN&co1=AND&TERM2=Kashyap&FIELD2=IN&d=PG01 |
| Description | Our software helped Intel improve their driver software for omnipath networks. DiRAC RSE's were hired by Intel to work with the Turing institute on machine learning. |
| First Year Of Impact | 2018 |
| Sector | Digital/Communication/Information Technologies (including Software),Electronics |
| Impact Types | Economic |
| Description | Intel ATI codesign project |
| Organisation | Intel Corporation |
| Department | Intel Corporation (Jones Farm) |
| Country | United States |
| Sector | Private |
| PI Contribution | I lead the Alan Turing Institute / Intel codesign project. Two Intel engineers are placed in Edinburgh to work with me. We have developed profiling tools for machine learning packages and led to insight into the architectural requirements of deep learning that have been propagated to Intel. The tool has been used to study reduced precision floating point formats, and specific new instruction set extensions have been proposed to Intel. |
| Collaborator Contribution | Two Intel engineers are placed in Edinburgh to work with me. We have developed profiling tools for machine learning packages and led to insight into the architectural requirements of deep learning that have been propagated to Intel. The tool has been used to study reduced precision floating point formats, and specific new instruction set extensions have been proposed to Intel. |
| Impact | Paper with Intel MPI team. Multidisciplinary, particle physics, computing science and electronic engineering. |
| Start Year | 2016 |
| 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. |
| Description | Panel discussion on machine learning and future HPC Intel HPC developer conference. |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Invited as panel expert on future of HPC and machine learning by Intel at their annual HPC developer conference attended widely by Industry and research lab sector. Note, Boyle second from left in photograph on the Intel web page linked below. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.intel.com/content/www/us/en/events/hpcdevcon/overview.html |
| Description | Talk on MPI optimisation on Intel stand at Supercomputing 2017 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Decision influence: I Influenced Intel to modify, update and release optimisations to their MPI library for the Intel Omnipath interconnect. Coauthored a paper on this topic. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://inspirehep.net/record/1636204 |
| Description | Talks presented on this activity at Intel Xeon Phi User Group conferences. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Presented work in several Intel Xeon Phi User Group meetings. |
| Year(s) Of Engagement Activity | 2016,2017 |