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
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
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
Duguid C
(2020)
Convective turbulent viscosity acting on equilibrium tidal flows: new frequency scaling of the effective viscosity
in Monthly Notices of the Royal Astronomical Society
Dutta R
(2020)
MUSE Analysis of Gas around Galaxies (MAGG) - II: metal-enriched halo gas around z ~ 1 galaxies
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
Du Buisson L
(2020)
Cosmic rates of black hole mergers and pair-instability supernovae from chemically homogeneous binary evolution
in Monthly Notices of the Royal Astronomical Society
Dymott R
(2023)
Linear and non-linear properties of the Goldreich-Schubert-Fricke instability in stellar interiors with arbitrary local radial and latitudinal differential rotation
in Monthly Notices of the Royal Astronomical Society
Eager-Nash J
(2020)
Implications of different stellar spectra for the climate of tidally locked Earth-like exoplanets
in Astronomy & Astrophysics
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
Edwards B
(2020)
Hubble WFC3 Spectroscopy of the Habitable-zone Super-Earth LHS 1140 b
in The Astronomical Journal
Edwards B
(2023)
Characterizing a World Within the Hot-Neptune Desert: Transit Observations of LTT 9779 b with the Hubble Space Telescope/WFC3
in The Astronomical Journal
Edwards B
(2023)
Exploring the Ability of Hubble Space Telescope WFC3 G141 to Uncover Trends in Populations of Exoplanet Atmospheres through a Homogeneous Transmission Survey of 70 Gaseous Planets
in The Astrophysical Journal Supplement Series
Edwards B
(2020)
ARES I: WASP-76 b, A Tale of Two HST Spectra*
in The Astronomical Journal
Eke V
(2020)
Understanding the large inferred Einstein radii of observed low-mass galaxy clusters
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
Elsender D
(2023)
On the frequencies of circumbinary discs in protostellar systems
in Monthly Notices of the Royal Astronomical Society
Elsender D
(2021)
The statistical properties of protostellar discs and their dependence on metallicity
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
Elvis M
(2020)
Q wind code release: a non-hydrodynamical approach to modelling line-driven winds in active galactic nuclei
in Monthly Notices of the Royal Astronomical Society
Etherington A
(2022)
Automated galaxy-galaxy strong lens modelling: No lens left behind
in Monthly Notices of the Royal Astronomical Society
Evans T
(2020)
How unusual is the Milky Way's assembly history?
in Monthly Notices of the Royal Astronomical Society
Evstafyeva T
(2023)
Unequal-mass boson-star binaries: initial data and merger dynamics
in Classical and Quantum Gravity
Evstafyeva T
(2023)
Boson stars in massless and massive scalar-tensor gravity
in Physical Review D
Falck B
(2021)
Indra: a public computationally accessible suite of cosmological N -body simulations
in Monthly Notices of the Royal Astronomical Society
Falle S
(2020)
Thermal instability revisited
in Monthly Notices of the Royal Astronomical Society
Fancher J
(2023)
On the relative importance of shocks and self-gravity in modifying tidal disruption event debris streams
in Monthly Notices of the Royal Astronomical Society
Fattahi A
(2020)
A tale of two populations: surviving and destroyed dwarf galaxies and the build-up of the Milky Way's stellar halo
in Monthly Notices of the Royal Astronomical Society
Fenton A
(2024)
The 3D structure of disc-instability protoplanets
in Astronomy & Astrophysics
Figueras P
(2023)
Endpoint of the Gregory-Laflamme instability of black strings revisited
in Physical Review D
Figueras P
(2020)
Gravitational collapse in cubic Horndeski theories
in Classical and Quantum Gravity
Figueras P
(2022)
Black hole binaries in cubic Horndeski theories
in Physical Review D
Fiteni K
(2021)
The relative efficiencies of bars and clumps in driving disc stars to retrograde motion
in Monthly Notices of the Royal Astronomical Society
Flynn J
(2023)
Exclusive semileptonic B s ? K l ? decays on the lattice
in Physical Review D
Font A
(2020)
The artemis simulations: stellar haloes of Milky Way-mass galaxies
in Monthly Notices of the Royal Astronomical Society
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
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
(2020)
Modeling MMS Observations at the Earth's Magnetopause with Hybrid Simulations of Alfvénic Turbulence
in The Astrophysical Journal
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
(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
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
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 |