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
Sedda M
(2020)
The missing link in gravitational-wave astronomy: discoveries waiting in the decihertz range
in Classical and Quantum Gravity
Figueras P
(2020)
Gravitational collapse in cubic Horndeski theories
in Classical and Quantum Gravity
Pandya A
(2022)
Dynamics of a nonminimally coupled scalar field in asymptotically AdS 4 spacetime
in Classical and Quantum Gravity
Clough K
(2021)
Continuity equations for general matter: applications in numerical relativity
in Classical and Quantum Gravity
Evstafyeva T
(2023)
Unequal-mass boson-star binaries: initial data and merger dynamics
in Classical and Quantum Gravity
Helfer T
(2022)
Malaise and remedy of binary boson-star initial data
in Classical and Quantum Gravity
Croft R
(2023)
The gravitational afterglow of boson stars
in Classical and Quantum Gravity
Ripley J
(2022)
Computing the quasinormal modes and eigenfunctions for the Teukolsky equation using horizon penetrating, hyperboloidally compactified coordinates
in Classical and Quantum Gravity
Cardoso V
(2023)
Curvature and dynamical spacetimes: can we peer into the quantum regime?
in Classical and Quantum Gravity
Gerosa D
(2022)
The irreducible mass and the horizon area of LIGO's black holes
in Classical and Quantum Gravity
Radia M
(2022)
Lessons for adaptive mesh refinement in numerical relativity
in Classical and Quantum Gravity
Adamek J
(2020)
Numerical solutions to Einstein's equations in a shearing-dust universe: a code comparison
in Classical and Quantum Gravity
Aurrekoetxea J
(2020)
Coherent gravitational waveforms and memory from cosmic string loops
in Classical and Quantum Gravity
Skullerud J
(2022)
Hadrons at high temperature: An update from the FASTSUM collaboration
in EPJ Web of Conferences
Cossu G
(2018)
Testing algorithms for critical slowing down
in EPJ Web of Conferences
Boyle P
(2018)
| V us | determination from inclusive strange tau decay and lattice HVP
in EPJ Web of Conferences
Lawlor D
(2022)
Thermal Transitions in Dense Two-Colour QCD
in EPJ Web of Conferences
Boyle P
(2018)
Isospin Breaking Corrections to the HVP with Domain Wall Fermions
in EPJ Web of Conferences
Spriggs T
(2022)
A comparison of spectral reconstruction methods applied to non-zero temperature NRQCD meson correlation functions
in EPJ Web of Conferences
Di Carlo M
(2022)
Electromagnetic finite-size effects beyond the point-like approximation
in EPJ Web of Conferences
Attanasio F
(2022)
Equation of state from complex Langevin simulations
in EPJ Web of Conferences
Changeat Q
(2022)
Disentangling atmospheric compositions of K2-18 b with next generation facilities.
in Experimental astronomy
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
Trotta D
(2022)
Single-spacecraft techniques for shock parameters estimation: A systematic approach
in Frontiers in Astronomy and Space Sciences
Tinoco-Arenas A
(2022)
Parametric Study of Magnetosheath Jets in 2D Local Hybrid Simulations
in Frontiers in Astronomy and Space Sciences
Stevenson P
(2022)
Mean-field simulations of Es-254 + Ca-48 heavy-ion reactions
in Frontiers in Physics
Barausse E
(2020)
Prospects for fundamental physics with LISA
in General Relativity and Gravitation
Mason S
(2022)
Magnetoconvection in a rotating spherical shell in the presence of a uniform axial magnetic field
in Geophysical & Astrophysical Fluid Dynamics
Gupta P
(2022)
A study of global magnetic helicity in self-consistent spherical dynamos
in Geophysical & Astrophysical Fluid Dynamics
Davison T
(2022)
Complex Crater Formation by Oblique Impacts on the Earth and Moon
in Geophysical Research Letters
Read P
(2020)
The turbulent dynamics of Jupiter's and Saturn's weather layers: order out of chaos?
in Geoscience Letters
Sergeev D
(2023)
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
in Geoscientific Model Development
Raducan S
(2022)
Ejecta distribution and momentum transfer from oblique impacts on asteroid surfaces
in Icarus
Halim S
(2021)
Assessing the survivability of biomarkers within terrestrial material impacting the lunar surface
in Icarus
Young R
(2019)
Simulating Jupiter's weather layer. Part II: Passive ammonia and water cycles
in Icarus
Bartlett D
(2024)
Exhaustive Symbolic Regression
in IEEE Transactions on Evolutionary Computation
Hardy F
(2023)
Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning
in International Journal of Data Science and Analytics
Heyl J
(2023)
Data quality and autism: Issues and potential impacts
in International Journal of Medical Informatics
Stevenson P
(2020)
Internuclear potentials from the Sky3D code
in IOP SciNotes
De Jong E
(2023)
Spinning primordial black holes formed during a matter-dominated era
in Journal of Cosmology and Astroparticle Physics
De Jong E
(2022)
Primordial black hole formation with full numerical relativity
in Journal of Cosmology and Astroparticle Physics
Pedersen C
(2020)
Massive neutrinos and degeneracies in Lyman-alpha forest simulations
in Journal of Cosmology and Astroparticle Physics
Nazari Z
(2021)
Oscillon collapse to black holes
in Journal of Cosmology and Astroparticle Physics
Srinivasan S
(2021)
Cosmological gravity on all scales. Part II. Model independent modified gravity N-body simulations
in Journal of Cosmology and Astroparticle Physics
Aurrekoetxea J
(2020)
The effects of potential shape on inhomogeneous inflation
in Journal of Cosmology and Astroparticle Physics
Macpherson H
(2023)
Cosmological distances with general-relativistic ray tracing: framework and comparison to cosmographic predictions
in Journal of Cosmology and Astroparticle Physics
Barrera-Hinojosa C
(2020)
GRAMSES: a new route to general relativistic N -body simulations in cosmology. Part II. Initial conditions
in Journal of Cosmology and Astroparticle Physics
Pedersen C
(2021)
An emulator for the Lyman-a forest in beyond-?CDM cosmologies
in Journal of Cosmology and Astroparticle Physics
Bozorgnia N
(2020)
The dark matter component of the Gaia radially anisotropic substructure
in Journal of Cosmology and Astroparticle Physics
Givans J
(2022)
Non-linearities in the Lyman-a forest and in its cross-correlation with dark matter halos
in Journal of Cosmology and Astroparticle Physics
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 |