Efficient Computing for Particle Physics (Lead Proposal)
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Physics and Astronomy
Abstract
STFC science projects are highly data-intensive, and depend crucially on high performance software and computing, as well as on instruments and facilities. Software is used for: control and operation of experiments; filtering and data reduction; signal processing and reconstruction; statistical data analysis; and experiment simulation. The high-luminosity LHC upgrade (HL-LHC) in 2026, will require greater computing performance than any other current science facility. At present, there is no means of meeting HL-LHC computing requirements within a reasonable funding envelope. In particular, the use of current software with a ten times larger dataset will not scale, since the "Moore's Law" decrease of computing costs with time has effectively ceased due to our inability to efficiently use modern computing platforms. It is clear that this is an urgent issue that must be addressed promptly, while there is still time to put in place the required new technologies. We will engage with industry and train ourselves in new computing architectures. We will identify the parts of our millions of lines of code that will benefit most strongly from adaptation to these new methods.
Planned Impact
This proposal will develop methods to yield greater scientific output per unit computing cost by both leveraging recent trends in hardware and through a comprehensive, cross-experiment approach to software optimisation. As a direct impact, this will result in a real-terms increase in the scientific sensitivity of HEP experiments and the direct societal impact of better value for money for public funds spent in science, not just for the HEP experiments to which the proposal investigators are affiliated, but to large-scale scientific infrastructure in general.
Over the next decade, several UKRI supported programs (SKA, LSST, DUNE, HL-LHC upgrades to name a few) will collect the largest datasets ever recorded in their respective fields, often by orders of magnitude. At the same time, the rate of increase of data processing capabilities for standard (x86) computing architectures is slowing. The consequences of improved software performance and the development of new software optimised for specialised (GPU, FPGA and ASIC) architectures stands to benefit all of the UKRI programs which are seeking to realise the potential of big data.
There are significant synergies with recent trends in industry, where FPGA-based machine learning solutions are in ascendancy. Microsoft's cloud computing platform has started to offer FPGA coprocessor options for this task (https://www.eetimes.com/document.asp?doc_id=1334741), incentivising current cloud users to develop for these performant and energy efficient technologies. This proposal will result in a roadmap by which we will train the future users of these technologies through the Centres for Doctoral Training (CDTs) specialising in data-intensive science, and through CASE studentships with industry partners. These training goals generate direct impact through industry placements and secondary impact as trained students graduate and transfer to industry. As we face the real challenges of climate change and urban pollution, there has been recent interest in real-time processing of big data relating to efficient use of resources (http://ximantis.com/). The intelligent use of big data to promote greener cities can only be realised with performant algorithms of the kind enabled by this proposal. We will involve industry in the UK workshops through existing links with universities and through CERN's OPENLAB, in addition to fostering new industry relations in the aforementioned areas.
Over the next decade, several UKRI supported programs (SKA, LSST, DUNE, HL-LHC upgrades to name a few) will collect the largest datasets ever recorded in their respective fields, often by orders of magnitude. At the same time, the rate of increase of data processing capabilities for standard (x86) computing architectures is slowing. The consequences of improved software performance and the development of new software optimised for specialised (GPU, FPGA and ASIC) architectures stands to benefit all of the UKRI programs which are seeking to realise the potential of big data.
There are significant synergies with recent trends in industry, where FPGA-based machine learning solutions are in ascendancy. Microsoft's cloud computing platform has started to offer FPGA coprocessor options for this task (https://www.eetimes.com/document.asp?doc_id=1334741), incentivising current cloud users to develop for these performant and energy efficient technologies. This proposal will result in a roadmap by which we will train the future users of these technologies through the Centres for Doctoral Training (CDTs) specialising in data-intensive science, and through CASE studentships with industry partners. These training goals generate direct impact through industry placements and secondary impact as trained students graduate and transfer to industry. As we face the real challenges of climate change and urban pollution, there has been recent interest in real-time processing of big data relating to efficient use of resources (http://ximantis.com/). The intelligent use of big data to promote greener cities can only be realised with performant algorithms of the kind enabled by this proposal. We will involve industry in the UK workshops through existing links with universities and through CERN's OPENLAB, in addition to fostering new industry relations in the aforementioned areas.
Publications
Aad G
(2023)
New techniques for jet calibration with the ATLAS detector
in The European Physical Journal C
Wiseman P
(2023)
Multiwavelength observations of the extraordinary accretion event AT2021lwx
in Monthly Notices of the Royal Astronomical Society
Aad G
(2023)
Search for exclusive Higgs and Z boson decays to ?? and Higgs boson decays to K?? with the ATLAS detector
in Physics Letters B
Aad G
(2023)
Search for pairs of muons with small displacements in pp collisions at s = 13 TeV with the ATLAS detector
in Physics Letters B
Aad G
(2023)
Search for heavy long-lived multi-charged particles in the full LHC Run 2 pp collision data at s = 13 TeV using the ATLAS detector
in Physics Letters B
Aad G
(2023)
Search for pair production of third-generation leptoquarks decaying into a bottom quark and a $$\tau $$-lepton with the ATLAS detector
in The European Physical Journal C
Aad G
(2023)
Evidence of off-shell Higgs boson production from ZZ leptonic decay channels and constraints on its total width with the ATLAS detector
in Physics Letters B
Aad G
(2023)
Measurement of Suppression of Large-Radius Jets and Its Dependence on Substructure in Pb+Pb Collisions at sqrt[s_{NN}]=5.02 TeV with the ATLAS Detector.
in Physical review letters
Aad G
(2023)
Search for a new pseudoscalar decaying into a pair of muons in events with a top-quark pair at s = 13 TeV with the ATLAS detector
in Physical Review D
Aad G
(2023)
Observation of Single-Top-Quark Production in Association with a Photon Using the ATLAS Detector.
in Physical review letters
Aad G
(2023)
Measurement of the Sensitivity of Two-Particle Correlations in p p Collisions to the Presence of Hard Scatterings
in Physical Review Letters
Aad G
(2023)
Measurement of the production of a W boson in association with a charmed hadron in p p collisions at s = 13 TeV with the ATLAS detector
in Physical Review D
Aad G
(2023)
Search for Dark Photons in Rare Z Boson Decays with the ATLAS Detector.
in Physical review letters
Aad G
(2023)
Measurements of the suppression and correlations of dijets in Xe+Xe collisions at s N N = 5.44 TeV
in Physical Review C
Aad G
(2023)
Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector.
in Physical review letters
Aad G
(2023)
Comparison of inclusive and photon-tagged jet suppression in 5.02 TeV Pb+Pb collisions with ATLAS
in Physics Letters B
Aad G
(2023)
Measurement of the Higgs boson mass with H ? ?? decays in 140 fb-1 of s = 13 TeV pp collisions with the ATLAS detector
in Physics Letters B
Aad G
(2023)
Pursuit of paired dijet resonances in the Run 2 dataset with ATLAS
in Physical Review D
Aad G
(2023)
Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
in Journal of Instrumentation
Aad G
(2023)
Search for Majorana neutrinos in same-sign WW scattering events from pp collisions at $$\sqrt{s}=13$$ TeV
in The European Physical Journal C
Aad G
(2023)
Observation of four-top-quark production in the multilepton final state with the ATLAS detector
in The European Physical Journal C
Aad G
(2023)
Performance of the reconstruction of large impact parameter tracks in the inner detector of ATLAS
in The European Physical Journal C
Aad G
(2024)
Evidence for the Higgs Boson Decay to a Z Boson and a Photon at the LHC.
in Physical review letters
Aad G
(2024)
Measurement of jet substructure in boosted t t ¯ events with the ATLAS detector using 140 fb - 1 of 13 TeV p p collisions
in Physical Review D
Aad G
(2024)
Calibration of a soft secondary vertex tagger using proton-proton collisions at s = 13 TeV with the ATLAS detector
in Physical Review D
Aad G
(2024)
Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector.
in Physical review letters
Aad G
(2024)
Search for Nearly Mass-Degenerate Higgsinos Using Low-Momentum Mildly Displaced Tracks in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector.
in Physical review letters
Aad G
(2024)
Beam-induced backgrounds measured in the ATLAS detector during local gas injection into the LHC beam vacuum
in Journal of Instrumentation
Aad G
(2024)
Search for non-resonant production of semi-visible jets using Run 2 data in ATLAS
in Physics Letters B
Aad G
(2024)
The ATLAS experiment at the CERN Large Hadron Collider: a description of the detector configuration for Run 3
in Journal of Instrumentation
Aad G
(2024)
Observation of W?? triboson production in proton-proton collisions at s = 13 TeV with the ATLAS detector
in Physics Letters B
Aad G
(2024)
The ATLAS trigger system for LHC Run 3 and trigger performance in 2022
in Journal of Instrumentation
ATLAS Collaboration
(2024)
Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb-1 of s=13 TeV pp collision data.
in Science bulletin
Aad G
(2024)
Disentangling Sources of Momentum Fluctuations in Xe + Xe and Pb + Pb Collisions with the ATLAS Detector
in Physical Review Letters
| Description | Efficient solutions to deliver computing for high energy physics were investigated. This was a pump-prime project that, with other work, led to Swift-HEP. |
| Exploitation Route | The partnerships with industry, the working group structure and the individual projects have all been taken forward. |
| Sectors | Digital/Communication/Information Technologies (including Software) |
| Description | ST/V002562/1 STFC Swift-HEP PPRP project |
| Amount | £1,500,000 (GBP) |
| Funding ID | ST/V002562/1 |
| Organisation | Science and Technologies Facilities Council (STFC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2021 |
| End | 04/2024 |
| Description | nVidia |
| Organisation | NVIDIA |
| Country | Global |
| Sector | Private |
| PI Contribution | Farrington and postdoc (vishwakarma) working with Nvidia and CERN on ATLAS EM calorimeter geometry adaptation for parallelised implementation. |
| Collaborator Contribution | Nvidia mentoring of Vishwakarma; training to ambassador status. |
| Impact | No outputs yet. |
| Start Year | 2019 |
| Description | ECHEP/Excalibur Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Workshop involving wider UK particle physics community addressing the need for modern software and computing solutions to deal with exa-scale datasets. Informed Swift-HEP proposal/collaboration. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://indico.cern.ch/event/928965/ |