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Details of Grant 

EPSRC Reference: EP/V001329/1
Title: Lattice Field Theory at the Exascale Frontier
Principal Investigator: Del Debbio, Professor L
Other Investigators:
Researcher Co-Investigators:
Project Partners:
ARM Ltd Numerical Algorithms Group Ltd nVIDIA
Department: Sch of Physics and Astronomy
Organisation: University of Edinburgh
Scheme: Standard Research - NR1
Starts: 01 April 2020 Ends: 30 June 2021 Value (£): 93,898
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
EP/V001191/1 EP/V001477/1 EP/V001248/1 EP/V001140/1
Panel History:
Panel DatePanel NameOutcome
04 Mar 2020 Software use code development for exascale computing Announced
Summary on Grant Application Form
Lattice Field Theory (LFT) provides the tools to study the fundamental forces of nature using numerical simulations. The traditional realm of application of LFT has been Quantum Chromodynamics (QCD), the theory describing the strong nuclear force within the Standard Model (SM) of particle physics. These calculations now include electromagnetic effects and achieve sub percent accuracy. Other applications span a wide range of topics, from theories beyond the Standard Model, to low-dimensional strongly coupled fermionic models, to new cosmological paradigms. At the core of this scientific endeavour lies the ability to perform sophisticated and demanding numerical simulations. The Exascale era of High Performance Computing therefore looks like a time of great opportunities.

The UK LFT community has been at the forefront of the field for more than three decades and has developed a broad portfolio of research areas, with synergetic connections to High-Performance Computing, leading to significant progress in algorithms and code performance.

Highlights of successes include: influencing the design of new hardware (Blue Gene systems); developing algorithms (Hybrid Monte Carlo) that are used widely by many other communities; maximising the benefits from new technologies (lattice QCD practitioners were amongst the first users of new platforms, including GPUs for scientific computing); applying LFT techniques to new problems in Artificial Intelligence.

The research programme in LFT, and its impact, can be expanded in a transformative way with the advent of pre-Exascale and Exascale systems, but only if key challenges are addressed. As the number of floating point operations per second increases, the communications between computing nodes are lagging behind, and this imbalance will severely affect future LFT simulations across the board.

These challenges are common to all LFT codebases, and more generally to other communities that are large users of HPC resources. The bottlenecks on new architectures need to be carefully identified, and software that minimises the communications must be designed in order to make the best usage of forthcoming large computers. As we are entering an era of heterogeneous architectures, the design of new software must clearly isolate the algorithmic progress from the details of the implementation on disparate hardware, so that our software can be deployed efficiently on forthcoming machines with limited effort.

The goal of the EXA-LAT project is to develop a common set of best practices, KPIs and figures of merit that can be used by the whole LFT community in the near future and will inform the design and procurement of future systems. Besides the participation of the LFT community, numerous vendors and computing centres have joined the project, together with scholars from 'neighbouring' disciplines. Thereby we aim to create a national and international focal point that will foster the activity of scholars, industrial partners and Research Sotfware Engineers (RSEs). This synergetic environment will host training events for academics, RSEs and students, which will contribute to the creation of a skilled work force immersed in a network that comprises the leading vendors in the subject.

EXA-LAT will set the foundations for a long-term effort by the LFT community to fully benefit of Exascale facilities and transfer some of the skills that characterise our scientific work to a wider group of users across disciplines.

Key Findings
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