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This working group will focus on solutions to gain orders of magnitude for tackling simulations at scales which are relevant for real life applications. Below are the objectives to achieve this

Driver

nicolin Hongyang Cheng Deepak Tunuguntla

Approver

Daniel Barreto

Contributors

nicolin Hongyang Cheng Deepak Tunuguntla

Informed

ObjectiveObjectives

  1. High-Performance Computing: Leveraging new hardware architectures and parallelization parallelisation techniques (CPU and GPU) to overcome challenges in simulating non-spherical particles, load balancing, and contact algorithms.

  2. Model-Based Solutions: Developing enriched contact models and utilizing spatial and temporal periodicities to optimize particle usage and time steps.

  3. Exploring Additional Alternative Techniques: Investigating other innovative methods, including machine-learning and model order reduction approaches, to further improve simulation efficiency.

Due date

Key outcomes

D1.1. (M9)–Short list of academic reference problems (including their full definition in terms of initial configuration, boundary condition, time step, expected results, and/or analytical solutions, etc.). The first list relates to problems that may be tackled by considering various hardware architectures, the second one to model-based challenges. Lists will be compiled as part of a short technical report and made available via the wiki and repositories.

D1.2. (M12, M24, M36 and M48) – Implementation of hardware- and model-based short-list problems using at least two open-source DEM codes to give a comprehensive comparison of the different investigated solutions. Two individual problems will be considered each year (i.e. one hardware-based, another one model-based) and summarised in individual technical reports (M12, M24, M36 and M48). These will also be made available as individual datasets fully documented and shared via repositories.

D1.3. (M48) – Two journal papers considering the conclusions and findings related to (i) hardware-based solutions and (ii) model-based solutions, respectively.

Status

Status
colourYellow
titlein progress

\uD83E\uDD14 Problem Statement

This working group will focus on solutions to gain orders of magnitude for tackling simulations at scales which are relevant for real life applications. Below are three approaches to achieve this:

  1. Using high-performance computing and taking benefits of new hardware architectures.

    • Well-known solutions such as parallelisation with both CPU and GPU will be investigated.

    The efficient

    • Efficient use of these solutions in particular contexts is still challenging. For instance, simulating non-spherical particles with GPU-based algorithm, handling load balancing on CPU parallel architecture for simulations or parallelising contact algorithms such as Voronoi based searching remains difficult and are still scientific barriers.

    The potential

    • Potential participation of hardware manufacturers will enable new insights for the optimisation of DEM algorithms.

  2. Using model-based solutions

    These could be

    1. enriched contact models;

    2. space periodicity; and/or time extrapolation strategies.

The first approach consists in enriching the contact model in order to do more with fewer particles. The second and third approaches take advantage of temporal and spatial periodicities to use less particles or time steps.

  1. Of course, other promising solutions which are not listed here may be investigated such as machine-learning based techniquesAlternative methods Investigating other innovative methods, including machine-learning and model order reduction approaches, to further improve simulation efficiency.

🎯 Scope

Must have:

  • create Create a detailed list of reference problems related to both hardware and model-based challenges, including their full definitions and available via wiki and repositories.

  • execute Execute two open-source DEM code implementations each year, focusing on one hardware-based and one model-based problem, culminating in documented technical reports and datasets.

  • produce Produce two journal papers by the end of the project, summarizing the conclusions from hardware-based and model-based solutions.

Nice to have:

  • engage Engage with hardware manufacturers to gain insights into optimizing DEM algorithms.

  • investigate Investigate additional promising solutions, such as machine-learning techniques, to enhance simulation capabilities.

Not in scope:

\uD83D\uDDD3 Timeline

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\uD83D\uDEA9 Hardware Resources For WG1

Name

CPU/GPU

Threads

Linux/Win

University

Hours/Days

\uD83D\uDEA9 Milestones and deadlines

Milestone

Owner

Deadline

Status

\uD83D\uDD17 Reference materials

Important Pages

/wiki/spaces/WG1/pages/26312728

💻 How to contribute?

🚩 Milestones and deliverables

Milestone

Deliverable

D1.1 (M9)

Important Pages

👋 Meet the team

Image RemovedNicolin GovenderGovender1.pngImage Added

WG1 & DEM-HPC sub-group lead

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Hongyang Cheng

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WG1 co-lead & DEM-UP sub-group lead

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Deepak Tunuguntla

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WG1 co-lead & DEM-ML sub-group lead