Towards a more sustainable High Performance Computing sector: a hardware/software co-design proof-of-concept
Lead Research Organisation:
IMPERIAL COLLEGE LONDON
Department Name: Aeronautics
Abstract
The information and communication technologies (ICT) sector has become an inextricable part of our modern world, from supporting basic day-to-day tasks to performing complex simulations and calculations that are essential to scientific research, engineering, security, control and other fields. High-Performance Computing (HPC) systems that are capable of solving hugely complex and demanding problems with high computational power, are critical in the development of leading-edge global scientific and engineering projects currently underway in the commercial, academic and government spaces, spurring discoveries and innovations. With the rise of artificial intelligence and big data, it is inevitable that the percentage of global power consumption from HPC systems will grow rapidly and is going to be a major challenge in the future. As such, there is growing interest in how the expansion of HPC systems can be delivered in a manner that is environmentally sustainable and in line with the government's pledge for a net zero society.
This project will investigate how to exploit configurable and customisable processing technologies to design more sustainable HPC systems. The team of investigators will use a co-design approach, tuning processing hardware and software in collaboration to optimise HPC systems for both performance and energy efficiency. The proposed approach will be used to look at how to improve the performance and sustainability of wind energy systems, which in turn can be used to power HPC systems, further reducing the environmental impact of these systems. This is our primary research objective. For this exercise, we will dramatically reduce the environmental impact of Xcompact3d, an open-source software designed to study numerically fluid flows. It is currently among the most used softwares on ARCHER2, the UK supercomputing service. We will focus on Xcompact3d's wind farm simulator, a tool which can faithfully replicate virtually realistic scenarios encounter by modern wind farms. It will be re-designed with a hardware-software co-design approach. We will focus on three hardware architectures; 1) Field Programmable Gate Arrays (FPGAs), 2) Coarse Grained Reconfigurable Architectures (CGRAs), 3) RISC-V.
With FPGAs we are able to undertake the design of low-level bespoke memory access designs or algorithm level concurrency, with CGRAs to explore different approaches for mapping algorithms to the hardware and communication, and
RISC-V to experiment with different CPU and accelerator designs and their configurations. With support from the vendors, all these experiments will result in actionable results that will be use to further direct the research, but also for the vendors to understand how to best enhance their products for the HPC community.
The team of investigators will also gain insights into sustainability efforts in HPC with an in-depth study focusing on possible sustainability and net zero strategies. One of the overarching goals of the project is to provide a road-map for a more sustainable HPC landscape, and transfer the outcomes of the project to policy-making for a wide range of academic, government and industry stakeholders.
The project team is composed of experts from a variety of fields, including high performance computing, computer science, programming, computational fluid dynamics, innovation and sustainability, and policy-making. This interdisciplinary team will be able to bring a wide range of expertise to bear on the challenges of developing and utilising more energy and resource efficient HPC systems.
This project will investigate how to exploit configurable and customisable processing technologies to design more sustainable HPC systems. The team of investigators will use a co-design approach, tuning processing hardware and software in collaboration to optimise HPC systems for both performance and energy efficiency. The proposed approach will be used to look at how to improve the performance and sustainability of wind energy systems, which in turn can be used to power HPC systems, further reducing the environmental impact of these systems. This is our primary research objective. For this exercise, we will dramatically reduce the environmental impact of Xcompact3d, an open-source software designed to study numerically fluid flows. It is currently among the most used softwares on ARCHER2, the UK supercomputing service. We will focus on Xcompact3d's wind farm simulator, a tool which can faithfully replicate virtually realistic scenarios encounter by modern wind farms. It will be re-designed with a hardware-software co-design approach. We will focus on three hardware architectures; 1) Field Programmable Gate Arrays (FPGAs), 2) Coarse Grained Reconfigurable Architectures (CGRAs), 3) RISC-V.
With FPGAs we are able to undertake the design of low-level bespoke memory access designs or algorithm level concurrency, with CGRAs to explore different approaches for mapping algorithms to the hardware and communication, and
RISC-V to experiment with different CPU and accelerator designs and their configurations. With support from the vendors, all these experiments will result in actionable results that will be use to further direct the research, but also for the vendors to understand how to best enhance their products for the HPC community.
The team of investigators will also gain insights into sustainability efforts in HPC with an in-depth study focusing on possible sustainability and net zero strategies. One of the overarching goals of the project is to provide a road-map for a more sustainable HPC landscape, and transfer the outcomes of the project to policy-making for a wide range of academic, government and industry stakeholders.
The project team is composed of experts from a variety of fields, including high performance computing, computer science, programming, computational fluid dynamics, innovation and sustainability, and policy-making. This interdisciplinary team will be able to bring a wide range of expertise to bear on the challenges of developing and utilising more energy and resource efficient HPC systems.
Publications
Brown N
(2025)
What is RISC-V and why should we care?
Chen L
(2025)
AskNatureGPT: an LLM-driven concept generation method based on bio-inspired design knowledge
in Journal of Engineering Design
Chen L
(2025)
How generative AI supports human in conceptual design
in Design Science
Wang B
(2025)
Creative combinational design through generative AI in different dimensional representations: An exploration
in Design and Artificial Intelligence
Wang B
(2025)
From analogy to innovation: A creative conceptual design approach leveraging large language models
in Advanced Engineering Informatics
Wang P
(2025)
Enhancing designer creativity through human-AI co-ideation: a co-creation framework for design ideation with custom GPT
in Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Wang P
(2025)
Human-AI co-ideation via combinational generative model
in Journal of Engineering Design
| Title | A systematic review of scientific machine learning based studies in the fields of Physics and Engineering; Reporting of methods, policy recommendations and energy use. |
| Description | Scientific machine learning (SML) is a cross-cutting research area combining physics-based models, machine learning (ML) and/or artificial intelligence (AI) predominantly in the physical sciences. SML draws from computer science, computational science, Physics and Engineering. The rapid development of methods for SML and the use of ever more complex tools creates a tangible problem of model interpretation, transparency, reproducibility and validation. These issues are ubiquitous in all fields of AI and ML. In addition, computational energy consumption, energy usage optimization and sustainability are usually not considered or reported in this field. Being transparent around energy use is key, especially in this field where the extended use of cloud computing might foster hidden environmental impacts. We have proposed the development of the Sustainable Scientific Machine-learning Reporting Toolkit (SSMART); a reporting guideline to aid authors to thoroughly describe all elements of an SML-based study. SSMART will be structured in three topics: the data used (evidence base), the methods used (models) and the energy consumption (for training and inference). |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2026 |
| Provided To Others? | Yes |
| Impact | he findings of this systematic review will be a key part of the development of SSMART. This systematic review will be used to appraise the quality of current reporting within the domain of SML based studies. The first objective of this systematic review will be to evaluate the methodological conduct and reporting of SML based studies in the fields of Physics and Engineering. The second objective will be to identify and evaluate the metrics and tools that are being used for tracking energy use and to appraise the reporting quality of energy use for SML models. Overall, the systematic review will provide the primary evidence base for the items to be considered in the reporting |
| URL | https://www.researchregistry.com/browse-the-registry/#registryofsystematicreviewsmeta-analyses/regis... |
| Title | Sustainable Scientific Machine-learning Reporting Toolkit (SSMART) protocol |
| Description | This protocol describes the development of the Sustainable Scientific Machine-learning Reporting Toolkit (SSMART). This will be a checklist-based reporting guideline for scientific machine learning (SML) based studies in the physical sciences. The development process will consist of five phases. In phase one, a systematic review will be executed to appraise the current state of reporting methods and energy use in SML-based studies. In phase two, an online Delphi exercise will be used to identify the items to be considered for the checklist. In phase three, during a consensus meeting, the items to be included in the SSMART checklist will be finalized. Phase four will involve developing and writing up the main paper where SSMART will be reported, and an explanation and elaboration paper providing more detailed information. During the final phase, a comprehensive dissemination plan will be implemented to promote the adoption of SSMART by a diverse range of users. We anticipate that SSMART will help to establish the incorporation of energy use considerations and transparent reporting for SML-based studies and promote sustainability. Moreover, SSMART will help researchers report key aspects of their work so that readers can interpret their findings, understand limitations, and appraise research quality. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2025 |
| Provided To Others? | Yes |
| Impact | Still on-going research. |
| URL | https://www.protocols.io/view/protocol-for-developing-the-sustainable-scientific-4r3l2pkrqg1y/v1 |
