Energy efficiency and sustainability (EE&S) have become central challenges for modern High-Performance Computing (HPC) and AI infrastructures. While energy savings are often viewed as the main issue, sustainability also includes ecological, economic, and societal dimensions. A full life-cycle perspective—from planning and procurement to operation and decommissioning—is necessary to avoid rebound effects such as the Jevons paradox[1].

HPC and AI systems increasingly interact with their environment. Their multi-megawatt, highly variable power demand influences grid stability and requires better forecasting and closer coordination with energy providers. Future operations must incorporate energy- and carbon-aware scheduling, while heat reuse concepts and standardized sustainability metrics (PUE, WUE, ERE) become integral to system design and evaluation.

Growing hardware heterogeneity (CPUs, GPUs, accelerators, and emerging neuromorphic or quantum devices) offers efficiency potential that is still underutilized due to insufficient software optimization. Progress requires stronger hardware–software co-design, greater support for developers in exploiting advanced architectural features, and systematic use of monitoring data to identify inefficient applications. European semiconductor initiatives further enable alignment between hardware and computational requirements.

Software, algorithms, and workflows are equally important. Energy-efficient algorithms, data reduction techniques (compression, filtering, deduplication), improved I/O strategies, and optimized workflows can significantly reduce resource usage. Strengthening reproducibility and robust experiment packaging across diverse systems helps prevent wasted compute time and supports sustainable software lifecycles.

Digital twins (DTs) are emerging as key tools for lifecycle management. They support planning, operational optimization, predictive analysis, and end-of-life decisions. Effective DTs require harmonized monitoring, standard metrics, and shared methodologies. A European knowledge initiative could accelerate adoption and ensure consistent practices across HPC sites.

Raising awareness and empowering stakeholders—developers, users, operators, vendors, and funding bodies—is essential. Transparent monitoring infrastructures and vendor-agnostic sustainability indicators enable informed decision-making. User feedback mechanisms (dashboards, reports, incentives) can encourage more energy-conscious behaviour. Funding agencies, particularly the European Commission, should embed sustainability metrics, full life-cycle assessments [ISO 14040/14044], and continuous reporting requirements into procurement and project evaluations.

In the post-exascale era, simple performance scaling through increased energy use is no longer viable. With growing AI workloads and the need to reduce energy demand and CO₂ emissions, coordinated progress across hardware, software, operations, infrastructure, and user behaviour is imperative. Only a combined effort by all stakeholders will enable high-performing HPC and AI systems to meet future needs while aligning with long-term sustainability goals.

In this paper, an automation process to perform Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) analysis is developed to carry out aerodynamic design and optimization. The aircraft model/geometry is defined by a Common Parametric Aircraft Configuration Schema (CPACS) file, and the analyses are facilitated using high performance computers (HPC). As the computational capability of the available HPC systems is a limiting factor in the complexity of analyses that can be performed, a detailed performance analysis of the open source CFD code SU2 is undertaken and the profiling and performance analyses for large simulations are carried out.

Airinnova is a start-up company with a key competency in the automation of high-fidelity computational fluid dynamics (CFD) analysis. Following on from our previous PRACE SHAPE project, we have continued collaborating with the PDC Center for High Performance Computing at the KTH Royal Institute of Technology (KTH-PDC), to investigate the performance analysis of the open source CFD code SU2 and further develop the automation process for the field of aerodynamic optimization and design.

This document describes the work undertaken by Task 7.2 "Joint Access to Data, HTC and Cloud Computing Resources" within the EUDAT2020 project focusing on the interoperability between EUDAT and EGI. The main objective of the conducted work was to realize cross-infrastructure services that enable the desired perceived seamless access to a combined infrastructure offering by both EGI and EUDAT services in pairing their data and high-throughput computing resources together. This document concentrates on the phase after the definition of the generic use case and depicts in detail the challenges encountered by the early adopters, the resulting new requirements and the identified solutions.

Automatic multidisciplinary design optimization is one of the challenges that are faced in the processes involved in designing efficient wings for aircraft. In this paper we present mixed fidelity aerodynamic and aero-structural optimization methods for designing wings. A novel shape design methodology has been developed - it is based on a mix of the automatic aerodynamic optimization for a reference aircraft model, and the aero-structural optimization for an uninhabited air vehicle (UAV) with a high aspect ratio wing. This paper is a significant step towards making it possible to perform all the core processes for aerodynamic and aero-structural optimization that require special skills in a fully automatic manner - this covers all the processes from creating the mesh for the wing simulation to executing the high-fidelity computational fluid dynamics (CFD) analysis code. Our results confirm that the simulation tools can make it possible for a far broader range of engineering researchers and developers to design aircraft in much simpler and more efficient ways. This is a vital step in the evolution of wing design processes as it means that the extremely expensive laboratory experiments that were traditionally used when designing the wings can now be replaced with more cost effective high performance computing (HPC) simulation that utilize accurate numerical methods.

This Best Practice Guide provides information about Intel’s Many Integrated Core (MIC) architecture and programmingmodels for the first generation Intel® Xeon Phi™ coprocessor named Knights Corner (KNC) in orderto enable programmers to achieve good performance out of their applications.The guide covers a wide range of topics from the description of the hardware of the Intel® Xeon Phi™ coprocessorthrough information about the basic programming models as well as information about porting programs up totools and strategies how to analyse and improve the performance of applications. Through the highly parallel architectureand the use of high bandwidth memory, the MIC architecture allows higher performance than traditionalCPUs for many types of scientific applications. The guide was created based on the PRACE-3IP Intel® XeonPhi™ Best Practice Guide. New is the inclusion of information about applications, benchmarks and EuropeanIntel® Xeon Phi™ based systems.

Creo Dynamics [1] is a Swedish engineering company with core competence in fluid dynamics, acoustics and structuraldynamics. Creo Dynamics has broad experience from participating in national and international research programmesfocussing on the development of new emerging technologies for the automotive or aerospace industries. In the company,experienced engineers develop and deliver simulation tools and procedures – often based on open source software [2] –which are tailored towards specific needs and applications; this process always involves maintaining a conscious balancebetween turn-around time and accuracy.Creo Dynamics has recently undertaken a PRACE SHAPE project together with application experts from the KTH RoyalInstitute of Technology. During the course of the project we developed templates and “recipes” for a range of tasks that areinvolved in the automotive industry – such as automated handling of computer-aided design (CAD), parallel meshing,solving and post-processing. These were all tailored towards a particular automotive application, namely the aerodynamics ofa heavy-duty semi-trailer. We also focused on parallel implementations and executions for large-scale simulations. Inaddition to monitoring the efficiency of the processes (for meshing and solving run-time performance and scalability) andidentifying critical bottlenecks, we gave significant attention to pinpointing performance deficits in the processes so as togive guidance for a further fine-tuning of the overall methodology.

Airinnova is a start-up company with a key competency in the automation of high fidelity computational fluiddynamics (CFD) analysis. The goal of this SHAPE project, a collaboration with the PDC Center for HighPerformance Computing at the KTH Royal Institute of Technology (KTH-PDC), was to develop automatedprocedures for carrying out CFD analysis in the field of aerodynamic optimization and design. The project is asignificant step towards automation of the core processes that ordinarily would require specialist skills, such ascreation of the simulation mesh, thus assisting a broader sphere of engineers to design aircraft in more efficientand simpler ways.