High-performance computing (HPC) is changing faster than its benchmarks can measure.
The industry was driven primarily by hardware progress and raw compute. It is now becoming a complex ecosystem where multiple forces intersect: artificial intelligence, heterogeneous architectures, sustainability constraints, evolving software stacks and increasingly distributed innovation models.
This convergence is changing how systems are built, used and orchestrated. As workloads become more diverse and interconnected, HPC is shifting toward a more integrated paradigm where compute, data and AI capabilities must operate seamlessly together.
The following eight trends, drawn from discussions at ISC High Performance Conference 2026, illustrate how this transformation is redefining the foundations of high-performance computing.
Summary:
Hybrid infrastructures are paving the way for quantum computing
Software, co-design and collaboration are becoming strategic foundations
For decades, HPC progress meant one thing: building increasingly powerful systems. The industry organised itself around that objective through hardware roadmaps, benchmarks and procurement decisions.
That logic is changing. In ISC26 closing keynote, Jack Dongarra, Research Professor Emeritus, University of Tennessee, Oak Ridge National Laboratory, shared that the world's top supercomputers routinely achieve less than 1% of their theoretical peak performance in practice. The reason is that processors spend most of their time waiting for data to move between memory, storage and compute. If a machine running at full theoretical speed still delivers less than 1% of that in practice, peak FLOPS is not a perfect measure. This is why benchmarks such as high performance conjugate gradients (HPCG) have been created as they better reflect the memory and data movement challenges of real applications.
What matters is what the system actually produces: a validated simulation, a trained model, a scientific result that can be trusted and reproduced.
Raw performance remains important, but it is no longer sufficient to evaluate success. The metrics that matter now are around time-to-solution, energy per trusted result, workflow efficiency and reproducibility.
The question is shifting from “How powerful is this system?” to “What does it enable?”. This shift also redirects attention from isolated systems to full workflows, where simulation, data and AI must be orchestrated end to end.
HPC progress used to follow Moore’s LawMoore's Law is the observation made by Gordon Moore in 1965 that the number of transistors on a microchip would roughly double every two years, enabling a steady increase in computing performance. While it has driven decades of progress in computing, physical, energy and economic constraints have significantly slowed this pace in recent years., with each generation delivering predictable performance gains. As these gains slow, the industry is moving toward heterogeneous architectures, combining multiple computing architectures, each optimised for different types of workloads.
CPUs and GPUs remain central, but they are now complemented by AI accelerators AI accelerators are specialised hardware designed to efficiently run AI workloads such as training and inference, optimised for parallel processing. , quantum processors, and other domain-specific technologies. Rather than replacing each other, these architectures are designed to coexist and specialise.
As these architectures converge, software becomes the critical enabler. The main bottleneck is data movement, scheduling and dependency management across distributed environments.
Orchestrating classical and quantum resources, ensuring interoperability and supporting hybrid workflows will be key to unlocking the next generation of HPC systems.
AI is reshaping HPC by extending its simulation capabilities.
Hybrid workflows are becoming increasingly common, where physics-based simulations are combined with AI models. AI can accelerate calculations, explore large design spaces and reduce computation time by learning from simulation data. AI acts as an assistant to simplify and accelerate specific stages of the computation process. This approach is already transforming fields such as scientific research, climate modelling, drug discovery, advanced manufacturing and digital twins.
Rather than competing, AI and HPC are becoming interdependent. Modern infrastructures must now support simulation, AI training, inference and analytics within a unified environment.
The conversation about quantum computing has evolved.
Rather than waiting for fault-tolerant quantum computers, organisations are already preparing for this quantum era through hybrid infrastructures that combine classical HPC, AI and quantum technologies.
The focus is also about developing relevant use cases designed to accelerate specific workloads such as optimisation, chemistry and materials simulation.
As systems scale toward exascale, energy consumption, cooling and infrastructure costs have become limiting factors. Performance alone is no longer viable if it comes with unsustainable energy use.
Energy efficiency is now a key element into HPC architecture and software decisions. Software is increasingly optimised for energy-to-solution, while hardware design focuses on thermal efficiency, power management and data movement reduction.
This shift is reinforced by benchmarking frameworks such as the Green500. In recent rankings, Bull-built systems have consistently demonstrated leadership, securing the top position for five consecutive editions and occupying the entire podium in 2 successive rankings.
The complexity of HPC systems (heterogeneous hardware, hybrid workflow, quantum integration, software…) requires collaboration across a broad ecosystem of actors: researchers, industry, software developers, hardware manufacturers and public initiatives.
Indeed, progress in HPC increasingly relies on a tight integration between hardware and software through co-design, where architectures and applications are developed in close alignment to maximise efficiency and scalability.
Shared development models, including open-source contributions and large-scale programs such as EuroHPC Joint Undertaking, reflect this shift toward collective innovation, where complexity can only be addressed through coordinated effort.
AI factories are emerging as integrated ecosystems that combine compute, data, AI capabilities and workflow orchestration into unified environments.
Rather than acting as traditional data centres, they operate as continuous production systems for AI, enabling large-scale training, fine-tuning and deployment of models.
In Europe, initiatives such as EuroHPC Joint Undertaking are positioning these platforms as strategic assets for competitiveness and sovereignty across sectors such as healthcare, energy and manufacturing.
Within this landscape, Bull has been involved in several AI-Gigafactory initiatives such as AION consortium contributing to the development of integrated HPC-AI platforms for large-scale model training and deployment.
More broadly, AI factories reflect a shift from infrastructure as a resource to infrastructure as a strategic innovation platform.
The fishbowl panel at ISC26, titled "What is truth, anyway?", addressed the fact that outputs of AI and simulation systems are not inherently trustworthy, and the gap between what systems produce and what users can verify is widening.
The panel, led by Addison Snell of Intersect360 Research and Martin Schulz of TU Munich, covered AI's probabilistic outputs and their limitations in scientific contexts, the difficulty of communicating uncertainty to non-specialist audiences, quantum computing's inference-based nature (phenomena are inferred, not directly observed), and the tension between open scientific collaboration and national sovereignty over foundational technologies.
Trust is built through validation, explainability, reproducibility and accessibility across complex workflows that combine simulation, AI and data-driven methods for both scientific validity and industrial adoption.
These developments are also tied to questions of digital sovereignty. High-performance computing infrastructures are strategic assets for nations and regions, making control over data, systems and compute capabilities a key dimension of competitiveness and resilience.
The trends shaping the future of HPC all point in the same direction: success will no longer be defined solely by computational performance, but by the ability to deliver meaningful scientific and industrial outcomes.
HPC is becoming an integrated ecosystem where compute, AI, quantum, data and software must work together across scales. The challenge is now to design coherent systems capable of delivering end-to-end value.
Within this transformation, Bull’s positioning reflects a broader European contribution to HPC innovation, spanning energy-efficient architectures, co-designed systems, AI factory initiatives, quantum / HPC hybridisation and emulation and participation in collaborative frameworks such as EuroHPC.
Ultimately, the future of HPC will belong to systems and ecosystems capable of turning computational power into trusted, usable and sustained impact.