Inside Space's new engine room: high-performance computing

There is Hopper. There is an Octopus. There is IceCore. There is Marvin. There is Satyameba. There is Param Rudra. These are not star students strolling across a university campus, but star machines powering those students, their professors, and entire research ecosystems.

The world of exascale simulations, GPU clusters, and high-performance computing is blossoming in new colours and scales across universities, laboratories, and space-tech projects everywhere.

Recently, the European Space Agency unveiled the ESA Space High-Performance Computing (HPC) environment, now available to support scientific research and technological development across all ESA programmes. The ESA Space HPC supercomputer, inaugurated at ESRIN in Italy in March, has been designed to meet the rising computational demands of the European space industry—particularly for weather modelling, rapid warnings, and advanced simulations.

Earlier this year, the National University of Singapore expanded its computing capabilities with Hopper, a supercomputer capable of 25 quadrillion calculations per second (25 PetaFLOPS), dramatically empowering NUS researchers with unprecedented HPC access.

In the United States, the University of Alabama is developing an HPC project to mitigate the rising financial and energy costs associated with AI and machine learning research.

The Executive Director of the University’s High Performance Computing and Data Centre described the push simply: “Having access to the GPUs at the HPC will enable our faculty and students to do scientific-level research and AI.”

Across the Pacific, the Osaka University D3 Centre is progressing swiftly with trial operations of the “Osaka University Compute and sTOrage Platform Urging open Science” (Octopus), built by NEC Corporation. With a theoretical performance of 2.293 PetaFLOPS and 140 computing nodes, Octopus is a computational and data platform designed to drive open science.

In Vermont, a USD 2.1 million grant is powering a new AI-focused supercomputer at the University of Vermont’s Advanced Computing Center. The IceCore cluster, reportedly 100 times faster than UVM’s existing systems, promises to reshape the institution’s research capabilities.

Meanwhile, Marvin—another supercomputer—has been energising research at the University of Bonn. Professor Maren Bennewitz, Vice Rector for Digitalisation and Information Management, has hailed it as a “game-changer”, opening entirely new scientific horizons.

The university has invested heavily in HPC infrastructure under its Excellence and Digital Strategies, recognising that modern research depends fundamentally on computing strength.

India’s Tryst with Supercomputers

Back home, India’s National Supercomputing Mission has established a robust HPC ecosystem, led by Param Rudra, and other advanced facilities. Institutions such as the Inter-University Accelerator Centre in Delhi and the SN Bose National Centre for Basic Sciences in Kolkata are gearing up to utilise Param Rudra across various research domains.

Across India, progress is rapid. Graphic Era University in Dehradun has inaugurated a Centre of Excellence for Artificial Intelligence, Skill Development, and Innovation—Uttarakhand’s first NVIDIA AI and HPC hub—powered by an NVIDIA DGX B200 system with eight GPUs and 1.74 TB of GPU memory.

Similarly, the University of Engineering and Management in Kolkata has launched Satyameba—Supercomputing Architecture for Transformative Yield in AI and Multi-GPU Engine-Based Acceleration.

Designed to revolutionise AI and scientific computing research, it delivers a peak performance of 978 TFLOPS and a sustained performance of 967 TFLOPS, integrating NVIDIA RTX 5070 GPUs, Intel i7/i9 processors, and Intel Xeon W-9 servers.

The pattern is unmistakable. The kind of data crunching, computational heavy lifting, and complex modelling that university labs once struggled with cannot be handled by the “Average Joe” workstation or the humble computing garage set-up of a decade ago. Today’s research problems demand muscle power, speed, and capabilities that only HPC-grade systems can provide.

High-performance computing has become the JCB of the digital world, and it is no surprise that space agencies, research institutes, and private space-tech companies are turning to these environments for simulation-heavy, data-intensive, and mission-critical work.

So, the question arises: is this just a temporary spike, or has HPC now become the foundational bedrock shaping universities, laboratories, and space exploration? And why does HPC integrate so effortlessly into these environments?

HPC as the New Academic Workhorse

Prof V Krishna Nandivada of the Department of Computer Science and Engineering at IIT Madras notes that HPC has become essential for research institutions because these systems deliver the computational power, speed, and infrastructure necessary to process, simulate, and interpret massive datasets—far beyond the capabilities of standard computing platforms.

He explains that HPC-supported research institutions achieve faster scientific breakthroughs by enabling complex analyses, simulations, and optimisation tasks that would otherwise be infeasible, or at best, painfully time-consuming.

As modern scientific and industrial data volumes grow exponentially, HPC enables discoveries across medicine, physics, climate science, and engineering.

At CERN, for example, HPC helps scientists process vast amounts of data from the LHC. Maria Girone, Head of CERN Openlab and Principal Applied Scientist in CERN’s IT department, puts it bluntly: “High-Energy Physics experiments are entering a new era of data-intensive research. This shift is driven by the High-Luminosity LHC that will generate exabyte-scale datasets each year.”

Prof Bibhudatta Sahoo, Head of the Department of Computer Science and Engineering at NIT Rourkela, adds that HPC aggregates powerful computing resources—multi-core CPUs, GPUs, and high-speed interconnects—working in parallel to perform large-scale computations far beyond what standard desktops or servers can handle.

At NIT Rourkela, the High-Performance Computing Facility supports research in various fields, including computational fluid dynamics, structural and material analysis, deep learning, data science, molecular dynamics, bioinformatics, cybersecurity, and network simulation.

He describes HPC as the “cornerstone of computational research infrastructure,” emphasising that modern sensors, satellites, sequencing machines, IoT systems, and large-scale experiments have led to an explosive growth in research data.

For universities, having in-house HPC capabilities significantly reduces operational costs and reliance on external cloud resources. At GITAM in Visakhapatnam, the G-cluster supercomputer—reportedly ranked 49th among India’s high-performance systems—enables faculty and students to address computationally intensive challenges without depending on public cloud HPC services.

The university’s decision to invest in on-premise HPC ensures uninterrupted access for research and lowers long-term costs. It creates an infrastructure backbone that empowers investigators to push the boundaries of science.

For Indian institutions, Prof Sahoo stresses that HPC enhances self-reliance and positions them for global collaboration on cutting-edge challenges. Traditional computing setups cannot efficiently manage, analyse, or visualise massive datasets.

HPC resolves this by delivering compute, memory, and storage resources at scale. Modern HPC systems also integrate AI frameworks such as TensorFlow and PyTorch, along with orchestration tools like Kubernetes and OpenStack, enabling hybrid computing that blends on-premise clusters with national cloud platforms. Resource schedulers like SLURM dynamically allocate CPU and GPU resources to boost performance and utilisation.

HPC clusters are inherently scalable; institutions can add nodes and GPUs as data volumes and computational demands grow. With high-throughput storage solutions (such as Lustre and GPFS) and low-latency networks, HPC enables seamless data flow between the compute and storage layers.

This architecture is vital for simulation workloads, AI training, and data analytics—reducing simulation runtimes from weeks to hours, allowing real-time visualisation, and enabling rapid model refinement. As Prof Sahoo summarises, “HPC divides massive computational problems into smaller sub-tasks distributed across thousands of nodes and processors.”

HPC’s Expanding Role in Space Tech

Beyond university corridors, HPC has become deeply embedded in the space-tech sector. From ESA’s Space HPC to start-ups designing next-generation launch vehicles, supercomputing is fast becoming the engine room of space research.

NASA, for instance, is investing aggressively in High-Performance Spaceflight Computing (HPSC) to support increasingly complex missions that require advances in navigation, control, scientific instrumentation, autonomous robotics, and communications.

Immanuel Louis, Co-founder of Astrophel Aerospace, explains that HPC has transformed rocket development. “At Astrophel, we rely on HPC and advanced CFD simulations to model complex cryogenic flow dynamics, combustion behaviour, and reusable launch profiles before we touch a single physical component. What used to take months of physical iteration can now be optimised digitally within days.”

Girone reiterates that to realise the physics potential of massive scientific datasets fully, HPC centres must provide both pledged and opportunistic resources to accelerate scientific output.

From a satellite payload and Earth Observation (EO) perspective, Sanjay Kumar, Co-Founder and CEO of EON Space Labs, illustrates that advanced imaging payloads, such as their MIRA space telescope and LUMIRA imaging systems, generate terabytes of multispectral, hyperspectral, and thermal data every day.

HPC enables rapid calibration, atmospheric correction, and feature extraction—often while satellites or drones are still in operation. High-speed onboard computing, integrated with cloud-based supercomputing workflows, enables real-time simulations, optical distortion modelling, and AI analytics that convert raw imagery into actionable intelligence within hours.

Across global space agencies, HPC is now embedded in every stage of mission design and execution. NASA’s Ames Research Center runs the Pleiades supercomputer—one of the world’s largest HPC environments dedicated to aerospace—which supports everything from supersonic transport modelling to Mars entry, descent, and landing (EDL) simulations. ESA operates multiple HPC clusters for spacecraft dynamics, radiation modelling, cryogenic propulsion, and autonomous navigation systems. Even JAXA relies on HPC to model extreme heat fluxes on re-entry vehicles, simulate shock interactions around hypersonic bodies, and validate flight-control algorithms for deep-space missions.

This deep reliance on HPC is accelerating rapidly as space missions become more autonomous. Next-generation lunar missions, asteroid rendezvous missions, and planetary landers require onboard systems capable of interpreting sensor inputs in real-time.

HPC, combined with AI models trained on supercomputers, powers these autonomous guidance, navigation, and control (GNC) systems. For instance, AI-driven terrain-relative navigation demands datasets so large—and simulations so complex—that they can only be generated and validated using HPC-scale resources.

This rapid turnaround is critical for defence applications such as border monitoring, maritime domain awareness, and asset tracking, as well as for civilian roles in urban planning, environmental conservation, and disaster response.

ISRO too is scaling its HPC footprint. While India does not widely publicise its internal supercomputing capabilities, the organisation has confirmed the use of high-end clusters for trajectory optimisation, cryogenic engine simulation, payload thermal modelling, and Chandrayaan-3’s successful guidance algorithms.

Additionally, work on Gaganyaan’s crew safety systems involves thousands of Monte Carlo simulations that require HPC-grade compute. The Semi-Cryogenic Engine (SCE-200) has undergone CFD simulations, which are believed to have been run on national HPC facilities, such as the PARAM supercomputers.

Supercomputing also supports the creation of digital twins of propulsion systems, which learn from physical test data, predict wear, and optimise performance across cycles. Louis stresses that this fusion of HPC, CFD, and AI is what makes modern rockets “smarter, more reliable, and more affordable”.

Across the EO value chain, HPC drives everything from sensor calibration and radiometric correction to AI-assisted classification and anomaly detection. Space agencies and private companies utilise HPC to simulate orbital environments, predict sensor behaviour under solar flares, analyse thermal fluctuations, and assess the impact on data transmission quality.

Multi-sensor fusion enabled by HPC can detect subtle heat patterns, moisture variations, and movement signatures—unlocking applications ranging from agricultural stress mapping to defence-grade target tracking.

Space situational awareness (SSA) is another domain that is rapidly expanding in complexity. With more than 11,000 active satellites and tens of thousands of tracked debris objects, predicting conjunctions requires real-time, high-precision orbital simulations.

HPC clusters enable agencies to run millions of debris-collision scenarios, model variations in atmospheric drag, and forecast risk windows for satellite operators. This is becoming central to operations at USSPACECOM, ESA’s Space Safety Programme, and emerging players such as Australia’s new SSA centres.

As Louis notes, HPC has become the backbone of modern space engineering. Space systems generate enormous data volumes, from satellite telemetry and propulsion tests to deep-space imaging and environmental modelling. At Astrophel, engine test stands monitor hundreds of parameters in real time through SCADA systems, with HPC enabling immediate analysis to enhance engine efficiency and safety.

HPC also supports accurate simulation of orbital mechanics, atmospheric re-entry, cryogenic behaviour, and structural stresses, reducing the need for expensive physical tests and enabling more confident design decisions.

How HPC Powers the Future of Space

Market projections emphasise that HPC is not a passing trend; it is a long-term infrastructural force. Future Market Insights estimates that the global HPC market grew from USD 42 billion in 2020 to USD 60.2 billion in 2025, and is poised to reach USD 124.2 billion by 2035.

Grand View Research projects a market size of USD 87.31 billion by 2030. HPC is already entrenched across academic, commercial, and government institutions, addressing complex challenges across public safety, weather forecasting, climate research, and environmental preservation.

In the space sector specifically, demand is rising even faster. The Space Foundation reports that space data volumes are increasing at a 38% annual rate, driven by mega-constellations, synthetic-aperture radar (SAR) payloads, and hyperspectral sensors.

Each new generation of satellites exponentially increases downstream data-processing requirements, making HPC indispensable not only in national agencies but also across new space startups building analytics platforms, Earth observation marketplaces, and satellite-as-a-service models.

Prof Nandivada encapsulates this shift: HPC enables institutions to keep pace with the escalating scale and complexity of scientific data, driving innovation across disciplines.

Girone adds that integrating HPC into High Energy Physics workflows delivers transformative benefits—greater computational power, faster simulations, and more sophisticated AI and machine-learning algorithms.

Prof Sahoo emphasises that institutions utilising HPCs experience significant improvements in research productivity. HPC environments serve as shared ecosystems supporting diverse domains. By enabling cross-domain workloads, HPC fosters collaborative and interdisciplinary research—essential for tackling modern scientific problems that are multi-variable, nonlinear, and multidimensional.

HPC offers massive parallelism, high bandwidth, low latency, and advanced resource management to address challenges such as molecular dynamics, CFD, Finite Element Analysis, and AI model training.

The same principles are now shaping the future of space infrastructure. AI-driven mission planning, autonomous onboard navigation, interplanetary communication systems, and deep-space habitability models all rely on training and validating algorithms at HPC scale.

Even inter-satellite link optimisation for laser communication networks—often referred to as “the internet of space”—requires multi-node HPC simulations to model beam steering, turbulence, jitter, and Doppler shift across thousands of moving spacecraft.

Kumar distils the strategic imperative: without HPC, the delay between data capture and actionable insight can stretch from hours to months. Many Indian EO providers that depend on foreign feeds still face this challenge. Bridging this gap, he argues, represents the next major leap in India’s sovereign space intelligence.

Louis observes that supercomputing has transformed space engineering from a trial-and-error approach to continuous digital optimisation. Earlier, engineers built rockets through physics-based calculations, wind-tunnel tests, and significant trial and error, relying on slide rules, analogue computers, and physical models. HPC has rewritten this playbook entirely.

HPC has become the modern laboratory backhoe—the data excavator, simulation forklift, and analytical crane that academic and space researchers have always wished for. The era of standing by as the passive “Umarell” is over. HPC has moved from the periphery to the porch—fully accessible, fully integrated, and fully indispensable for the thinkers shaping the future.

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