Cosmic feedback at scale: Supercomputing reveals how quasars regulate the early Universe
Cosmic feedback at scale: Supercomputing reveals how quasars regulate the early Universe
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Cosmic feedback at scale: Supercomputing reveals how quasars regulate the early Universe

Recent studies are reshaping our understanding of astrophysics, revealing that galaxy evolution in the early universe is not a solitary process. Instead, it is a computationally intricate and interconnected phenomenon, strongly influenced by extreme feedback from quasars. This feedback is now being unraveled through advanced supercomputing. Central to this new perspective is the increasing dependence on high-performance computing (HPC) to simulate, reconstruct, and decode the nonlinear physics of galaxy formation over cosmic timescales.

Quasars as cosmic “blowtorches”

Observational work led by researchers at the University of Arizona provides compelling evidence that quasars, highly luminous, accreting supermassive black holes, can suppress star formation not only within their host galaxies but across vast intergalactic distances.
 
Using data from the James Webb Space Telescope, scientists identified a deficit of star-forming galaxies surrounding some of the brightest quasars in the early universe. The mechanism is now understood as radiative feedback, where intense radiation heats and dissociates molecular hydrogen, the essential fuel for star formation.
 
Crucially, interpreting these observations requires sophisticated modeling. Radiative transfer, gas dynamics, and galaxy clustering must be simulated simultaneously, often across volumes spanning millions of light-years. These calculations are only tractable through massively parallel HPC systems.

Supercomputing the “galaxy ecosystem”

The emerging paradigm, sometimes described as a “galaxy ecosystem,” reframes cosmic evolution as a networked system in which energy output from one galaxy influences the fate of many others.
To quantify this, researchers employ cosmological hydrodynamics simulations, which integrate:
  • Gravity-driven structure formation
  • Radiative feedback from quasars
  • Gas cooling, heating, and turbulence
  • Star formation and chemical evolution
These simulations are computationally intensive, often requiring millions of CPU hours and distributed-memory architectures. Codes derived from frameworks such as adaptive mesh refinement (AMR) solvers, historically associated with tools like RAMSES, allow scientists to dynamically refine resolution in regions of interest, capturing both large-scale structure and small-scale physics.
 
Without supercomputing, resolving these multiscale interactions would be impossible.

Reading the Universe through data

Parallel to simulation efforts, European teams, including those affiliated with the University of Barcelona, are advancing new methodologies for “reading” the universe through data-driven analysis. Their work focuses on reconstructing the three-dimensional distribution of matter and radiation from observational datasets, a task that involves:
  • Processing petabyte-scale astronomical surveys
  • Applying inverse modeling techniques
  • Leveraging machine learning for pattern detection
These pipelines depend heavily on HPC infrastructure to handle the combinatorial complexity of parameter spaces and to reconcile observational uncertainty with theoretical models.

From Observation to Prediction

Another key contribution from recent studies is the integration of observational astronomy with predictive simulation. By combining telescope data with HPC-driven models, researchers can test competing hypotheses about galaxy evolution in silico.
 
For example, simulations now reproduce the observed suppression of star formation near quasars by explicitly modeling how radiation propagates through intergalactic gas. These models confirm that quasar feedback can extend over million-light-year scales, fundamentally altering the growth of neighboring galaxies.
 
This represents a shift from descriptive astronomy to predictive cosmology, where supercomputers act as virtual laboratories for testing the physics of the universe.

HPC as the Engine of Modern Astrophysics

Across all the studies referenced, the common thread is clear: supercomputing is no longer ancillary to astrophysics; it is foundational.
 
Modern investigations into galaxy formation rely on HPC systems to:
  • Simulate billions of particles representing dark matter and gas.
  • Model radiation transport across cosmological volumes
  • Analyze high-resolution telescope data in near real time.
  • Perform statistical inference across vast parameter spaces.
These capabilities enable researchers to move beyond simplified models and capture the full complexity of cosmic evolution.

Toward a Unified Model of Galaxy Evolution

The convergence of observational breakthroughs and computational power is bringing astrophysics closer to a unified understanding of how galaxies form, evolve, and interact.
 
Quasars, once studied primarily as isolated phenomena, are now recognized as cosmic regulators, capable of shaping entire regions of the universe. Their influence, revealed through a combination of cutting-edge telescopes and supercomputing simulations, underscores the interconnected nature of cosmic structure.
 
As HPC systems continue to scale, the next frontier will be even more ambitious: fully coupled simulations that integrate dark matter, baryonic physics, radiation, and magnetic fields across the observable universe.
 
In this emerging era, the story of the cosmos is no longer written solely in the stars; it is computed.
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From data deluge to diagnostic insight: RAMSES supercomputer powers next-generation AI pathology at Cologne

A new study highlights a pivotal shift in biomedical research: breakthroughs now depend as much on powerful computational tools as on laboratory instruments. Driving this transformation is the RAMSES supercomputer at the University of Cologne's IT Center in Germany, empowering researchers to process and analyze enormous digital pathology datasets at a scale previously unattainable.
 
Featured in a recent Nature Medicine publication, this work presents SPARK, an advanced AI-driven framework described as “agentic” for its ability to autonomously generate, test, and validate hypotheses in cancer pathology. While the conceptual innovation is noteworthy, it is RAMSES’s computational power that makes such a system practically feasible.

Scaling digital pathology beyond human limits

Digital pathology operates on whole-slide images (WSIs), each of which can reach gigapixel resolution. When multiplied across thousands of patient samples and multiple cancer types, the resulting data volume quickly becomes prohibitive for conventional computing.
 
To address this, researchers deployed a hybrid computational architecture in which high-throughput workloads were executed on RAMSES, a high-performance computing (HPC) system designed for large-scale modeling and simulation. The system integrates advanced GPU resources, including multiple NVIDIA H100 accelerators, optimized for parallel processing of AI and image analysis pipelines.
 
Within this environment, each pathology case required substantial dedicated resources, up to 120 GB of memory and 12 CPU cores per sample, highlighting the intensity of the computational workload.

The SPARK framework: AI at scale

The SPARK system represents a shift from static machine learning models to dynamic, reasoning-based AI workflows. Rather than being trained solely on labeled data, SPARK generates its own analytical “ideas,” translates them into executable code, and evaluates their predictive value across large datasets.
 
This process unfolds in several stages:
  • Idea generation using large language models (LLMs)
  • Automated code creation and validation
  • High-throughput parameter extraction from WSIs
  • Statistical modeling and prognostic evaluation
While early-stage development and prototyping could be conducted on smaller systems, the full-scale execution, particularly across cohorts exceeding 5,000 patients, required the parallel processing capabilities of RAMSES.

High-performance computing meets oncology

The integration of HPC into this workflow enabled several key advances:
1. Massive Parallel Image Analysis
RAMSES allowed simultaneous processing of thousands of WSIs, performing segmentation, cell classification, and spatial mapping across seven distinct cell types.
2. Large-Scale Parameter Exploration
The system generated and evaluated thousands of candidate biomarkers, over 2,400 validated parameters in some analyses, each representing a potential predictor of cancer progression.
3. Predictive Modeling at Population Scale
Using HPC resources, the team conducted multivariable statistical modeling across diverse cancer cohorts, identifying features with independent prognostic value beyond traditional clinical metrics.
4. Temporal Reconstruction of Tumor Evolution
By analyzing spatial patterns within tumors, the system inferred evolutionary sequences of disease progression, an inherently data-intensive task requiring both computational power and algorithmic sophistication.

RAMSES: Infrastructure as an enabler of discovery

The RAMSES system, formally known as the Research Accelerator for Modeling and Simulation with Enhanced Security, played a central role in enabling these analyses. Hosted at the University Hospital Cologne and supported by national and European funding initiatives, it provides a secure, scalable environment for data-intensive biomedical research.
 
Crucially, RAMSES is not merely a computing resource but an integrated platform supporting:
  • GPU-accelerated AI workloads
  • High-memory nodes for large dataset handling
  • Parallelized pipelines for image and statistical analysis
  • Secure processing of sensitive clinical data
Without such infrastructure, the SPARK framework would be constrained to small-scale experiments rather than clinically relevant population studies.

Toward autonomous scientific discovery

The implications of this work extend beyond pathology. By combining agent-based AI systems with supercomputing infrastructure, researchers are moving toward autonomous scientific discovery pipelines, systems that can generate hypotheses, test them, and refine their own analytical strategies.
 
In oncology, this approach could accelerate the identification of novel biomarkers, improve patient stratification, and ultimately inform more personalized treatment strategies. More broadly, it signals a shift in how science is conducted: from manually driven analysis to computational ecosystems capable of operating at scale.

The supercomputing imperative in modern medicine

The study reinforces a central theme in contemporary research: data alone is not enough. The ability to extract meaning from complex, high-dimensional datasets depends critically on access to advanced computational infrastructure.
 
In this case, the RAMSES supercomputer transformed a conceptual AI framework into a practical, high-impact tool, demonstrating that in the era of digital medicine, supercomputing is not an accessory but a necessity.
 
As biomedical datasets continue to expand in size and complexity, systems like RAMSES will increasingly define the boundary between theoretical possibility and real-world application.
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Hidden order, revealed at scale: Supercomputing, electron ptychography uncover the inner workings of relaxor ferroelectrics

A recent study led by researchers at the Massachusetts Institute of Technology has shed new light on one of materials science’s most persistent puzzles: the elusive structural organization inside relaxor ferroelectrics. Although these materials are foundational to technologies such as precision actuators and advanced sensors, the atomic-level disorder inherent to relaxor ferroelectrics has, until now, masked the origins of their exceptional electromechanical behavior.
 
The breakthrough, highlighted in MIT News, goes beyond experimental advances; it is fundamentally computational. Central to this progress is the integration of high-resolution electron ptychography with large-scale simulation workflows powered by high-performance computing (HPC), bridging the gap between experiment and theory across various length scales.

A computational lens into atomic disorder

Relaxor ferroelectrics such as lead magnesium niobate–lead titanate (PMN-PT) exhibit what researchers describe as a “polar slush,” a complex, fluctuating arrangement of nanoscale polarization domains. Capturing this structure requires more than imaging; it demands reconstruction, simulation, and statistical interpretation of vast multidimensional datasets.
 
The MIT-led team employed multislice electron ptychography to generate 4D scanning transmission electron microscopy (4D-STEM) datasets. Each dataset consists of diffraction patterns collected across a real-space grid, yielding an immense volume of information that requires iterative reconstruction algorithms. These reconstructions rely on computational frameworks such as PtychoShelves and custom multislice solvers, tools that are computationally intensive and inherently suited to supercomputing environments.
 
Critically, the reconstruction process overcomes multiple scattering effects and retrieves depth-resolved structural information at near-atomic resolution. This allows researchers to visualize polarization variations through the thickness of the material, something unattainable with conventional microscopy techniques.

Supercomputing the physics of polarization

Beyond imaging, the study’s true computational depth emerges in its integration with molecular dynamics (MD) simulations. These simulations model supercells as large as 72 × 72 × 72 unit cells under varying strain conditions, tracking atomic displacements and polarization vectors over nanosecond timescales.
 
Such simulations are not trivial. They require:
  • Parallelized computation of interatomic forces using bond-valence models
  • Thermodynamic control via Nose–Hoover thermostats and Parrinello–Rahman barostats
  • Statistical averaging across billions of atomic interactions
The resulting datasets enable direct comparison with experimental reconstructions, effectively validating observed polar structures and revealing their dependence on strain and chemical ordering.
 
Moreover, multislice simulations of electron scattering, used to replicate experimental conditions, incorporate frozen phonon approximations with dozens of configurations to ensure convergence. 
 
These calculations, which simulate electron propagation through matter at atomic resolution, are computationally demanding and benefit significantly from HPC acceleration.

Data-driven discovery at the nanoscale

To interpret the immense data volumes, the researchers deployed advanced statistical and machine learning techniques. Principal component analysis (PCA) was applied to local polarization environments, reducing high-dimensional datasets into dominant “polar motifs” that describe recurring structural patterns.
 
Additionally, clustering algorithms were used to identify contiguous polarization domains, while pair-correlation functions quantified spatial relationships between dipoles. These analyses revealed that:
  • Polarization is strongly influenced by local chemical heterogeneity, particularly the distribution of Nb⁵⁺ and Mg²⁺ ions.
  • Short-range chemically ordered regions significantly enhance long-range polar correlations.
  • Strain drives a transition toward more ordered, ferroelectric-like behavior without eliminating intrinsic disorder.
Such findings would be inaccessible without the combination of high-resolution experimental input and large-scale computational analysis.

Resolving the limits of measurement

One of the study’s notable achievements is quantifying the resolution limits of ptychographic reconstruction. Through simulation, the team demonstrated that polar domains as small as ~1 nm can be resolved under optimal conditions, despite a depth resolution of ~3.2 nm due to inherent blurring effects.
 
This calibration, achieved through synthetic datasets and reconstruction pipelines, underscores the importance of computational modeling in interpreting experimental data. It also highlights a broader trend in materials science: measurement is no longer purely observational but deeply intertwined with simulation.

Toward predictive materials design

By bridging atomistic simulations with experimental imaging, the MIT team has effectively created a multiscale framework for understanding relaxor ferroelectrics. The implications extend beyond academic curiosity.
 
With HPC-enabled workflows, researchers can now:
  • Predict how nanoscale chemical ordering influences macroscopic properties.
  • Optimize strain conditions for enhanced electromechanical performance.
  • Design next-generation materials with tailored polarization behavior.
This convergence of supercomputing and microscopy signals a shift toward predictive materials engineering, where computation does not merely support experiments but guides them.

The supercomputing imperative

The study exemplifies how modern materials science is inseparable from high-performance computing. From reconstructing terabyte-scale microscopy datasets to simulating millions of atomic interactions, every stage of the workflow depends on computational power.
 
As datasets grow richer and models more sophisticated, the role of supercomputers will only expand, transforming hidden atomic disorder into actionable scientific insight.
 
In the case of relaxor ferroelectrics, what was once considered noise is now recognized as structure, and it is supercomputing that has made it visible.
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