This project introduces an interactive, web-based 3D data viewer specifically designed for the analysis of large-scale scientific cosmological datasets. The OpenCosmo Compute Portal [1], developed by Argonne National Laboratory’s Cosmological Physics and Advanced Computing group, provides easy access to cosmological simulations. Adding advanced visualization capabilities, such as those offered by this viewer, would significantly enhance the portal’s utility, allowing a broader audience to gain deeper insights into queried datasets without needing to build their own visualization tools.We address the critical need to democratize access to advanced scientific visualization, particularly for researchers who aren’t visualization experts. Leveraging modern web technologies, our viewer provides a fluid and responsive environment for exploring complex point cloud data. Key features include customizable visual properties, interactive selection, and robust state management, all accessible directly within a web browser, or virtual reality headset, if available.

Recent innovations in 3D reconstruction and the rise in popularity of Digital Twins present a unique opportunity for the integration of the two technologies for high quality data visualization. In this work, I present an architecture for integrating Gaussian Splat reconstructions with dynamic data and user interaction, implemented in Unreal Engine. I also explore the application of this method to digital twin creation and how it addresses specific visualization challenges.

We present a multi-GPU extension of the 3D Gaussian Splatting (3D-GS) pipeline for scientific visualization. Building on previous work that demonstrated high-fidelity isosurface reconstruction using Gaussian primitives, we incorporate a multi-GPU training backend adapted from Grendel-GS to enable scalable processing of large datasets. By distributing optimization across GPUs, our method improves training throughput and supports high-resolution reconstructions that exceed single-GPU capacity. In our experiments, the system achieves a 5.6× speedup on the Kingsnake dataset (4M Gaussians) using four GPUs compared to a single-GPU baseline, and successfully trains the Miranda dataset (18M Gaussians) that is an infeasible task on a single A100 GPU. This work lays the groundwork for integrating 3D-GS into HPC-based scientific workflows, enabling real-time post hoc and in situ visualization of complex simulations.

General Experimentation and Natural Interface Utility System (GENIUS), is an AI personal assistant specifically tailored for scientists engaged in experimentation and research. GENIUS integrates Large Language Models (LLM), with immersive mixed reality (XR) capabilities. Through natural speech recognition, users can interact effortlessly with GENIUS to ask questions, visualize and manipulate complex 3D models, and execute computational jobs on Argonne Leadership Computing Facility (ALCF) supercomputers.

Mixed reality (MR) enables immersive, intuitive engagement with scientific data. When paired with AI-driven assistants, it has the potential to transform traditional workflows. In this paper, we introduce a modular agentic architecture for scientific visualization in MR, designed to balance general-purpose flexibility with domain-specific extensibility. Our modular architecture supports composable tools, contextual reasoning, and dynamic task execution. We outline a three-layer design, domain module integration, and orchestration of multistep workflows. We demonstrate the system’s capabilities through use cases in biology and general-purpose scientific visualization, including protein interaction networks and remote ParaView-based rendering. The result is a flexible and extensible foundation for spatial scientific computing.

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

Classic visual analysis relies on a single medium for displaying and interacting with data. Large-scale tiled display walls, virtual reality using head-mounted displays or CAVE systems, and collaborative touch screens have all been utilized for data exploration and analysis. We present our initial findings of combining numerous display environments and input modalities to create an interactive multi-modal display space that enables researchers to leverage various pieces of technology that will best suit specific sub-tasks. Our main contributions are 1) the deployment of an input server that interfaces with a wide array of interaction devices to create a single uniform stream of data usable by custom visual applications, and 2) three real-world use cases of leveraging multiple display environments in conjunction with one another to enhance scientific discovery and data dissemination.

Remarkable observational advances have established a compelling cross-validated model of the Universe. Yet, two key pillars of this model -- dark matter and dark energy -- remain mysterious. Next-generation sky surveys will map billions of galaxies to explore the physics of the 'Dark Universe'. Science requirements for these surveys demand simulations at extreme scales; these will be delivered by the HACC (Hybrid/Hardware Accelerated Cosmology Code) framework. HACC's novel algorithmic structure allows tuning across diverse architectures, including accelerated and multi-core systems. On the IBM BG/Q, HACC attains unprecedented scalable performance - currently 6.23 PFlops at 62% of peak and 92% parallel efficiency on 786,432 cores (48 racks) - at extreme problem sizes with up to almost two trillion particles, larger than any cosmological simulation yet performed. HACC simulations at these scales will for the first time enable tracking individual galaxies over the entire volume of a cosmological survey.

Interfacing atomistic-based with continuum-based simulation codes is now required in many multiscale physical and biological systems. We present the computational advances that have enabled the first multiscale simulation on 190,740 processors by coupling a high-order (spectral element) Navier-Stokes solver with a stochastic (coarse-grained) Molecular Dynamics solver based on Dissipative Particle Dynamics (DPD). The key contributions are proper interface conditions for overlapped domains, topology-aware communication, SIMDization, multiscale visualization and a new domain partitioning for atomistic solvers. We study blood flow in a patient-specific cerebrovasculature with a brain aneurysm, and analyze the interaction of blood cells with the arterial walls endowed with a glycocalyx causing thrombus formation and eventual aneurysm rupture. The macro-scale dynamics (about 3 billion unknowns) are resolved by NεκTαr - a spectral element solver; the micro-scale flow and cell dynamics within the aneurysm are resolved by an in-house version of DPD-LAMMPS (for an equivalent of about 100 billions molecules).

Abstract Visualization is an important part of the data analysis process. Many researchers, however, do not have access to the resources required to do visualization effectively for large datasets. This problem is illustrated through several user scenarios. To remedy this problem, we propose a Visualization Gateway that provides simplified access to such resources to a broad population of users. The current implementation of this gateway is described, including the technology used and the services made available. In particular, a detailed description of a ParaView portlet is included. A proposed design for enabling access to community users is discussed. Technology as well as policy issues that were raised, including security and data management, are covered, as are methods for providing additional services, scaling to include additional resources, and other areas of future development. The paper concludes with a summary of the topics covered. Copyright © 2006 John Wiley & Sons, Ltd.

Large-scale simulation codes typically execute for extended periods of time and often on distributed computational resources. Because these simulations can run for hours, or even days, scientists like to get feedback about the state of the computation and the validity of its results as it runs. It is also important that these capabilities be made available with little impact on the performance and stability of the simulation. Visualizing and exploring data in the early stages of the simulation can help scientists identify problems early, potentially avoiding a situation where a simulation runs for several days, only to discover that an error with an input parameter caused both time and resources to be wasted. We describe an application that aids in the monitoring and analysis of a simulation of the human arterial tree. The application provides researchers with high-level feedback about the state of the ongoing simulation and enables them to investigate particular areas of interest in greater detail. The application also offers monitoring information about the amount of data produced and data transfer performance among the various components of the application.

Grid applications can combine the use of computation, storage, network, and other resources. These resources are often geographically distributed, adding to application complexity and thus the difficulty of understanding application performance. We present GridMapper, a tool for monitoring and visualizing the behavior of such distributed systems. GridMapper builds on basic mechanisms for registering, discovering, and accessing performance information sources, as well as for mapping from domain names to physical locations. The visualization system itself then supports the automatic layout of distributed sets of such sources and animation of their activities. We use a set of examples to illustrate how the system can provide valuable insights into the behavior and performance of a range of different applications.