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.

In-transit visualization has become essential in high-performance computing (HPC) to reduce I/O overheads and enable real-time data analysis. However, as simulations grow in scale and complexity, these visualization tasks increasingly demand substantial computational resources, exacerbating energy consumption and limiting system scalability. As we show in this paper, a key bottleneck is the conventional one-rank-per-GPU allocation model, which leads to irregular GPU utilization and waste of hardware resources. To tackle this challenge, we propose using GPU-sharing strategies to improve energy efficiency in in-transit visualization without compromising performance. We evaluate six distinct configurations built upon three NVIDIA GPU-sharing mechanisms: the default CUDA model with context switching between processes, Multi-Process Service (MPS), which enables dynamic context sharing, and Multi-Instance GPU (MIG), which provides hardware-level partitioning. Using the WarpX simulation code and Ascent visualization framework, our experiments on the Polaris supercomputer span multiple rendering techniques, node counts, and data sizes. Results show that GPU-sharing strategies can improve the trade-off between performance and energy, represented by the energy-delay product (EDP) metric, by up to 81.7%. We also show that workload-aware strategy selection is essential to improve performance-energy efficiency: MIG-based configurations are more effective for lightweight and regular workloads, offering up to 64.5% energy savings, while MPS better handles GPU-intensive workloads, achieving up to 71.1% EDP improvement. Finally, we demonstrate that optimized sharing strategies can reduce the required compute nodes by up to 75%, freeing system resources for concurrent workloads.

Modern scientific research often relies on powerful computing systems that use graphics processing units (GPUs) to run complex applications. However, running these systems requires a large amount of energy, which contributes to carbon emissions and raises concerns about environmental impact. Given this scenario, we explore how sharing a single GPU between multiple applications can improve both performance and sustainability when running scientific workflows. We consider three execution strategies: running applications one after another, running two at the same time, and replacing finished tasks with new ones right away, using eighteen widely used scientific applications on three different GPUs (AMD MI250X, AMD RX 7900XT, and NVIDIA RTX 4090). To demonstrate that finding the best co-execution combination of applications improves resource efficiency, we use a mathematical approach based on linear programming to schedule which applications run together. Our results show that this approach can reduce total execution time by up to 47% and lower carbon emissions by as much as 34%, with minimal impact on the performance of individual applications. Additionally, when optimal combinations of parallel applications are used, the overall performance of a complete scientific workflow can improve by 36%, while carbon emissions are reduced by 25%.

Current climate change has posed a grand challenge in the field of numerical modeling due to its complex, multiscale dynamics. In hydrological modeling, the increasing demand for high-resolution, real-time simulations has led to the adoption of GPU-accelerated platforms and performance portable programming frameworks such as Kokkos. In this work, we present a comprehensive performance study of the SERGHEI-SWE solver, a shallow water equations code, across four state-of-the-art heterogeneous HPC systems: Frontier (AMD MI250X), JUWELS Booster (NVIDIA A100), JEDI (NVIDIA H100), and Aurora (Intel Max 1550). We assess strong scaling up to 1024 GPUs and weak scaling upwards of 2048 GPUs, demonstrating consistent scalability with a speedup of 32 and an efficiency upwards of 90% for most almost all the test range. Roofline analysis reveals that memory bandwidth is the dominant performance bottleneck, with key solver kernels residing in the memory-bound region. To evaluate performance portability, we apply both harmonic and arithmetic mean-based metrics while varying problem size. Results indicate that while SERGHEI-SWE achieves portability across devices with tuned problem sizes (<70%), there is room for kernel optimization within the solver with more granular control of the architecture specifically by using Kokkos teams and architecture specific tunable parameters. These findings position SERGHEI-SWE as a robust, scalable, and portable simulation tool for large-scale geophysical applications under evolving HPC architectures with potential to enhance its performance.

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.