Technical Demonstrations at the DOE Booth

The DOE’s Integrated Research Infrastructure aims to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. This demo will showcase the Fusion Pathfinder Project which uses multiple HPC facilities, ALCF and NERSC, to analyze data from a fusion experiment at the DIII-D National Fusion Facility. All three facilities are interconnected via the ESnet network.

This presentation will introduce the newly established PESO project (https://pesoproject.org), which supports software-ecosystem stewardship and advancement, collaborating through the Consortium for the Advancement of Scientific Software (CASS). PESO’s vision is that investments by the U.S. Department of Energy (DOE) in software have maximum impact through a sustainable scientific software ecosystem consisting of high-quality libraries and tools that deliver the latest high-performance algorithms and capabilities to serve application needs at DOE and beyond. Key PESO goals are (1) enabling applications to leverage robust, curated scientific libraries and tools, especially in pursuit of improvement in high-end capabilities and energy efficiency by leveraging accelerator (GPU) devices, and (2) emphasizing software product quality, the continued fostering of software product communities, and the delivery of products, while advancing workforce inclusivity and sustainable career paths. PESO delivers and supports software products via Spack and E4S, and PESO provides porting and testing platforms leveraged across product teams to ensure code stability and portability. PESO also facilitates the delivery of other products, such as AI/ML libraries, as needed by the HPC community. PESO collaborates with CASS to transform independently developed products into a portfolio whose total is much more than the sum of its parts—establishing a trusted software ecosystem essential to DOE’s mission.

We present an AI-driven framework for catalyst discovery, combining linguistic reasoning with quantum chemistry feedback. Our approach uses large language models (LLMs) to generate hypotheses and graph neural networks (GNNs) to evaluate 3D atomistic structures. The iterative process incorporates structural evaluation, reaction pathways, and stability assessments. Automated planning methods guide the exploration, rivaling expert-driven approaches. This integration of language-guided reasoning and computational chemistry feedback accelerates trustworthy catalyst discovery for sustainable chemical processes.

In turbulent and stochastic magnetic fusion plasma, the plasma transport analysis using grid quantities often misses important physics, such as subgrid phenomena and particle auto-correlation. Traditional in-line analysis significantly enhances memory footprint and slows down the main simulation. An accurate in-situ in-transit workflow is presented here to demonstrate a streaming analysis method focused on large-scale data generated from plasma particles in magnetic confinement fusion simulations, specifically using XGC. This workflow streamlines the computational data from simulation nodes to dedicated analysis nodes using asynchronous data movement. It thus enables parallelization of the data analysis and the main simulation without affecting the memory footprint and speed of the main simulation, offering insights into fusion plasma behavior with unparalleled detail and efficiency in near real time.

This presentation will introduce the newly established PESO project (https://pesoproject.org), which supports software-ecosystem stewardship and advancement, collaborating through the Consortium for the Advancement of Scientific Software (CASS). PESO’s vision is that investments by the U.S. Department of Energy (DOE) in software have maximum impact through a sustainable scientific software ecosystem consisting of high-quality libraries and tools that deliver the latest high-performance algorithms and capabilities to serve application needs at DOE and beyond. Key PESO goals are (1) enabling applications to leverage robust, curated scientific libraries and tools, especially in pursuit of improvement in high-end capabilities and energy efficiency by leveraging accelerator (GPU) devices, and (2) emphasizing software product quality, the continued fostering of software product communities, and the delivery of products, while advancing workforce inclusivity and sustainable career paths. PESO delivers and supports software products via Spack and E4S, and PESO provides porting and testing platforms leveraged across product teams to ensure code stability and portability. PESO also facilitates the delivery of other products, such as AI/ML libraries, as needed by the HPC community. PESO collaborates with CASS to transform independently developed products into a portfolio whose total is much more than the sum of its parts—establishing a trusted software ecosystem essential to DOE’s mission.

The CLAS12 experiment at Jefferson Lab produces immense volumes of raw data that demand immediate processing to enable prompt physics analysis. We have developed an innovative approach that seamlessly streams this data in real-time across the ESnet6 network backbone to multiple high-performance computing (HPC) facilities, including the Perlmutter supercomputer at NERSC, the Defiant cluster at Oak Ridge National Laboratory, and additional computational resources in NSF FABRIC testbed. These distributed resources collaboratively process the data stream and return results to Jefferson Lab in real time for validation, persistence, and final analysis—all accomplished without buffering, temporary storage, data loss, or latency issues.

This achievement is underpinned by three cutting-edge technologies developed by ESnet and Jefferson Lab. (1) EJFAT (ESnet JLab FPGA Accelerated Transport) is a high-speed data transport and load-balancing mechanism that utilizes FPGA acceleration to optimize real-time data transmission over ESnet6. (2) JIRIAF (JLab Integrated Research Infrastructure Across Facilities) provides a framework that streamlines resource management and optimizes HPC workloads across heterogeneous environments by leveraging Kubernetes and Virtual Kubelet to manage resources within user space dynamically. (3) ERSAP (Environment for Real-Time Streaming, Acquisition and Processing) is a reactive, actor-model, flow-based programming framework that decomposes data processing applications into small, monofunctional actors. This decomposition allows for independent scaling and optimization of each actor and context-aware data processing, facilitating operation in heterogeneous environments and utilizing diverse accelerators.
Our demonstration confirms that real-time remote data stream processing over high-speed networks without intermediate storage is feasible and highly efficient. This approach represents a significant advancement in data analysis workflows for large-scale physics experiments, offering a scalable and resilient solution for real-time scientific computing.

The Extreme-scale Scientific Software Stack (E4S) [https://e4s.io] is a curated, Spack based software distribution of 100+ HPC, EDA, and AI/ML packages. The Spack package manager is a core component of E4S and it is a platform for product integration and deployment of performance evaluation tools such as TAU, HPCToolkit, DyninstAPI, PAPI, etc. and supports both bare-metal and containerized deployment for CPU and GPU platforms. E4S provides a Spack binary cache and a set of base and full-featured container images with support for GPUs from NVIDIA, AMD, and Intel. E4S is a community effort to provide open-source software packages for developing, deploying, and running scientific applications and tools on HPC platforms. It has built a comprehensive, coherent software stack that enables application developers to productively develop highly parallel applications that effectively target diverse exascale architectures. E4S supports commercial cloud platforms, including AWS, Azure, and GCP with a Remote Desktop that simplifies performance tool integration and deployment. It also includes a container launch tool (e4s-cl) that allows binary distribution of applications by substituting MPI in the containerized application with the system MPI. It features e4s-alc, a tool to customize base container images provided by E4S by adding packages using Spack and OS package managers. These containers are available for download from the E4S website and DockerHub. This talk will describe performance tool and runtime systems integration issues with GPU runtimes in E4S and instrumentation APIs exposed for performance evaluation tools. To meet the needs of computational scientists to evaluate the performance of their parallel, scientific applications, we will focus on the use of E4S and the TAU Performance System(R) [http://tau.uoregon.edu] for performance data collection, analysis, and performance optimization. The talk will cover the runtime systems that TAU supports to transparently insert instrumentation hooks in the application to observe CPU and GPU performance and will provide updates on TAU and E4S’ latest features. Both TAU and E4S are released in liberal open-source licenses and supported by U.S. Department of Energy’s Exascale Computing Project [https://www.exascaleproject.org] and the PESO Project [https://pesoproject.org].

The DOE’s Integrated Research Infrastructure aims to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. The tools and technology emerging from this effort are already beginning to open up new avenues to how we use experimental and computational scientific instruments together. This session will show video demos of technologies and applications that define the current state of practice for time-sensitive, data integration-intensive, and long-term campaign patterns. As entry points into this new design space, they show a glimpse of what could be achieved if we coordinate to meet the difficult challenges of deploying IRI.

The Quantum Approximate Optimization Algorithm (QAOA) has demonstrated potential for solving combinatorial optimization problems on near-term quantum computing systems. However, QAOA encounters challenges when handling high-dimensional problems, primarily due to the large number of qubits required and the complexity of deep circuits, which constrain its scalability for practical applications. In this study, we introduce a distributed QAOA (DQAOA) that utilizes a high-performance computing-quantum computing (HPC-QC) integrated system. DQAOA employs distributed computing techniques to break down large tasks into smaller sub-tasks, which are then processed on the HPC-QC system. The global solution is iteratively refined by aggregating sub-solutions obtained from DQAOA, facilitating convergence towards the optimal solution. We demonstrate that DQAOA is capable of handling large-scale optimization problems (e.g., 1,000-bit problems), achieving a high approximation ratio (~99%) with a short time-to-solution (~276 s). To extend the applicability of this algorithm to material science, we have further developed an active learning algorithm integrated with DQAOA (AL-DQAOA), which combines machine learning, DQAOA, and active data production in an iterative process. Using AL-DQAOA, we successfully optimize photonic structures, demonstrating that our approach makes it feasible to solve real-world optimization problems using gate-based quantum computing. We anticipate that DQAOA will be applicable to a wide range of optimization challenges, with AL-DQAOA finding broader applications in material science.

The Extreme-scale Scientific Software Stack (E4S) [https://e4s.io] is a curated, Spack based software distribution of 100+ HPC, EDA, and AI/ML packages. The Spack package manager is a core component of E4S and it is a platform for product integration and deployment of performance evaluation tools such as TAU, HPCToolkit, DyninstAPI, PAPI, etc. and supports both bare-metal and containerized deployment for CPU and GPU platforms. E4S provides a Spack binary cache and a set of base and full-featured container images with support for GPUs from NVIDIA, AMD, and Intel. E4S is a community effort to provide open-source software packages for developing, deploying, and running scientific applications and tools on HPC platforms. It has built a comprehensive, coherent software stack that enables application developers to productively develop highly parallel applications that effectively target diverse exascale architectures. E4S supports commercial cloud platforms, including AWS, Azure, and GCP with a Remote Desktop that simplifies performance tool integration and deployment. It also includes a container launch tool (e4s-cl) that allows binary distribution of applications by substituting MPI in the containerized application with the system MPI. It features e4s-alc, a tool to customize base container images provided by E4S by adding packages using Spack and OS package managers. These containers are available for download from the E4S website and DockerHub. This talk will describe performance tool and runtime systems integration issues with GPU runtimes in E4S and instrumentation APIs exposed for performance evaluation tools. To meet the needs of computational scientists to evaluate the performance of their parallel, scientific applications, we will focus on the use of E4S and the TAU Performance System(R) [http://tau.uoregon.edu] for performance data collection, analysis, and performance optimization. The talk will cover the runtime systems that TAU supports to transparently insert instrumentation hooks in the application to observe CPU and GPU performance and will provide updates on TAU and E4S’ latest features. Both TAU and E4S are released in liberal open-source licenses and supported by U.S. Department of Energy’s Exascale Computing Project [https://www.exascaleproject.org] and the PESO Project [https://pesoproject.org].

In recent years, quantum computing has demonstrated the potential to revolutionize specific algorithms and applications by solving problems exponentially faster than classical computers. However, its widespread adoption for general computing remains a future prospect. This paper discusses the integration of quantum computing within High-Performance Computing (HPC) environments, focusing on a resource management framework designed to streamline quantum simulators’ use and enhance runtime performance and efficiency.

The complexity of the HPC I/O stack combined with gaps in the state-of-the-art profiling tools creates a barrier that does not help end-users and scientific application developers solve the I/O performance problems they encounter. Closing this gap requires cross-layer analysis combining multiple metrics and, when appropriate, drilling down to the source code. Dristhi is a multi-source interactive analysis framework for I/O to visualize traces, highlight bottlenecks, and help understand the behavior of applications. The framework contains an interactive I/O trace analysis component for end-users to visually inspect their applications’ I/O behavior, focusing on areas of interest and getting a clear picture of common root causes of I/O performance bottlenecks. Drishti maps common and well-known bottlenecks based on heuristic-based automatic detection of I/O performance bottlenecks and solution recommendations that users can implement.

The National Environmental Policy Act (NEPA) of 1969, is a bedrock and enduring environmental law in the United States with the express intent of fostering a productive harmony between humans and the environment for present and future generations. The NEPA statute and implementing regulations of the Council on Environmental Quality establish procedures requiring all federal agencies to consider and communicate the environmental effects in their planning and decisions to the public. Thus, agencies conduct environmental review and prepare a written document disclosing the potential environmental impact of proposed actions. Additionally, various studies are conducted by the scientific community to measure the impact of actions/projects on environment that eventually support NEPA review. Environmental data serves as a fundamental block in streamlining NEPA reviews where rich information contained in historical review and scientific documents could enable us to efficiently retrieve, analyze and find patterns that can inform future NEPA reviews. Currently documents are distributed across several agencies and its raw (PDF) form restricts from searchable and coupling with AI applications. We present two AI-ready data platforms, SearchNEPA and WindAssist that offers seamless access to past review and scientific documents. SearchNEPA is a cloud-driven AI-ready search tool that enables fast retrieval of policy-relevant information from over 28k environmental review documents. We developed several data standardization and augmentation techniques to improve the quality and access to the data records with the construction of NEPA ontology that include data objects such as project, process, document, public involvement, comments, and GIS. SearchNEPA runs on (i) single low-cost data storage in the cloud that accommodates diverse data types, (ii) open, standardized, and AI-compatible storage formats that facilitate comprehensive and fast data consumption, and (iii) end-to-end streaming that automates PDF data ingestion, metadata enrichment, and application endpoints. SearchNEPA opens a wide range of applications for both insight and foresight on NEPA performance and risk, including deep searches at multiple hierarchy, chatting with a collection of NEPA documents with Retrieval Augmented Generation (RAG) techniques, analytics from historical reviews that can inform future studies and geo-visualization of the NEPA projects through GIS information extracted through their documents. More recently, we publicly released a text corpus of data from more than 28k NEPA documents that powered SearchNEPA, the National Environmental Policy Act Text Corpus (NEPATEC1.0). WindAssist is a multimodal AI-driven data and knowledge discovery platform for wind energy-related documents collected from the Tethys database consisting of scientific articles, technical reports, and NEPA Reviews. It curates and unifies diverse data modalities into a structured AI-ready form via multimodal vectorization that can facilitate efficient information access. Specifically, we leverage large language model-based parsing to extract text, images, and tables from 2709 PDF documents comprising of 125k text chunks and 30k images. The platform offers a multimodal search tool, WindAssist-Search, which independently retrieves text and images that are semantically relevant to the query. In addition to the search, the multimodal conversation assistant tool, WindAssist-Chat, compiles the retrieved documents and images to generate responses to user queries. Unlike traditional keyword-based searches and chat assistant tools, our database ensures comprehensive data retrieval at a granular level, down to individual pages or text within images, and tools facilitate effective use of information to craft high quality responses. SearchNEPA and WindAssist aligns with the DOE efforts to transform the DOE’s vast repository of data into high quality AI-ready form and accelerate deployment of clean energy by streamlining siting and permitting processes via providing a one-stop-platform for various federal agencies (via OneID, login.gov, etc.) to (i) manage NEPA review and scientific documents, (ii) search for relevant information and (iii) fetch hidden insights which altogether support agency specific NEPA workflows.

Memory-to-memory data streaming between scientific instruments and remote high-performance computing (HPC) nodes has emerged as a key requirement to enable online processing of high-volume and high-velocity data for feature detection, experiment steering, and other purposes. In contrast to file transfer between scientific facilities for which a well-defined architecture exists in the form of science DMZ, data transfer nodes (DTN) and the associated tools, there is no well-defined infrastructure to enable efficient and secure memory-to-memory data streaming between scientific instruments and HPC nodes. It is especially important as both scientific instruments and HPC nodes lack direct external network connectivity. SciStream establishes a well-defined architecture and control protocols with an open-source implementation to enable distributed scientific workflows to use their choice of data streaming tools to move data from scientific instruments’ memory to HPC nodes’ memory. In this demo, we will describe the architecture and protocols that SciStream uses to establish authenticated and transparent connections between producers and consumers, and show how to stream data through SciStream. We will also discuss our experience integrating and running real-world scientific applications with SciStream.

Significant improvements in numerical weather prediction (NWP) can be attributed to modern satellite observing systems and the data assimilation techniques that enable the incorporation of satellite observations in NWP models. When new instruments are developed it is essential to understand their usefulness for weather prediction and climate monitoring, especially extreme events, in the context of existing observations. The value added by a new satellite instrument can be evaluated using a digital twin surrogate of the satellite instrument and by performing Observing System Simulation Experiments. The next generation NOAA Geostationary Extended Observations (GeoXO) satellites are planned to be operational in the early 2030s. The GeoXO payload will include an atmospheric sounder instrument (GXS) that will enable timely and improved forecasting of hurricanes and severe weather. We will demonstrate and discuss the potential advantages of GXS, using a digital twin prototype for the GXS, toward forecasting a hypothetical severe weather event. The prototype GXS digital twin was developed at the University of Wisconsin-Madison Space Science and Engineering Center, using data from a global 1-km seasonal simulation completed by the European Center for Medium Range Weather Forecasts at the Oak Ridge Leadership Computing Facility via a DOE ASCR INCITE award.

State-of-the-art physics drivers, such as Lawrence Livermore National Laboratory’s National Ignition Facility and ITER, along with multi-million-dollar university-scale facilities, are not optimal platforms for pioneering wide-ranging explorations in digital infrastructure and closed-loop control schemes using AI, as they are engaged in critical scientific research with expensive equipment. To address this challenge, we are developing modular, flexible small-scale surrogate facilities, termed “sidekick facilities,” which replicate the complex non-physics aspects of closed-loop autonomous operations and enhance data generation and acquisition rates.

The convergence of recent advancements in artificial intelligence, robotics, advanced instrumentation, edge and high-performance computing, and high-throughput networks is ushering in a transformative era for self-driving autonomous laboratories. Yet, without a coordinated scientific approach, the landscape risks becoming fragmented with disparate solutions that lack seamless integration, leading to siloed “smart” labs and user facilities. The Interconnected Science Ecosystem (INTERSECT) initiative is co-designing a common ecosystem and building interoperable “self-driving” autonomous laboratories across the ORNL directorates. The primary goal of the INTERSECT initiative is to lead and coordinate smart laboratory efforts across ORNL to unlock unprecedented efficiencies and groundbreaking new research approaches/outcomes.

In turbulent and stochastic magnetic fusion plasma, the plasma transport analysis using grid quantities often misses important physics, such as subgrid phenomena and particle auto-correlation. Traditional in-line analysis significantly enhances memory footprint and slows down the main simulation. An accurate in-situ in-transit workflow is presented here to demonstrate a streaming analysis method focused on large-scale data generated from plasma particles in magnetic confinement fusion simulations, specifically using XGC. This workflow streamlines the computational data from simulation nodes to dedicated analysis nodes using asynchronous data movement. It thus enables parallelization of the data analysis and the main simulation without affecting the memory footprint and speed of the main simulation, offering insights into fusion plasma behavior with unparalleled detail and efficiency in near real time.

Several DoE labs are collaboratively building a set of composable system management tools with the goal of being capable of running the next generation(s) of large HPC procurements. Alex Lovell-Troy from LANL will demonstrate several of the components including hardware discovery, federation, and inventory management and discuss the overall architecture of the project. This will be especially useful for system administrators responsible for managing HPC systems. The OpenCHAMI consortium is focused on reducing toil for sysadmins with responsibility for multiple systems.

State-of-the-art physics drivers, such as Lawrence Livermore National Laboratory’s National Ignition Facility and ITER, along with multi-million-dollar university-scale facilities, are not optimal platforms for pioneering wide-ranging explorations in digital infrastructure and closed-loop control schemes using AI, as they are engaged in critical scientific research with expensive equipment. To address this challenge, we are developing modular, flexible small-scale surrogate facilities, termed “sidekick facilities,” which replicate the complex non-physics aspects of closed-loop autonomous operations and enhance data generation and acquisition rates.

The DOE’s Integrated Research Infrastructure aims to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. The tools and technology emerging from this effort are already beginning to open up new avenues to how we use experimental and computational scientific instruments together. This session will show video demos of technologies and applications that define the current state of practice for time-sensitive, data integration-intensive, and long-term campaign patterns. As entry points into this new design space, they show a glimpse of what could be achieved if we coordinate to meet the difficult challenges of deploying IRI.

Inspired by principles of the brain, SpiNNaker2 is a many-core neuromorphic chip designed for large-scale asynchronous processing. The flexibility provided by its reconfigurability, scalability afforded by its real-time, large-scale mesh, and native support for hybrid acceleration of symbolic spiking and deep neural networks make SpiNNaker2 a unique computing platform. Sandia National Laboratories has partnered with SpiNNcloud to explore computational advantages neuromorphic computing can enable for a variety of applications. This demo will showcase the SpiNNaker2 architecture across a range of applications – large language models (LLMs), optimizations, and AI/ML.

Inspired by principles of the brain, SpiNNaker2 is a many-core neuromorphic chip designed for large-scale asynchronous processing. The flexibility provided by its reconfigurability, scalability afforded by its real-time, large-scale mesh, and native support for hybrid acceleration of symbolic spiking and deep neural networks make SpiNNaker2 a unique computing platform. Sandia National Laboratories has partnered with SpiNNcloud to explore computational advantages neuromorphic computing can enable for a variety of applications. This demo will showcase the SpiNNaker2 architecture across a range of applications – large language models (LLMs), optimizations, and AI/ML.

This demonstration shows how the Lightweight Distributed Metric Service’s (LDMS) capabilities enable autonomous operations through the capability to gather and perform run time analysis of application and system data and to provide a real-time feedback path to optimize resource utilization.

The CLAS12 experiment at Jefferson Lab produces immense volumes of raw data that demand immediate processing to enable prompt physics analysis. We have developed an innovative approach that seamlessly streams this data in real-time across the ESnet6 network backbone to multiple high-performance computing (HPC) facilities, including the Perlmutter supercomputer at NERSC, the Defiant cluster at Oak Ridge National Laboratory, and additional computational resources in NSF FABRIC testbed. These distributed resources collaboratively process the data stream and return results to Jefferson Lab in real time for validation, persistence, and final analysis—all accomplished without buffering, temporary storage, data loss, or latency issues.

This achievement is underpinned by three cutting-edge technologies developed by ESnet and Jefferson Lab. (1) EJFAT (ESnet JLab FPGA Accelerated Transport) is a high-speed data transport and load-balancing mechanism that utilizes FPGA acceleration to optimize real-time data transmission over ESnet6. (2) JIRIAF (JLab Integrated Research Infrastructure Across Facilities) provides a framework that streamlines resource management and optimizes HPC workloads across heterogeneous environments by leveraging Kubernetes and Virtual Kubelet to manage resources within user space dynamically. (3) ERSAP (Environment for Real-Time Streaming, Acquisition and Processing) is a reactive, actor-model, flow-based programming framework that decomposes data processing applications into small, monofunctional actors. This decomposition allows for independent scaling and optimization of each actor and context-aware data processing, facilitating operation in heterogeneous environments and utilizing diverse accelerators.

Our demonstration confirms that real-time remote data stream processing over high-speed networks without intermediate storage is feasible and highly efficient. This approach represents a significant advancement in data analysis workflows for large-scale physics experiments, offering a scalable and resilient solution for real-time scientific computing.

The field of neuromorphic computing looks to the brain for inspiration in designing algorithms and architectures. As the field matures, it is crucial to explore the capabilities of top neuromorphic systems. Here we explore trends in the development of large-scale neuromorphic systems, as well as consider applications neuromorphic approaches may impact. By highlighting these advancements, we aim to demonstrate the emerging role neuromorphic computing may have for HPC.

This demonstration will showcase new capabilities being piloted by the Oak Ridge Leadership Computing Facility (OLCF) to support deployment of biomedical Large Language Model (LLM) pipelines on ORNL’s Frontier Citadel environment. This demonstration features work resulting from a collaboration between ORNL, NIH’s National Center for Advancing Translational Sciences (NCATS), and the National Science Foundation (NSF) as part of the NAIRR Secure Pilot program. The demonstration showcases biomedical application workflows through JupyterHub on OLCF’s Frontier supercomputer and a prototype for “Ask AIthena”, an intuitive chat interface using private and secure LLMs on OLCF’s Frontier supercomputer and NCATS’s DALÍ HPC system. Presenters: Nick Schaub, Hugo Hernández, Matt Ezell, Verónica Melesse Vergara

We present an AI-driven framework for catalyst discovery, combining linguistic reasoning with quantum chemistry feedback. Our approach uses large language models (LLMs) to generate hypotheses and graph neural networks (GNNs) to evaluate 3D atomistic structures. The iterative process incorporates structural evaluation, reaction pathways, and stability assessments. Automated planning methods guide the exploration, rivaling expert-driven approaches. This integration of language-guided reasoning and computational chemistry feedback accelerates trustworthy catalyst discovery for sustainable chemical processes.

Quantum networks promise to deliver revolutionary applications such as distributing cryptographic keys with provable security, ultra-high-precision distributed sensing, and synchronizing clocks with unprecedented accuracy. Recent breakthroughs in quantum engineering have allowed experimental realizations of quantum network prototypes. A key engineering challenge is building networks that scale along the dimensions of node count, user count, distance, and application diversity. Achieving this goal requires advances in hardware engineering, network architectures and protocols. Quantum network simulations can help in understanding the tradeoffs of alternative quantum network architectures, optimizing quantum hardware, and developing a robust control plane. Simulator of QUantum Network Communication (SeQUeNCe) is a customizable, discrete-event quantum network simulator that models quantum hardware and network protocols. SeQUeNCe uses a modularized design that allows the testing of alternative quantum network protocols and hardware models and the study of their interactions. In this demo, we will introduce SeQUeNCe and present its design, interface, and capabilities.

The SINES DC facility in Sines, Portugal, developed by Start Campus, is a 1.2 GW data center campus poised to become Europe’s largest and most sustainable by 2030. This ambitious project features a groundbreaking seawater cooling system, using the ocean as a natural heat sink without depleting water resources. With an investment of €8.5 billion, SINES DC will operate entirely on renewable energy, aiming for an industry-leading Power Usage Effectiveness (PUE) of 1.1. Its first building, SIN01, is designed for up to 15 MW of IT load and incorporates both liquid and air-cooling technologies. The facility’s Liquid Cooled Lab (LCL) will support 1 MW of IT load, providing a testing environment for cutting-edge cooling technologies like direct-to-chip and immersion cooling. By utilizing ocean water, the campus achieved a Water Usage Effectiveness (WUE) of zero. BNL researchers lead the study and will be discussing the carbon reduction and chilled water flow optimization, setting a new benchmark for sustainability and efficiency in data center operations worldwide.

The rapid advancement of large language models (LLMs) has revolutionized various domains, including chemistry and molecular discovery. However, the ability of LLMs to access and reason over domain-specific knowledge and tools remains a significant challenge. In this demo, we present CACTUS (Chemistry AI Agent Connecting Tool-Usage to Science), an enhanced version of the CACTUS agent that leverages open-source LLMs and integrates domain-specific tools to enable accurate and efficient reasoning and problem-solving in chemistry. In this demo, I will discuss the performance of state-of-the-art open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a comprehensive benchmark of chemistry questions. I will also highlight the impact of domain-specific prompting and hardware configurations on model performance, emphasizing the significance of prompt engineering and the feasibility of deploying smaller models on consumer-grade hardware without compromising accuracy. We will present real-world applications, including molecular discovery and material design, where CACTUS aids in hypothesis testing and validation, accelerating the discovery process and enabling data-driven decision-making.

High-performance computing applications are increasingly executed as complex workflows integrating AI, visualization, and in-situ analysis. To meet the demands of these modern applications, middleware tools must adapt to the challenges of managing distributed datasets, complex queries, and large-scale data analysis. This demonstration showcases new features designed to efficiently support scientific campaigns involving multiple runs, terabytes of data per run, and the visualization and analysis of primary variables and derived data. We will highlight solutions for efficient local and in-situ data handling, ensuring optimal performance and usability for researchers.

High-performance computing applications are increasingly executed as complex workflows integrating AI, visualization, and in-situ analysis. To meet the demands of these modern applications, middleware tools must adapt to the challenges of managing distributed datasets, complex queries, and large-scale data analysis. This demonstration showcases new features designed to efficiently support scientific campaigns involving multiple runs, terabytes of data per run, and the visualization and analysis of primary variables and derived data. We will highlight solutions for efficient local and in-situ data handling, ensuring optimal performance and usability for researchers.

This demonstration showcases the use of the ALCF Polaris supercomputer for processing data (both online and offline) from Advanced Photon Source (APS) experiments in near real-time. It shows how the APS utilizes the features of Globus Compute to create end-to-end data workflows connecting APS instruments with computing resources at the ALCF. And how ALCF infrastructure innovations, including beamline service accounts, facility allocations and instrument suballocations, and management nodes facilitate this integration. These capabilities will be demonstrated for a high-impact technique at the APS, X-ray Photon Correlation Spectroscopy (XPCS). The APS, located at Argonne National Laboratory, is a synchrotron light source funded by the U.S Department of Energy (DOE), Office of Science-Basic Energy Sciences (BES) to produce high-energy, high-brightness x-ray beams. The APS has become one of the largest scattering user facilities in the world, averaging 5,500 unique users and producing more than 2,000 scientific publications every year. Scientists from every state in the US and international users utilize the 68 beamlines at the APS to conduct cutting-edge basic and applied research in the fields of materials science, biological and life science, physics, chemistry, environmental, geophysical, and planetary science, and innovative x-ray instrumentation. As part of the APS Upgrade project, the facility has replaced the storage ring and is in the midst of commissioning new and upgraded beamlines and instruments. More than ever before, advanced computational approaches and technologies are essential in fully unlocking the scientific potential of the facility. The upgraded source opens the door for new measurement techniques and increases in throughput, which, coupled to technological advances in detectors, new multi-modal data, and advances in data analysis algorithms, including artificial intelligence and machine learning (AI/ML), will open a new era of synchrotron light source enabled research. In particular, the high-brightness, and increase in coherent x-ray flux at the new APS is leading to significant increases in data rates and experiment complexity that can only be addressed with advanced computing capabilities. Join us to see firsthand how collaboration between the APS and the ALCF is driving cutting-edge research forward.

Several DoE labs are collaboratively building a set of composable system management tools with the goal of being capable of running the next generation(s) of large HPC procurements. Alex Lovell-Troy from LANL will demonstrate several of the components including hardware discovery, federation, and inventory management and discuss the overall architecture of the project. This will be especially useful for system administrators responsible for managing HPC systems. The OpenCHAMI consortium is focused on reducing toil for sysadmins with responsibility for multiple systems.

Significant improvements in numerical weather prediction (NWP) can be attributed to modern satellite observing systems and the data assimilation techniques that enable the incorporation of satellite observations in NWP models. When new instruments are developed it is essential to understand their usefulness for weather prediction and climate monitoring, especially extreme events, in the context of existing observations. The value added by a new satellite instrument can be evaluated using a digital twin surrogate of the satellite instrument and by performing Observing System Simulation Experiments. The next generation NOAA Geostationary Extended Observations (GeoXO) satellites are planned to be operational in the early 2030s. The GeoXO payload will include an atmospheric sounder instrument (GXS) that will enable timely and improved forecasting of hurricanes and severe weather. We will demonstrate and discuss the potential advantages of GXS, using a digital twin prototype for the GXS, toward forecasting a hypothetical severe weather event. The prototype GXS digital twin was developed at the University of Wisconsin-Madison Space Science and Engineering Center, using data from a global 1-km seasonal simulation completed by the European Center for Medium Range Weather Forecasts at the Oak Ridge Leadership Computing Facility via a DOE ASCR INCITE award.

The National Environmental Policy Act (NEPA) of 1969, is a bedrock and enduring environmental law in the United States with the express intent of fostering a productive harmony between humans and the environment for present and future generations. The NEPA statute and implementing regulations of the Council on Environmental Quality establish procedures requiring all federal agencies to consider and communicate the environmental effects in their planning and decisions to the public. Thus, agencies conduct environmental review and prepare a written document disclosing the potential environmental impact of proposed actions. Additionally, various studies are conducted by the scientific community to measure the impact of actions/projects on environment that eventually support NEPA review. Environmental data serves as a fundamental block in streamlining NEPA reviews where rich information contained in historical review and scientific documents could enable us to efficiently retrieve, analyze and find patterns that can inform future NEPA reviews. Currently documents are distributed across several agencies and its raw (PDF) form restricts from searchable and coupling with AI applications. We present two AI-ready data platforms, SearchNEPA and WindAssist that offers seamless access to past review and scientific documents. SearchNEPA is a cloud-driven AI-ready search tool that enables fast retrieval of policy-relevant information from over 28k environmental review documents. We developed several data standardization and augmentation techniques to improve the quality and access to the data records with the construction of NEPA ontology that include data objects such as project, process, document, public involvement, comments, and GIS. SearchNEPA runs on (i) single low-cost data storage in the cloud that accommodates diverse data types, (ii) open, standardized, and AI-compatible storage formats that facilitate comprehensive and fast data consumption, and (iii) end-to-end streaming that automates PDF data ingestion, metadata enrichment, and application endpoints. SearchNEPA opens a wide range of applications for both insight and foresight on NEPA performance and risk, including deep searches at multiple hierarchy, chatting with a collection of NEPA documents with Retrieval Augmented Generation (RAG) techniques, analytics from historical reviews that can inform future studies and geo-visualization of the NEPA projects through GIS information extracted through their documents. More recently, we publicly released a text corpus of data from more than 28k NEPA documents that powered SearchNEPA, the National Environmental Policy Act Text Corpus (NEPATEC1.0). WindAssist is a multimodal AI-driven data and knowledge discovery platform for wind energy-related documents collected from the Tethys database consisting of scientific articles, technical reports, and NEPA Reviews. It curates and unifies diverse data modalities into a structured AI-ready form via multimodal vectorization that can facilitate efficient information access. Specifically, we leverage large language model-based parsing to extract text, images, and tables from 2709 PDF documents comprising of 125k text chunks and 30k images. The platform offers a multimodal search tool, WindAssist-Search, which independently retrieves text and images that are semantically relevant to the query. In addition to the search, the multimodal conversation assistant tool, WindAssist-Chat, compiles the retrieved documents and images to generate responses to user queries. Unlike traditional keyword-based searches and chat assistant tools, our database ensures comprehensive data retrieval at a granular level, down to individual pages or text within images, and tools facilitate effective use of information to craft high quality responses. SearchNEPA and WindAssist aligns with the DOE efforts to transform the DOE’s vast repository of data into high quality AI-ready form and accelerate deployment of clean energy by streamlining siting and permitting processes via providing a one-stop-platform for various federal agencies (via OneID, login.gov, etc.) to (i) manage NEPA review and scientific documents, (ii) search for relevant information and (iii) fetch hidden insights which altogether support agency specific NEPA workflows.

The field of neuromorphic computing looks to the brain for inspiration in designing algorithms and architectures. As the field matures, it is crucial to explore the capabilities of top neuromorphic systems. Here we explore trends in the development of large-scale neuromorphic systems, as well as consider applications neuromorphic approaches may impact. By highlighting these advancements, we aim to demonstrate the emerging role neuromorphic computing may have for HPC.

This demonstration shows how the Lightweight Distributed Metric Service’s (LDMS) capabilities enable autonomous operations through the capability to gather and perform run time analysis of application and system data and to provide a real-time feedback path to optimize resource utilization.

In recent years, quantum computing has demonstrated the potential to revolutionize specific algorithms and applications by solving problems exponentially faster than classical computers. However, its widespread adoption for general computing remains a future prospect. This paper discusses the integration of quantum computing within High-Performance Computing (HPC) environments, focusing on a resource management framework designed to streamline quantum simulators’ use and enhance runtime performance and efficiency.

The rapid advancement of large language models (LLMs) has revolutionized various domains, including chemistry and molecular discovery. However, the ability of LLMs to access and reason over domain-specific knowledge and tools remains a significant challenge. In this demo, we present CACTUS (Chemistry AI Agent Connecting Tool-Usage to Science), an enhanced version of the CACTUS agent that leverages open-source LLMs and integrates domain-specific tools to enable accurate and efficient reasoning and problem-solving in chemistry. In this demo, I will discuss the performance of state-of-the-art open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a comprehensive benchmark of chemistry questions. I will also highlight the impact of domain-specific prompting and hardware configurations on model performance, emphasizing the significance of prompt engineering and the feasibility of deploying smaller models on consumer-grade hardware without compromising accuracy. We will present real-world applications, including molecular discovery and material design, where CACTUS aids in hypothesis testing and validation, accelerating the discovery process and enabling data-driven decision-making.

The convergence of recent advancements in artificial intelligence, robotics, advanced instrumentation, edge and high-performance computing, and high-throughput networks is ushering in a transformative era for self-driving autonomous laboratories. Yet, without a coordinated scientific approach, the landscape risks becoming fragmented with disparate solutions that lack seamless integration, leading to siloed “smart” labs and user facilities. The Interconnected Science Ecosystem (INTERSECT) initiative is co-designing a common ecosystem and building interoperable “self-driving” autonomous laboratories across the ORNL directorates. The primary goal of the INTERSECT initiative is to lead and coordinate smart laboratory efforts across ORNL to unlock unprecedented efficiencies and groundbreaking new research approaches/outcomes.

The DOE High Performance Computing for Energy Innovation (HPC4EI) Program offers U.S. manufacturers access to the nation’s most powerful supercomputers, advanced software models, and specialized expertise. Utilizing these supercomputers national laboratory scientists work with industrial partners to improve manufacturing processes, address product lifecycle energy consumption, increase the efficiency of energy conversion, improve the performance of energy storage technologies, and improve the performance of materials in complex environments.  This advanced research capability enables manufacturers to reduce energy and material use during manufacturing—and design and manufacture better, higher-value products that are more energy efficient during end use.

Deep learning (DL) models have shown the potential to greatly accelerate first-principles calculations while maintaining a high degree of accuracy. In particular, graph neural networks (GNNs) are effective DL models for material science because they take advantage of the topological information of the data samples by representing the atomic configurations of each atomistic structure as graphs. In this representation, each atom is represented as a node in the graph, and the edges between nodes represent the bonding interactions between the atoms. In this demo, Max and his team will detail the utility and use of the HydraGNN, a distributed graph neural network architecture for robust and scalable training on large volumes of atomistic materials modeling data. Max and his team will discuss how to effectively scale HydraGNN models on the Perlmutter supercomputer at NERSC as well as the Summit and Frontier supercomputers at the OLCF.

Deep learning (DL) models have shown the potential to greatly accelerate first-principles calculations while maintaining a high degree of accuracy. In particular, graph neural networks (GNNs) are effective DL models for material science because they take advantage of the topological information of the data samples by representing the atomic configurations of each atomistic structure as graphs. In this representation, each atom is represented as a node in the graph, and the edges between nodes represent the bonding interactions between the atoms. In this demo, Max and his team will detail the utility and use of the HydraGNN, a distributed graph neural network architecture for robust and scalable training on large volumes of atomistic materials modeling data. Max and his team will discuss how to effectively scale HydraGNN models on the Perlmutter supercomputer at NERSC as well as the Summit and Frontier supercomputers at the OLCF.