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DOE OSTI2020

CCAMP: An Integrated Translation and Optimization Framework for OpenACC and OpenMP

Heterogeneous computing and exploration into specialized accelerators are inevitable in current and future supercomputers. Although this diversity of devices is promising for performance, the array of architectures presents programming challenges. High-level programming strategies have emerged to face these challenges, such as the OpenMP offloading model and OpenACC. The varying levels of support for these standards, however, within vendor-specific and open-source tools, as well as the lack of performance portability across devices, have prevented the standards from achieving their goals. To address these shortcomings, we present CCAMP, an OpenMP and OpenACC interoperable framework. CCAMP provides two primary facilities: language translation between the two standards and device-specific directive optimization within each standard. We show that by using the CCAMP framework, programmers can easily transplant non-portable code into new ecosystems for new architectures. Additionally, by using CCAMP device-specific directive optimizations, users can achieve optimized performance across architectures using a single source code.

Lambert, Jacob↗

DOE OSTI2025

EnergyPlus-MCP: A model-context-protocol server for ai-driven building energy modeling

Traditional building energy modeling with the EnergyPlus building performance simulation engine requires domain expertise, programming skills, and intensive manual efforts limiting its effective adoption. This paper introduces EnergyPlus-MCP, the first open-source Model Context Protocol (MCP) server specifically designed for EnergyPlus simulation workflows, establishing a new foundational infrastructure for AI-driven building energy modeling. The MCP server implements a layered architecture with 35 specialized tools spanning model management, editing and analysis, HVAC and other systems configuration inspection, and simulation execution, enabling Large Language Models to interact with EnergyPlus through conversational interfaces. The server addresses critical workflow barriers by automating model validation, streamlining energy efficiency measures modification, and providing intelligent output management with interactive visualization. Through practical demonstrations using a multi-zone building retrofit analysis, we show how the EnergyPlus-MCP server significantly reduces manual efforts while maintaining full simulation rigor. By providing accessible natural language interfaces to sophisticated building energy analysis, this approach enables scalable deployment of simulation expertise across public and private organizations, educational institutions, and research teams, fundamentally transforming traditional building energy modeling practices.

AI↗

DOE OSTI2023

Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes

We explore the performance and portability of the high-level programming models: the LLVM-based Julia and Python/Numba, and Kokkos on high-performance computing (HPC) nodes: AMD Epyc CPUs and MI250X graphical processing units (GPUs) on Frontier’s test bed Crusher system and Ampere’s Arm-based CPUs and NVIDIA’s A100 GPUs on the Wombat system at the Oak Ridge Leadership Computing Facilities. We compare the default performance of a hand-rolled dense matrix multiplication algorithm on CPUs against vendor-compiled C/OpenMP implementations, and on each GPU against CUDA and HIP. Rather than focusing on the kernel optimization per-se, we select this naive approach to resemble exploratory work in science and as a lower-bound for performance to isolate the effect of each programming model. Julia and Kokkos perform comparably with C/OpenMP on CPUs, while Julia implementations are competitive with CUDA and HIP on GPUs. Performance gaps are identified on NVIDIA A100 GPUs for Julia’s single precision and Kokkos, and for Python/Numba in all scenarios. We also comment on half-precision support, productivity, performance portability metrics, and platform readiness. We expect to contribute to the understanding and direction for high-level, high-productivity languages in HPC as the first-generation exascale systems are deployed.

Godoy, William↗

DOE OSTI2023

Accelerating Adoption of Energy-Efficient Technology with the Thermalize Model

The accelerating impacts of climate change destabilize food systems and ecosystems alongside the communities that rely on them. Thus, climate resilience is a health equity issue at both a physical (relating to biological impacts of pollution and environmental degradation) and a cultural level (loss of subsistence lifestyles, languages, etc). With roughly 20% of US energy-related greenhouse gas emissions stemming from heating, cooling, and powering households, accessible energy-efficient home retrofits are a critical element of climate change mitigation strategies. For communities already producing electricity from renewable sources, like Juneau, incentivizing ductless heat pump upgrades and energy efficiency retrofits provides an accessible pathway to transition households away from oil heating and drastically reduce emissions. Thermalize Juneau is a pilot program that will explore a repeatable framework for accelerating the adoption of energy-efficient technology in Alaska communities. This poster will describe Thermalize Juneau's goals and framework, progress through spring 2021, and future plans.

ductless heat pumps↗

DOE OSTI2017

Domain Specific Language Support for Exascale

Domain-Specific Languages (DSLs) offer an attractive path to Exascale software since they provide expressive power through appropriate abstractions and enable domain-specific optimizations. But the advantages of a DSL compete with the difficulties of implementing a DSL, even for a narrowly defined domain. The DTEC project addresses how a variety of DSLs can be easily implemented to leverage existing compiler analysis and transformation capabilities within the ROSE open source compiler as part of a research program focusing on Exascale challenges. The OSU contributions to the DTEC project are in the area of code generation from high-level DSL descriptions, as well as verification of the automatically-generated code.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Collaborative development of Estonian nuclear master's program

In 2009 Estonia approved the National Development Plan for the Energy Sector, including the nuclear energy option. This can be realized by construction of a nuclear power plant (NPP) in Estonia or by participation in neighboring nuclear projects (e.g., Lithuania and/or Finland). Either option requires the availability of competent personnel. It is necessary to prepare specialists with expertise in all aspects related to nuclear infrastructure and to meet workforce needs (e.g. energy enterprises, public agencies, municipalities). Estonia's leading institutions of higher education and research with the support of the European Social Fund have announced in this context a new nuclear master's curriculum to be developed. The language of instruction will be English. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗

DOE OSTI2023

MILK : a Python scripting interface to MAUD for automation of Rietveld analysis

Modern diffraction experiments ( e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies ( i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction ( MAUD ), the MAUD Interface Language Kit ( MILK ) is developed along with an updated text batch interface for MAUD . The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD . Using MILK , model selection ( e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD . Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye–Scherrer , a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Titan on the Red (advanced AI / ML system) [Slides]

Born out of a need to make LANL Weapons Program archival material available to scientists and engineers. Material is the end result of decades of consolidation of mini-libraries and mini archives at LANL. Latest consolidation brought together LANL’s digital archives and physical archives. This houses over 75 years of nuclear weapons research, designs, procedures, videos, photos, and other reports. For the Titan on the Red machine learning project, the system must be able to automatically extract metadata from digitized documents, perform natural-language search, enforce security classification and NTK protocols, be expandable to ingest various data stores (Online Vault, PDMLink, shared drives, SharePoint, etc.), and utilize commercial, public domain, and LANL ontologies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2012

VCAT: Visual Crosswalk Analysis Tool [Slides]

VCAT is a knowledge modeling and analysis tool. It was synthesized from ideas in functional analysis, business process modeling, and complex network science. VCAT discovers synergies by analyzing natural language descriptions. Specifically, it creates visual analytic perspectives that capture intended organization structures, then overlays the serendipitous relationships that point to potential synergies within an organization or across multiple organizations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗

DOE OSTI2019

Development of eXternal Nuclear Reaction Analysis (XNRA) Detection Technique for Quantifying Light Isotope Concentrations

The National Nuclear Security Administration’s Tritium Sustainment Program is responsible for the design, development, demonstration, testing, analysis, and characterization of tritium-producing burnable absorber rods (TPBARs) and their components used to produce tritium for the nation’s strategic stockpile. The FY19 call for proposals included the specific basic science research topic, “Demonstration and evaluation of advanced characterization methods, particularly for quantifying the concentration of light isotopes ( 1 H, 3 H, 3 He, and 4 He, 6 Li and 7 Li) in metal or ceramic matrices”. Last year the same language appeared in the call for proposals, and a project IWO-389859 was awarded to the Ion Beam Lab (IBL) at Sandia-NM which was successful using Elastic Recoil Detection, but in the future could have resulted in tritium contamination that jeopardized other equally important NNSA projects. An alternative approach using deuterium nuclear reaction analysis was proposed and funded in FY2019 which was also successful and eliminated any possibility of contaminating the Ion Beam Laboratory with tritium, and will be described in this report.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

Programming with BIG data in R: Scaling analytics from one to thousands of nodes

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Performance and Architecture Lab Modeling Tool

Analytical application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult. Furthermore, models are frequently expressed in forms that are hard to distribute and validate. The Performance and Architecture Lab Modeling tool, or Palm, is a modeling tool designed to make application modeling easier. Palm provides a source code modeling annotation language. Not only does the modeling language divide the modeling task into sub problems, it formally links an application's source code with its model. This link is important because a model's purpose is to capture application behavior. Furthermore, this link makes it possible to define rules for generating models according to source code organization. Palm generates hierarchical models according to well-defined rules. Given an application, a set of annotations, and a representative execution environment, Palm will generate the same model. A generated model is a an executable program whose constituent parts directly correspond to the modeled application. Palm generates models by combining top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. A model's hierarchy is defined by static and dynamic source code structure. Because Palm coordinates models and source code, Palm's models are 'first-class' and reproducible. Palm automates common modeling tasks. For instance, Palm incorporates measurements to focus attention, represent constant behavior, and validate models. Palm's workflow is as follows. The workflow's input is source code annotated with Palm modeling annotations. The most important annotation models an instance of a block of code. Given annotated source code, the Palm Compiler produces executables and the Palm Monitor collects a representative performance profile. The Palm Generator synthesizes a model based on the static and dynamic mapping of annotations to program behavior. The model -- an executable program -- is a hierarchical composition of annotation functions, synthesized functions, statistics for runtime values, and performance measurements.

Tallent, Nathan↗

DOE OSTI2011

Renewable Energy Requirements for Future Building Codes: Options for Compliance

As the model energy codes are improved to reach efficiency levels 50 percent greater than current codes, use of on-site renewable energy generation is likely to become a code requirement. This requirement will be needed because traditional mechanisms for code improvement, including envelope, mechanical and lighting, have been pressed to the end of reasonable limits. Research has been conducted to determine the mechanism for implementing this requirement (Kaufman 2011). Kaufmann et al. determined that the most appropriate way to structure an on-site renewable requirement for commercial buildings is to define the requirement in terms of an installed power density per unit of roof area. This provides a mechanism that is suitable for the installation of photovoltaic (PV) systems on future buildings to offset electricity and reduce the total building energy load. Kaufmann et al. suggested that an appropriate maximum for the requirement in the commercial sector would be 4 W/ft 2 of roof area or 0.5 W/ft 2 of conditioned floor area. As with all code requirements, there must be an alternative compliance path for buildings that may not reasonably meet the renewables requirement. This might include conditions like shading (which makes rooftop PV arrays less effective), unusual architecture, undesirable roof pitch, unsuitable building orientation, or other issues. In the short term, alternative compliance paths including high performance mechanical equipment, dramatic envelope changes, or controls changes may be feasible. These options may be less expensive than many renewable systems, which will require careful balance of energy measures when setting the code requirement levels. As the stringency of the code continues to increase however, efficiency trade-offs will be maximized, requiring alternative compliance options to be focused solely on renewable electricity trade-offs or equivalent programs. One alternate compliance path includes purchase of Renewable Energy Credits (RECs). Each REC represents a specified amount of renewable electricity production and provides an offset of environmental externalities associated with non-renewable electricity production. The purpose of this paper is to explore the possible issues with RECs and comparable alternative compliance options. Existing codes have been examined to determine energy equivalence between the energy generation requirement and the RECs alternative over the life of the building. The price equivalence of the requirement and the alternative are determined to consider the economic drivers for a market decision. This research includes case studies that review how the few existing codes have incorporated RECs and some of the issues inherent with REC markets. Section 1 of the report reviews compliance options including RECs, green energy purchase programs, shared solar agreements and leases, and other options. Section 2 provides detailed case studies on codes that include RECs and community based alternative compliance methods. The methods the existing code requirements structure alternative compliance options like RECs are the focus of the case studies. Section 3 explores the possible structure of the renewable energy generation requirement in the context of energy and price equivalence. The price of RECs have shown high variation by market and over time which makes it critical to for code language to be updated frequently for a renewable energy generation requirement or the requirement will not remain price-equivalent over time. Section 4 of the report provides a maximum case estimate for impact to the PV market and the REC market based on the Kaufmann et al. proposed requirement levels. If all new buildings in the commercial sector complied with the requirement to install rooftop PV arrays, nearly 4,700 MW of solar would be installed in 2012, a major increase from EIA estimates of 640 MW of solar generation capacity installed in 2009. The residential sector could contribute roughly an additional 2,300 MW based on the same code requirement levels of 4 W/ft 2 of roof area. Section 5 of the report provides a basic framework for draft code language recommendations based on the analysis of the alternative compliance levels.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2011

PylotDB - A Database Management, Graphing, and Analysis Tool Written in Python

PylotDB, written completely in Python, provides a user interface (UI) with which to interact with, analyze, graph data from, and manage open source databases such as MySQL. The UI mitigates the user having to know in-depth knowledge of the database application programming interface (API). PylotDB allows the user to generate various kinds of plots from user-selected data; generate statistical information on text as well as numerical fields; backup and restore databases; compare database tables across different databases as well as across different servers; extract information from any field to create new fields; generate, edit, and delete databases, tables, and fields; generate or read into a table CSV data; and similar operations. Since much of the database information is brought under control of the Python computer language, PylotDB is not intended for huge databases for which MySQL and Oracle, for example, are better suited. PylotDB is better suited for smaller databases that might be typically needed in a small research group situation. PylotDB can also be used as a learning tool for database applications in general.

Barnette, DanielW.↗

DOE OSTI2025

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.

Young, Aaron [ORNL] (ORCID:0000000254484667)↗

DOE OSTI2015

NSR&D Program Fiscal Year (FY) 2015 Call for Proposals Mitigation of Seismic Risk at Nuclear Facilities using Seismic Isolation

Seismic isolation (SI) has the potential to drastically reduce seismic response of structures, systems, or components (SSCs) and therefore the risk associated with large seismic events (large seismic event could be defined as the design basis earthquake (DBE) and/or the beyond design basis earthquake (BDBE) depending on the site location). This would correspond to a potential increase in nuclear safety by minimizing the structural response and thus minimizing the risk of material release during large seismic events that have uncertainty associated with their magnitude and frequency. The national consensus standard America Society of Civil Engineers (ASCE) Standard 4, Seismic Analysis of Safety Related Nuclear Structures recently incorporated language and commentary for seismically isolating a large light water reactor or similar large nuclear structure. Some potential benefits of SI are: 1) substantially decoupling the SSC from the earthquake hazard thus decreasing risk of material release during large earthquakes, 2) cost savings for the facility and/or equipment, and 3) applicability to both nuclear (current and next generation) and high hazard non-nuclear facilities. Issue: To date no one has evaluated how the benefit of seismic risk reduction reduces cost to construct a nuclear facility. Objective: Use seismic probabilistic risk assessment (SPRA) to evaluate the reduction in seismic risk and estimate potential cost savings of seismic isolation of a generic nuclear facility. This project would leverage ongoing Idaho National Laboratory (INL) activities that are developing advanced (SPRA) methods using Nonlinear Soil-Structure Interaction (NLSSI) analysis. Technical Approach: The proposed study is intended to obtain an estimate on the reduction in seismic risk and construction cost that might be achieved by seismically isolating a nuclear facility. The nuclear facility is a representative pressurized water reactor building nuclear power plant (NPP) structure. Figure 1: Project activities The study will consider a representative NPP reinforced concrete reactor building and representative plant safety system. This study will leverage existing research and development (R&D) activities at INL. Figure 1 shows the proposed study steps with the steps in blue representing activities already funded at INL and the steps in purple the activities that would be funded under this proposal. The following results will be documented: 1) Comparison of seismic risk for the non-seismically isolated (non-SI) and seismically isolated (SI) NPP, and 2) an estimate of construction cost savings when implementing SI at the site of the generic NPP.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗