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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Logical Activation Functions v.1.1

SAND2024-01501O Logical Activation Functions software is a PyTorch implementation from the paper, "Logical Activation Functions for Training Arbitrary Probabilistic Boolean Logic." The activation functions approximate logit-space marginalization of probabilistic truth tables from probabilistic interpretations of inputs. They also provide a general methodology to approximate logical relationships between abstract antecedents and consequents for machine learning architectures. They do not target any specific application or use-case. By training probabilistic truth tables, these activation functions can capture more expressive relationships in a neural network than typical elementwise activation functions. This code is only designed for a single compute node with a GPU and is limited to machine learning architectures than can fit within the memory of a single GPU. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Duersch, Jed↗
DOE OSTI2014

Parallel Block Structured Adaptive Mesh Refinement on Graphics Processing Units

Block-structured adaptive mesh refinement is a technique that can be used when solving partial differential equations to reduce the number of zones necessary to achieve the required accuracy in areas of interest. These areas (shock fronts, material interfaces, etc.) are recursively covered with finer mesh patches that are grouped into a hierarchy of refinement levels. Despite the potential for large savings in computational requirements and memory usage without a corresponding reduction in accuracy, AMR adds overhead in managing the mesh hierarchy, adding complex communication and data movement requirements to a simulation. In this paper, we describe the design and implementation of a native GPU-based AMR library, including: the classes used to manage data on a mesh patch, the routines used for transferring data between GPUs on different nodes, and the data-parallel operators developed to coarsen and refine mesh data. We validate the performance and accuracy of our implementation using three test problems and two architectures: an eight-node cluster, and over four thousand nodes of Oak Ridge National Laboratory’s Titan supercomputer. Our GPU-based AMR hydrodynamics code performs up to 4.87× faster than the CPU-based implementation, and has been scaled to over four thousand GPUs using a combination of MPI and CUDA.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

SAR processing on the MPP

The processing of synthetic aperture radar (SAR) signals using the massively parallel processor (MPP) is discussed. The fast Fourier transform convolution procedures employed in the algorithms are described. The MPP architecture comprises an array unit (ARU) which processes arrays of data; an array control unit which controls the operation of the ARU and performs scalar arithmetic; a program and data management unit which controls the flow of data; and a unique staging memory (SM) which buffers and permutes data. The ARU contains a 128 by 128 array of bit-serial processing elements (PE). Two-by-four surarrays of PE's are packaged in a custom VLSI HCMOS chip. The staging memory is a large multidimensional-access memory which buffers and permutes data flowing with the system. Efficient SAR processing is achieved via ARU communication paths and SM data manipulation. Real time processing capability can be realized via a multiple ARU, multiple SM configuration.

Batcher, K. E.↗
DOE OSTI2021

Productive Programming of Distributed Systems with the SHAD C++ Library

High-performance computing (HPC) is often perceived as a matter of making large-scale systems (e.g., clusters) run as fast as possible, regardless the required programming effort. However, the idea of "bringing HPC to the masses" has recently emerged. Inspired by this vision, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Underneath, SHAD manages tasking and data-movements, moving the computation where data resides and taking advantage of asynchrony to tolerate network latency. At the bottom of his stack, SHAD can interface with multiple runtime systems: this not only improves developer’s productivity, by hiding the complexity of such software and of the underlying hardware, but also greatly enhance code portability. Thanks to its abstraction layers, SHAD can indeed target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level code. We have prototyped and open-sourced the implementation of (a subset of) the C++ standard library (STL) targeting multi-node HPC clusters. Our work allows plain STL-based C++ code to scale on HPC systems, with no need for rewriting the code to exploit the complex hardware. SHAD is available under Apache v2 License at https://github.com/pnnl/SHAD. In this paper we overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking; parallel and distributed data-structures; STL-compliant interfaces and algorithms.

Castellana, Vito G.↗
DOE OSTI2023

NA-213 Presentation.pdf

The Savannah River National Laboratory (SRNL) is one of 17 United States Department of Energy (DOE) National Laboratories. SRNL, a multi-program national laboratory, is a leading research and development institution for the Offices of Environmental Management and Legacy Management at the U.S. Department of Energy and the Weapons and Nonproliferation programs for the National Nuclear Security Administration. Battelle Savannah River Alliance, LLC (BSRA), a not-for-profit limited liability company, manages and operates SRNL for the DOE. BSRA board leadership includes Battelle Memorial Institute, Clemson University, University of South Carolina, South Carolina State University, University of Georgia, and Georgia Institute of Technology. Battelle Memorial Institute and the five universities are joined in partnership with preferred subcontractors TechSource and Longenecker & Associates with the singular purpose of maintaining SRNL as a best-in-class national laboratory. Scientists and Engineers at SRNL use leading edge science and technology to advance the Department of Energy’s critical mission outcomes.

Hasty, Timothy C.↗
DOE OSTI2016

Markers and Beyond: Categorizing Human Intrusion Situations to be Addressed in Sub-surface Marking Concepts - 16378

Concepts for the use of markers in the post-closure phase of deep geological repositories (DGRs), are often designed to prevent 'inadvertent' human intrusion (HI), while it is common practice not to protect against deliberate intrusion (i.e. 'advertent' HI). In the literature, inadvertent HI prevention studies usually combine future human activity scenarios with the assumption that all memory of the DGR has been lost, and analyze what can be done now to make the possible future intruder 'aware' of the associated risks. In this paper, we show that the classical 'advertent'/'inadvertent' duality classification of HI, as used in the performance assessment context, is not ideal to characterize potential HI situations in the RK and M preservation context. We suggest that the relevant HI categories are expanded and refined to 'un/necessary HI', 'un/intentional HI' and 'un/friendly HI'. Along with refining the taxonomy of HI that way, we refine the assumption relating to the loss of memory of the DGR. Different HI situations are likely to arise in different 'loss-of-memory situations'. We analyze the resulting 5 classes of the proposed HI classification and find that 2 hold the classic 'advertent' and 'inadvertent' HI scenarios while 3 are 'new'. We apply the proposed categories to sub-surface marking concepts from the literature and suggest that, in particular, sub-surface marking concepts would benefit from this new approach to HI and loss-of-memory categorization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

LabVIEW Task Manager v. 1.10.0

LabVIEW Task Manager is a debugging tool for use during code development in the National Instruments (NI) LabVIEW® IDE. While providing a dynamic & big-picture view of running code, an expandable/collapsible tree diagram displays detailed information (both static and dynamic) on all VIs in memory, belonging to a selected project/target. It allows for interacting with single or multiple selected VIs at a time, providing significant benefits while troubleshooting, and has the following features: Look & Feel similar to Windows® Task Manager; Selection of project/target; Lists all VIs in memory, grouped by class/library; Searches for and enumerates clones in memory; DropIn VI for including dynamically referenced clones (Clone Beacon); 'Refresh Now' (F5) re-reads all VIs in memory and adds new ones to the tree; Displays VI name, owning class/library, state, path, data size & code size; Displays VI FP Behavior, Reentrant?, Reentrancy Type, Paused? & Highlight?; Sort by any column, including by library name; Filter by item types vi, ctl, and vit/ctt; Filter out vi.lib and global VIs; Tracking of, and ability to toggle, execution highlighting on multiple selected VIs; Tracking of paused VIs with ability to Pause/Resume/TogglePause multiple selected VIs; DropIn VI for pausing on a condition; If a clone initiates a pause, a different pause symbol is used for all clones of that same reentrant original VI; Select multiple VIs and open or close their FPs or BDs; Double Click a VI from the tree to bring the BD (first choice) or FP to front, if already open; and Select multiple top-level VIs and Abort them.

Vargo, TimothyD.↗
NASA NTRS2002

Gilgamesh: A Multithreaded Processor-In-Memory Architecture for Petaflops Computing

Processor-in-Memory (PIM) architectures avoid the von Neumann bottleneck in conventional machines by integrating high-density DRAM and CMOS logic on the same chip. Parallel systems based on this new technology are expected to provide higher scalability, adaptability, robustness, fault tolerance and lower power consumption than current MPPs or commodity clusters. In this paper we describe the design of Gilgamesh, a PIM-based massively parallel architecture, and elements of its execution model. Gilgamesh extends existing PIM capabilities by incorporating advanced mechanisms for virtualizing tasks and data and providing adaptive resource management for load balancing and latency tolerance. The Gilgamesh execution model is based on macroservers, a middleware layer which supports object-based runtime management of data and threads allowing explicit and dynamic control of locality and load balancing. The paper concludes with a discussion of related research activities and an outlook to future work.

management locality load balance↗
DOE OSTI2016

Multilevel radiative thermal memory realized by the hysteretic metal-insulator transition of vanadium dioxide

Thermal information processing is attracting much interest as an analog of electronic computing. We experimentally demonstrated a radiative thermal memory utilizing a phase change material. The hysteretic metal-insulator transition of vanadium dioxide (VO{sub 2}) allows us to obtain a multilevel memory. We developed a Preisach model to explain the hysteretic radiative heat transfer between a VO{sub 2} film and a fused quartz substrate. The transient response of our memory predicted by the Preisach model agrees well with the measured response. Our multilevel thermal memory paves the way for thermal information processing as well as contactless thermal management.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pushing the limits of NAND technology scaling with ferroelectrics

Artificial intelligence (AI) continues to drive transformative advancements across various industries. The data-intensive nature of AI training (and inferencing) has resulted in the generation of unprecedented volumes of data with machine-generated content surpassing human-generated data by more than 100-fold in 2025. Efficiently managing this data influx necessitates advanced digital storage technologies. However, traditional NAND flash memory, which is critical for supporting data flows in AI systems—alongside high-bandwidth memory, for AI training—faces fundamental scaling limitations as it approaches the 1000-layer milestone, encompassing more than 40 trillion transistors. This article delves into the potential of hafnia-based ferroelectric materials as a breakthrough solution to these challenges. Recent advancements indicate that the intrinsic limitations of ferroelectric field-effect transistors (FEFETs) can be mitigated through material and device-level engineering. These advancements enable FEFETs to meet the stringent density, reliability, and scalability requirements of future three-dimensional NAND technology. The role of ferroelectrics in addressing NAND scaling challenges and expanding storage capabilities presents a promising avenue for meeting the storage demands of the AI-driven era.

3D NAND↗
DOE OSTI2022

Block-structured, equal-workload, multi-grid-nesting interface for the Boussinesq wave model FUNWAVE-TVD (Total Variation Diminishing)

Abstract. We describe the development of a block-structured, equal-CPU-load (central processing unit), multi-grid-nesting interface for the Boussinesq wave model FUNWAVE-TVD (Fully Nonlinear Boussinesq Wave Model with Total Variation Diminishing Solver). The new model framework does not interfere with the core solver, and thus the core program, FUNWAVE-TVD, is still a standalone model used for a single grid. The nesting interface manages the time sequencing and two-way nesting processes between the parent grid and child grid with grid refinement in a hierarchical manner. Workload balance in the MPI-based (message passing interface) parallelization is handled by an equal-load scheme. A strategy of shared array allocation is applied for data management that allows for a large number of nested grids without creating additional memory allocations. Four model tests are conducted to verify the nesting algorithm with assessments of model accuracy and the robustness in the application in modeling transoceanic tsunamis and coastal effects.

Choi, Young-Kwang↗
NASA NTRS1988

Concurrent Image Processing Executive (CIPE)

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are discussed. The target machine for this software is a JPL/Caltech Mark IIIfp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules; (1) user interface, (2) host-resident executive, (3) hypercube-resident executive, and (4) application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube a data management method which distributes, redistributes, and tracks data set information was implemented.

Lee, Meemong↗
NASA NTRS1990

Practical applications of remote sensing technology

Land managers increasingly are becoming dependent upon remote sensing and automated analysis techniques for information gathering and synthesis. Remote sensing and geographic information system (GIS) techniques provide quick and economical information gathering for large areas. The outputs of remote sensing classification and analysis are most effective when combined with a total natural resources data base within the capabilities of a computerized GIS. Some examples are presented of the successes, as well as the problems, in integrating remote sensing and geographic information systems. The need to exploit remotely sensed data and the potential that geographic information systems offer for managing and analyzing such data continues to grow. New microcomputers with vastly enlarged memory, multi-fold increases in operating speed and storage capacity that was previously available only on mainframe computers are a reality. Improved raster GIS software systems have been developed for these high performance microcomputers. Vector GIS systems previously reserved for mini and mainframe systems are available to operate on these enhanced microcomputers. One of the more exciting areas that is beginning to emerge is the integration of both raster and vector formats on a single computer screen. This technology will allow satellite imagery or digital aerial photography to be presented as a background to a vector display.

Whitmore, Roy A., Jr.↗
NASA NTRS1990

Concurrent Image Processing Executive (CIPE). Volume 1: Design overview

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are described. The target machine for this software is a JPL/Caltech Mark 3fp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules: user interface, host-resident executive, hypercube-resident executive, and application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube, a data management method which distributes, redistributes, and tracks data set information was implemented. The data management also allows data sharing among application programs. The CIPE software architecture provides a flexible environment for scientific analysis of complex remote sensing image data, such as planetary data and imaging spectrometry, utilizing state-of-the-art concurrent computation capabilities.

Lee, Meemong↗
DOE OSTI2012

Oak Ridge National Laboratory Annual Sustainability Report 2010-2011

The mission of Oak Ridge National Laboratory (ORNL) is to deliver scientific discoveries and technical breakthroughs that will accelerate the development and deployment of solutions in clean energy and global security, and in doing so create economic opportunity for the nation. To execute this mission, ORNL integrates and applies distinctive core capabilities that provide it with signature strengths in neutron scattering, advanced materials, high-performance computing, and nuclear science and engineering. The intended outcome is to produce transformational innovations that will enable a 21st century industrial revolution. Managed by UT-Battelle, a partnership of the University of Tennessee (UT) and Battelle Memorial Institute, ORNL was established in 1943 to support the Manhattan Project. From an early focus on chemical technology and reactor development, ORNL’s research and development (R&D) portfolio broadened to include programs supporting DOE missions in scientific discovery and innovation, clean energy, and nuclear security. Today, as DOE’s largest science and energy laboratory, ORNL is engaged in programs and partnerships that leverage major national investments in critical research infrastructure, including the world’s foremost resources for neutron sciences, the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR), and the nation’s most powerful scientific computing complex. ORNL also manages the U.S. ITER project for DOE. Each year, ORNL hosts thousands of facility users and visiting scientists, many of whom perform work at its 10 user facilities, and supports the development of the next generation of scientific and technical talent. ORNL has a staff of about 4,400 and an annual budget of more than $\$$1.6 billion. The Laboratory’s extensive capabilities for scientific discovery and innovation are applied to the delivery of mission outcomes for DOE and other sponsors.

Lapsa, Melissa Voss↗
DOE OSTI2012

Preservation of Records, Knowledge and Memory Across Generations. An emerging Multidisciplinary Work Area and an NEA Project - 12218

Disposal in engineered facilities built in stable, deep geological formations is the reference means for permanently isolating long-lived radioactive waste from the human biosphere. This management method is designed to be intrinsically safe and final, i.e. not dependent on human presence and intervention in order to fulfil its safety goal. There is however no intention to forgo, at any time, knowledge and awareness either of the repository or of the waste that it contains. The preservation of Records, Knowledge and Memory (RK and M) is seen as an integral part of radioactive waste management, supporting lengthy and complex socio-technical processes across pre-operational, operational and post-operational lifetimes. Long-term preservation of RK and M is an emerging multidisciplinary work area in which much learning is expected over the coming years. Novel methods are being sought that are least vulnerable to both natural degradation and to changes in socio-economic conditions. Progress has been made in individual countries, but there is a need to internationalise the thinking, compare approaches, investigate potential solutions and share decisions. This is the task of the NEA RK and M project. A major outcome of the project will be a 'menu-driven document' that will allow people to identify the main elements of a strategic action plan for RK and M preservation. In sum, the preservation of RK and M is a unprecedented task in which technical, scientific and social information is interwoven and needs to be developed and preserved across generations and across specialist boundaries. Important studies have been undertaken in the past decades to explore a variety of approaches to preserving RK and M across different timescales, including archives and markers. The work of the past in this area is useful, but innovative thinking is also needed. Seen from today's perspective, very little work is available on for example the contextualization of data for later use; on the systematic identification of mechanisms for RK and M transfer; on implementing a culture of RK and M-keeping in organisations; and on creating cultural links between the waste disposals and the siting communities. Moreover, international cooperation is recognised as being crucial in providing shared means and meanings for memory transmission over longer timescales. International cooperation has also been identified as a catalyst to ensure that a wide range of approaches and experiences is considered, thus potentially reducing uncertainty related to variations in approach. Overall, multiple approaches, requiring active and less active care, need to be considered from the start of the radioactive waste management programme and refined in the course of time. The RK and M project members want to further investigate and deliver support to fulfil this task. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

Here, we discuss the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Lastly, initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.

42 ENGINEERING↗
NASA NTRS2018

Climatespark: an In-Memory Distributed Computing Framework for Big Climate Data Analytics

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple- dimensional, array-based datasets in various geoscience domains.

Hu, Fei↗