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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

MO-AB-204-00: Interoperability in Radiation Oncology: IHE-RO Committee Update

You’ve experienced the frustration: vendor A’s device claims to work with vendor B’s device, but the practice doesn’t match the promise. Getting devices working together is the hidden art that Radiology and Radiation Oncology staff have to master. To assist with that difficult process, the Integrating the Healthcare Enterprise (IHE) effort was established in 1998, with the coordination of the Radiological Society of North America. Integrating the Healthcare Enterprise (IHE) is a consortium of healthcare professionals and industry partners focused on improving the way computer systems interconnect and exchange information. This is done by coordinating the use of published standards like DICOM and HL7. Several clinical and operational IHE domains exist in the healthcare arena, including Radiology and Radiation Oncology. The ASTRO-sponsored IHE Radiation Oncology (IHE-RO) domain focuses on radiation oncology specific information exchange. This session will explore the IHE Radiology and IHE RO process for; IHE solicitation process for new profiles. Improving the way computer systems interconnect and exchange information in the healthcare enterprise Supporting interconnectivity descriptions and proof of adherence by vendors Testing and assuring the vendor solutions to connectivity problems. Including IHE profiles in RFPs for future software and hardware purchases. Learning Objectives: Understand IHE role in improving interoperability in health care. Understand process of profile development and implantation. Understand how vendors prove adherence to IHE RO profiles. S. Hadley, ASTRO Supported Activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-AB-204-01: IHE RO Overview [Health Care]

You’ve experienced the frustration: vendor A’s device claims to work with vendor B’s device, but the practice doesn’t match the promise. Getting devices working together is the hidden art that Radiology and Radiation Oncology staff have to master. To assist with that difficult process, the Integrating the Healthcare Enterprise (IHE) effort was established in 1998, with the coordination of the Radiological Society of North America. Integrating the Healthcare Enterprise (IHE) is a consortium of healthcare professionals and industry partners focused on improving the way computer systems interconnect and exchange information. This is done by coordinating the use of published standards like DICOM and HL7. Several clinical and operational IHE domains exist in the healthcare arena, including Radiology and Radiation Oncology. The ASTRO-sponsored IHE Radiation Oncology (IHE-RO) domain focuses on radiation oncology specific information exchange. This session will explore the IHE Radiology and IHE RO process for; IHE solicitation process for new profiles. Improving the way computer systems interconnect and exchange information in the healthcare enterprise Supporting interconnectivity descriptions and proof of adherence by vendors Testing and assuring the vendor solutions to connectivity problems. Including IHE profiles in RFPs for future software and hardware purchases. Learning Objectives: Understand IHE role in improving interoperability in health care. Understand process of profile development and implantation. Understand how vendors prove adherence to IHE RO profiles. S. Hadley, ASTRO Supported Activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-AB-204-02: IHE RAD [Health care]

You’ve experienced the frustration: vendor A’s device claims to work with vendor B’s device, but the practice doesn’t match the promise. Getting devices working together is the hidden art that Radiology and Radiation Oncology staff have to master. To assist with that difficult process, the Integrating the Healthcare Enterprise (IHE) effort was established in 1998, with the coordination of the Radiological Society of North America. Integrating the Healthcare Enterprise (IHE) is a consortium of healthcare professionals and industry partners focused on improving the way computer systems interconnect and exchange information. This is done by coordinating the use of published standards like DICOM and HL7. Several clinical and operational IHE domains exist in the healthcare arena, including Radiology and Radiation Oncology. The ASTRO-sponsored IHE Radiation Oncology (IHE-RO) domain focuses on radiation oncology specific information exchange. This session will explore the IHE Radiology and IHE RO process for; IHE solicitation process for new profiles. Improving the way computer systems interconnect and exchange information in the healthcare enterprise Supporting interconnectivity descriptions and proof of adherence by vendors Testing and assuring the vendor solutions to connectivity problems. Including IHE profiles in RFPs for future software and hardware purchases. Learning Objectives: Understand IHE role in improving interoperability in health care. Understand process of profile development and implantation. Understand how vendors prove adherence to IHE RO profiles. S. Hadley, ASTRO Supported Activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-AB-204-03: Profile Development and IHE Process

You’ve experienced the frustration: vendor A’s device claims to work with vendor B’s device, but the practice doesn’t match the promise. Getting devices working together is the hidden art that Radiology and Radiation Oncology staff have to master. To assist with that difficult process, the Integrating the Healthcare Enterprise (IHE) effort was established in 1998, with the coordination of the Radiological Society of North America. Integrating the Healthcare Enterprise (IHE) is a consortium of healthcare professionals and industry partners focused on improving the way computer systems interconnect and exchange information. This is done by coordinating the use of published standards like DICOM and HL7. Several clinical and operational IHE domains exist in the healthcare arena, including Radiology and Radiation Oncology. The ASTRO-sponsored IHE Radiation Oncology (IHE-RO) domain focuses on radiation oncology specific information exchange. This session will explore the IHE Radiology and IHE RO process for; IHE solicitation process for new profiles. Improving the way computer systems interconnect and exchange information in the healthcare enterprise Supporting interconnectivity descriptions and proof of adherence by vendors Testing and assuring the vendor solutions to connectivity problems. Including IHE profiles in RFPs for future software and hardware purchases. Learning Objectives: Understand IHE role in improving interoperability in health care. Understand process of profile development and implantation. Understand how vendors prove adherence to IHE RO profiles. S. Hadley, ASTRO Supported Activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-AB-204-04: Connectathons and Testing

You’ve experienced the frustration: vendor A’s device claims to work with vendor B’s device, but the practice doesn’t match the promise. Getting devices working together is the hidden art that Radiology and Radiation Oncology staff have to master. To assist with that difficult process, the Integrating the Healthcare Enterprise (IHE) effort was established in 1998, with the coordination of the Radiological Society of North America. Integrating the Healthcare Enterprise (IHE) is a consortium of healthcare professionals and industry partners focused on improving the way computer systems interconnect and exchange information. This is done by coordinating the use of published standards like DICOM and HL7. Several clinical and operational IHE domains exist in the healthcare arena, including Radiology and Radiation Oncology. The ASTRO-sponsored IHE Radiation Oncology (IHE-RO) domain focuses on radiation oncology specific information exchange. This session will explore the IHE Radiology and IHE RO process for; IHE solicitation process for new profiles. Improving the way computer systems interconnect and exchange information in the healthcare enterprise Supporting interconnectivity descriptions and proof of adherence by vendors Testing and assuring the vendor solutions to connectivity problems. Including IHE profiles in RFPs for future software and hardware purchases. Learning Objectives: Understand IHE role in improving interoperability in health care. Understand process of profile development and implantation. Understand how vendors prove adherence to IHE RO profiles. S. Hadley, ASTRO Supported Activity.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Facilities Asset Mobile Environment (FAME): From Old School to World Class - 18224

Newport News Shipbuilding (NNS) is on a mission to achieve and sustain world-class performance and maintenance excellence for Facilities Support. Our Computerized Maintenance Management System (CMMS) installation was 17 years old, and legacy data structures were inadequate to support collection of accurate reliability data. The Facilities Asset Mobile Environment (FAME) project includes three major segments - Mobile Hardware, Information, and Process Upgrades supported by the CMMS. By implementing the FAME project, NNS is making significant strides in improving Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR). (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗
DOE OSTI2018

Steady-State Calibration of a Diesel Engine in Computational Fluid Dynamics Using a Graphical Processing Unit-Based Chemistry Solver

The prospect of analysis-driven precalibration of a modern diesel engine is extremely valuable in order to significantly reduce hardware investments and accelerate engine designs compliant with stricter fuel economy regulations. Advanced modeling tools, such as CFD, are often used with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. However, the effectiveness of CFD simulation tools for in-cylinder engine combustion is often compromised by the complexity, accuracy, and computational overhead of detailed chemical kinetics necessary for combustion calculations. The standard approach has been to use skeletal kinetic mechanisms (~50 species), which consume acceptable computational time but with degraded accuracy. In this work, a comprehensive demonstration and validation of the analytical precalibration process is presented for a passenger car diesel engine using CFD simulations and a graphical processing unit (GPU)-based chemical kinetics solver (Zero-RK, developed at Lawrence Livermore National Laboratory, Livermore, CA) on high performance computing resources to enable the use of detailed kinetic mechanisms. Diesel engine combustion computations have been conducted over 600 operating points spanning in-vehicle speed-load map, using massively parallel ensemble simulation sets on the Titan supercomputer located at the Oak Ridge Leadership Computing Facility. The results with different mesh resolutions have been analyzed to compare differences in combustion and emissions (NOx, carbon monoxide CO, unburned hydrocarbons (UHC), and smoke) with actual engine measurements. The results show improved agreement in combustion and NOx predictions with a large n-heptane mechanism consisting of 144 species and 900 reactions with refined mesh resolution; however, agreement in CO, UHC, and smoke remains a challenge.

42 ENGINEERING↗
DOE OSTI2024

OpenCHAMI Developer Summit [Slides]

The mission of the OpenCHAMI consortium is to steward the collaborative development and continuous evolution of cloud-like software to manage High Performance Computing capacity regardless of the size or deployment platform. We are guided by the operators and practitioners who use modern tooling and concepts to address the needs of classical HPC applications and the growing AI/ML and Data Science community that wish to leverage HPC capacity within their own workflows, to meet their needs with their own tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Build and Execute Environment

At exascale, the challenge becomes to develop applications that run at scale and use exascale platforms reliably, efficiently, and flexibly. Workflows become much more complex because they must seamlessly integrate simulation and data analytics. They must include down-sampling, post-processing, feature extraction, and visualization. Power and data transfer limitations require these analysis tasks to be run in-situ or in-transit. We expect successful workflows will comprise multiple linked simulations along with tens of analysis routines. Users will have limited development time at scale and, therefore, must have rich tools to develop, debug, test, and deploy applications. At this scale, successful workflows will compose linked computations from an assortment of reliable, well-defined computation elements, ones that can come and go as required, based on the needs of the workflow over time. We propose a novel framework that utilizes both virtual machines (VMs) and software containers to create a workflow system that establishes a uniform build and execution environment (BEE) beyond the capabilities of current systems. In this environment, applications will run reliably and repeatably across heterogeneous hardware and software. Containers, both commercial (Docker and Rocket) and open-source (LXC and LXD), define a runtime that isolates all software dependencies from the machine operating system. Workflows may contain multiple containers that run different operating systems, different software, and even different versions of the same software. We will run containers in open-source virtual machines (KVM) and emulators (QEMU) so that workflows run on any machine entirely in user-space. On this platform of containers and virtual machines, we will deliver workflow software that provides services, including repeatable execution, provenance, checkpointing, and future proofing. We will capture provenance about how containers were launched and how they interact to annotate workflows for repeatable and partial re-execution. We will coordinate the physical snapshots of virtual machines with parallel programming constructs, such as barriers, to automate checkpoint and restart. We will also integrate with HPC-specific container runtimes to gain access to accelerators and other specialized hardware to preserve native performance. Containers will link development to continuous integration. When application developers check code in, it will automatically be tested on a suite of different software and hardware architectures.

Guan, Qiang↗
DOE OSTI2020

JaqalPaq v.1.1

Jaqal-pup (Jaqal Programming Utilities Project) is a high-level Python metaprogramming interface for Jaqal, a quantum programming language for near-term quantum hardware (see https://arxiv.org/abs/2003.09382 for a description of Jaqal). In addition to parsing and writing out Jaqal programs, it includes the ability to read in and write out quantum programs in other languages and frameworks, e.g. Qiskit (IBM), Quil (Rigetti), Cirq (Google), Q# (Microsoft), ProjectQ (ETH Zurich), and tket (Cambridge Quantum Computing). It implements a gate scheduler, an emulator of a small-scale quantum computer programmed using Jaqal, and tie-ins for other simulation back-ends. SAND2020-12710 M 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.

Russo, Antonio↗
DOE OSTI2017

Build and Execute Environment

At exascale, the challenge becomes to develop applications that run at scale and use exascale platforms reliably, efficiently, and flexibly. Workflows become much more complex because they must seamlessly integrate simulation and data analytics. They must include down-sampling, post-processing, feature extraction, and visualization. Power and data transfer limitations require these analysis tasks to be run in-situ or in-transit. We expect successful workflows will comprise multiple linked simulations along with tens of analysis routines. Users will have limited development time at scale and, therefore, must have rich tools to develop, debug, test, and deploy applications. At this scale, successful workflows will compose linked computations from an assortment of reliable, well-defined computation elements, ones that can come and go as required, based on the needs of the workflow over time. We propose a novel framework that utilizes both virtual machines (VMs) and software containers to create a workflow system that establishes a uniform build and execution environment (BEE) beyond the capabilities of current systems. In this environment, applications will run reliably and repeatably across heterogeneous hardware and software. Containers, both commercial (Docker and Rocket) and open-source (LXC and LXD), define a runtime that isolates all software dependencies from the machine operating system. Workflows may contain multiple containers that run different operating systems, different software, and even different versions of the same software. We will run containers in open-source virtual machines (KVM) and emulators (QEMU) so that workflows run on any machine entirely in user-space. On this platform of containers and virtual machines, we will deliver workflow software that provides services, including repeatable execution, provenance, checkpointing, and future proofing. We will capture provenance about how containers were launched and how they interact to annotate workflows for repeatable and partial re-execution. We will coordinate the physical snapshots of virtual machines with parallel programming constructs, such as barriers, to automate checkpoint and restart. We will also integrate with HPC-specific container runtimes to gain access to accelerators and other specialized hardware to preserve native performance. Containers will link development to continuous integration. When application developers check code in, it will automatically be tested on a suite of different software and hardware architectures.

Guan, Qiang↗
DOE OSTI2026

Exact and Fixed-Point Grover Search with Qudits

Grover's algorithm provides a quadratic speedup for searching unstructured databases and is traditionally implemented with qubits in Hilbert spaces whose dimensions are powers of two. With the advent of quantum platforms utilizing qudits---quantum systems with more than two levels---there is a need to generalize Grover search to these architectures, including heterogeneous systems with qudits of varying dimensions. Here, we present a unified framework for qudit-based Grover search, detailing the construction of oracles and diffusion operators with and without ancilla qubits and generalizing deterministic and fixed-point search variants that ensure exact or bounded success probabilities. We analyze phase-matching techniques and provide explicit circuit decompositions suitable for diverse hardware platforms. We also compare the corresponding trajectories on the Bloch sphere to provide an intuitive visualization of how the different phase choices amplify the target state. These results facilitate flexible, hardware-oriented protocols for implementing Grover search on qudit processors, potentially reducing circuit depth and enhancing success probabilities, thereby offering a practical toolkit for quantum computation and sensing applications leveraging multilevel quantum systems.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗
DOE OSTI2024

Quantum tensor network algorithms for evaluation of spectral functions on quantum computers

We investigate quantum algorithms derived from tensor networks to simulate the static and dynamic properties of quantum many-body systems. Using a sequentially prepared quantum circuit representation of a matrix product state (MPS) that we call a quantum tensor network (QTN), we demonstrate algorithms to prepare ground and excited states on a quantum computer and apply them to molecular nanomagnets (MNMs) as a paradigmatic example. In this setting, we develop two approaches for extracting the spectral correlation functions measured in neutron-scattering experiments: (a) a generalization of the SWAP test for computing wave function overlaps and, (b) a generalization of the notion of matrix product operators to the QTN setting which generates a linear combination of unitaries. The latter method is discussed in detail for translationally invariant spin-half systems, where it is shown to reduce the qubit resource requirements compared with the SWAP method and may be generalized to other systems. We demonstrate the versatility of our approaches by simulating spin-1/2 and spin-3/2 MNMs, with the latter being an experimentally relevant model of a Cr$^{3+}_{8}$ ring. Here, our approach has qubit requirements that are independent of the number of constituents of the many-body system and scale only logarithmically with the bond dimension of the MPS representation, making them appealing for implementation on near-term quantum hardware with mid-circuit measurement and reset.

Neutron scattering↗
DOE OSTI2015

Collective I/O Tuning Using Analytical and Machine-Learning Models

The ever larger demand of scientific applications for computation and data is currently driving a continuous increase in scale of parallel computers. The inherent complexity of scaling up a computing systems in terms of both hardware and software stack exposes an increasing number of factors impacting the performance and complicating the process of optimization. In particular, the optimization of parallel I/O has become increasingly challenging due to increasing storage hierarchy and well known performance variability of shared storage systems. This paper focuses on model-based autotuning of the two-phase collective I/O algorithm from a popular MPI distribution on the Blue Gene/Q architecture. We propose a novel hybrid model, constructed as a composition of analytical models for communication and storage operations and black-box models for the performance of the individual operations. We perform an in-depth study of the complexity involved in performance modeling including architecture, software stack and noise. In particular we address this challenges of modeling the performance of shared storage systems by building a benchmark that helps synthesizing factors such as topology, file caching, and noise. The experimental results show that the hybrid approach produces significantly better results than state-of-the-art machine learning approaches and shows a higher robustness to noise, at the cost of a higher modeling complexity

I/O performance modeling↗
DOE OSTI2025

A Robust Communication-Free Protection Scheme for Islanded Microgrids with Relay Logic and Hardware-in-the-Loop Validation

This paper presents the Imbalance Square Factor (ISF) detection algorithm, an effective, computationally lightweight method for detecting faults in inverter-based microgrids. ISF provides a high magnitude at the time of the fault, which allows for fast detection and coordination between primary and backup relays. The imbalance squared factor, calculated using local voltage and currents, is used for fault detection and coordination among three relays. ISF is validated in hardware-in-the-loop (HIL) implementation inside commercial-grade relay logic (SEL-751). The HIL validation shows that ISF can coordinate primary, secondary, and tertiary relays in the 13-bus system in islanded operation.

Ferrari Maglia, Max [ORNL]↗
DOE OSTI2021

LANL Meteorological Program Self-Assessment 2021 Update

This self-assessment, documented in Table 1, has shown that there has been continued significant progress achieved in meeting the intent of the 21 recommendations from the 2015 DMCC Assist Visit. Every year after the Assist Visit, the LANL Met Program has improved, mainly by addressing the recommendations in the Assist Visit report. The Met Program management should continue to strive to apply its limited resources and seek to acquire additional human capital and physical resources to meet the dynamic needs of its growing sitewide customer base. This includes additional upgrading of the hardware and software elements of the Met Program and implementation of its operational procedures and QAPP to ensure it will consistently meet important present program requirements, as well as anticipated future requirements. These improvements have been summarized in the “present improvements and future initiatives” column in Table 1.

47 OTHER INSTRUMENTATION↗