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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-626: Practical Method to Implement Arc Therapy Using Scanned Particle Beams

Purpose: The purpose of this study was to propose a method to implement arc therapy that is compatible with existing particle therapy systems having gantries and pencil-beam scanning capacities. Furthermore, we sought to demonstrate expected benefits of this method for selected clival chordoma patients. Methods: We propose that a desired particle arc treatment plan can be discretized into a finite number of fixed beams and that only one (or a subset) of these beams be delivered in any single treatment fraction; the target should receive uniform dose during each fraction. For 3 clival-chordoma patients, robust-optimized, scanned proton beams were simulated to deliver 78 Gy (RBE) to clinical target volumes (CTVs), using either a single-field plan with a posterior-anterior (PA) beam or a discrete-arc plan with 16 beams that were equally spaced throughout a 360-degree axial arc. Dose-volume metrics were compared with emphasis on the brainstem, since risk of radiation necrosis there can often restrict application of tumoricidal doses for chordomas. Results: The mean volume of brainstem receiving a dose of 60 Gy (RBE) or higher (V60Gy) was 10.3±0.9 cm{sup 3} for the single-field plan and 4.7±1.8 cm{sup 3} for the discrete-arc plan, a reduction of 55% in favor of arcs. The mean dose to the brainstem was also reduced using arcs, by 18%, while the maximum dose was nearly identical for both methods. For the whole brain, V60Gy was reduced by 23%, in favor of arcs. Mean dose to the CTVs were nearly identical for both strategies, within 0.3%. Conclusion: Discrete arc treatments can be implemented using existing scanned particle-beam facilities. Aside from the physical advantages, the biological uncertainties of particle therapy, particularly high in the distal edge, can be reduced by arc therapy via rotational smearing, which may be of benefit for tumors near the brainstem.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

TU-CD-BRD-03: UCSD Experience, with Focus On Implementing Change

It has long been standard practice in radiation oncology to report internally when a patient’s treatment has not gone as planned and to report events to regulatory agencies when legally required. Most potential errors are caught early and never affect the patient. Quality assurance steps routinely prevent errors from reaching the patient, and these “near misses” are much more frequent than treatment errors. A growing number of radiation oncology facilities have implemented incident learning systems to report and analyze both errors and near misses. Using the term “incident learning” instead of “event reporting” emphasizes the need to use these experiences to change the practice and make future errors less likely and promote an educational, non-punitive environment. There are challenges in making such a system practical and effective. Speakers from institutions of different sizes and practice environments will share their experiences on how to make such a system work and what benefits their clinics have accrued. Questions that will be addressed include: How to create a system that is easy for front line staff to access How to motivate staff to report How to promote the system as positive and educational and not punitive or demeaning How to organize the team for reviewing and responding to reports How to prioritize which reports to discuss in depth How not to dismiss the rest How to identify underlying causes How to design corrective actions and implement change How to develop useful statistics and analysis tools How to coordinate a departmental system with a larger risk management system How to do this without a dedicated quality manager Some speakers’ experience is with in-house systems and some will share experience with the AAPM/ASTRO national Radiation Oncology Incident Learning System (RO-ILS). Reports intended to be of value nationally need to be comprehensible to outsiders; examples of useful reports will be shown. There will be ample time set aside for audience members to contribute to the discussion. Learning Objectives: Learn how to promote the use of an incident learning system in a clinic. Learn how to convert “event reporting” into “incident learning”. See examples of practice changes that have come out of learning systems. Learn how the RO-ILS system can be used as a primary internal learning system. Learn how to create succinct, meaningful reports useful to outside readers. Gary Ezzell chairs the AAPM committee overseeing RO-ILS and has received an honorarium from ASTRO for working on the committee reviewing RO-ILS reports. Derek Brown is a director of http://TreatSafely.org . Brett Miller has previously received travel expenses and an honorarium from Varian. Phillip Beron has nothing to report.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-207-01: Background and Clinical Implementation of a Screening Program

In the United States, Lung Cancer is responsible for more cancer deaths than the next four cancers combined. In addition, the 5 year survival rate for lung cancer patients has not improved over the past 40 to 50 years. To combat this deadly disease, in 2002 the National Cancer Institute launched a very large Randomized Control Trial called the National Lung Screening Trial (NLST). This trial would randomize subjects who had substantial risk of lung cancer (due to age and smoking history) into either a Chest X-ray arm or a low dose CT arm. In November 2010, the National Cancer Institute announced that the NLST had demonstrated 20% fewer lung cancer deaths among those who were screened with low-dose CT than with chest X-ray. In December 2013, the US Preventive Services Task Force recommended the use of Lung Cancer Screening using low dose CT and a little over a year later (Feb. 2015), CMS announced that Medicare would also cover Lung Cancer Screening using low dose CT. Thus private and public insurers are required to provide Lung Cancer Screening programs using CT to the appropriate population(s). The purpose of this Symposium is to inform medical physicists and prepare them to support the implementation of Lung Screening programs. This Symposium will focus on the clinical aspects of lung cancer screening, requirements of a screening registry for systematically capturing and tracking screening patients and results (such as required Medicare data elements) as well as the role of the medical physicist in screening programs, including the development of low dose CT screening protocols. Learning Objectives: To understand the clinical basis and clinical components of a lung cancer screening program, including eligibility criteria and other requirements. To understand the data collection requirements, workflow, and informatics infrastructure needed to support the tracking and reporting components of a screening program. To understand the role of the medical physicist in implementing Lung Cancer Screening protocols for CT, including utilizing resources such as the AAPM Protocols and the ACR Designated Lung Screening Center program. UCLA Department of Radiology has an Institutional research agreement with Siemens Healthcare; Dr. McNitt-Gray has been a recipient of Research Support from Siemens Healthcare in the past. Dr. Aberle has been a Member of Advisory Boards for the LUNGevity Foundation (2011-present) and Siemens Medical Solutions. (2013)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-E-15: Initial Experience Implementing a Case Method Teaching Approach to Radiation Oncology Physics Residents, Graduate Students and Doctorate of Medical Physics Students

Purpose: Case Method Teaching approach is a teaching tool used commonly in business school to challenge students with real-world situations—i.e. cases. The students are placed in the role of the decision maker and have to provide a solution based on the multitude of information provided. Specifically, students must develop an ability to quickly make sense of a complex problem, provide a solution incorporating all of the objectives (at time conflicting) and constraints, and communicate that solution in a succinct, professional and effective manner. The validity of the solution is highly dependent on the auxiliary information provided in the case and the basic didactic knowledge of the student. A Case Method Teaching approach was developed and implemented into an on-going course focused on AAPM Task Group reports at UTHSCSA. Methods: A current course at UTHSCSA reviews and discusses 15 AAPM Task Group reports per semester. The course is structured into three topic modules: Imaging QA, Stereotactic Radiotherapy, and Special Patient Measurements—i.e. pacemakers, fetal dose. After a topic module is complete, the students are divided into groups (2–3 people) and are asked to review a case study related to the module topic. Students then provide a solution presented in an executive summary and class presentation. Results: Case studies were created to address each module topic. Through team work and whole-class discussion, a collaborative learning environment was established. Students additionally learned concepts such vendor relations, financial negotiations, capital project management, and competitive strategy. Conclusion: Case Method Teaching approach is an effective teaching tool to further enhance the learning experience of radiation oncology physics students by presenting them with though-provoking dilemmas that require students to distinguish pertinent from peripheral information, formulate strategies and recommendations for action, and confront obstacles to implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-P-37: Implementation of An End-To-End QA Test of the Radiation Therapy Imaging, Planning and Delivery Process to Identify and Correct Possible Sources of Deviation

Purpose: To implement an end-to-end QA test of the radiation therapy imaging, planning and delivery process, aimed to assess the dosimetric agreement accuracy between planned and delivered treatment, in order to identify and correct possible sources of deviation. To establish an internal standard for machine commissioning acceptance. Methods: A test involving all steps of the radiation therapy: imaging, planning and delivery process was designed. The test includes analysis of point dose and planar dose distributions agreement between TPS calculated and measured dose. An ad hoc 16 cm diameter PMMA phantom was constructed with one central and four peripheral bores that can accommodate calibrated electron density inserts. Using Varian Eclipse 10.0 and Elekta XiO 4.50 planning systems, IMRT, RapidArc and 3DCRT with hard and dynamic wedges plans were planned on the phantom and tested. An Exradin A1SL chamber is used with a Keithley 35617EBS electrometer for point dose measurements in the phantom. 2D dose distributions were acquired using MapCheck and Varian aS1000 EPID.Gamma analysis was performed for evaluation of 2D dose distribution agreement using MapCheck software and Varian Portal Dosimetry Application.Varian high energy Clinacs Trilogy, 2100C/CD, 2000CR and low energy 6X/EX where tested.TPS-CT# vs. electron density table were checked for CT-scanners used. Results: Calculated point doses were accurate to 0.127% SD: 0.93%, 0.507% SD: 0.82%, 0.246% SD: 1.39% and 0.012% SD: 0.01% for LoX-3DCRT, HiX-3DCRT, IMRT and RapidArc plans respectively. Planar doses pass gamma 3% 3mm in all cases and 2% 2mm for VMAT plans. Conclusion: Implementation of a simple and reliable quality assurance tool was accomplished. The end-to-end proved efficient, showing excellent agreement between planned and delivered dose evidencing strong consistency of the whole process from imaging through planning to delivery. This test can be used as a first step in beam model acceptance for clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Robust and Efficient Implementation of LOBPCG

Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is widely used to compute eigenvalues of large sparse symmetric matrices. The algorithm can suffer from numerical instability if it is not implemented with care. This is especially problematic when the number of eigenpairs to be computed is relatively large. In this paper we propose an improved basis selection strategy based on earlier work by Hetmaniuk and Lehoucq as well as a robust convergence criterion which is backward stable to enhance the robustness. Here, we also suggest several algorithmic optimizations that improve performance of practical LOBPCG implementations. Numerical examples confirm that our approach consistently and significantly outperforms previous competing approaches in both stability and speed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Design and Implementation of “Many Parallel Task” Hybrid Subsurface Model

Continuum scale models have been used to study subsurface flow, transport, and reactions for many years. Recently, pore scale models, which operate at scales of individual soil grains, have been developed to more accurately model pore scale phenomena, such as precipitation, that may not be well represented at the continuum scale. However, particle-based models become prohibitively expensive for modeling realistic domains. Instead, we are developing a hybrid model that simulates the full domain at continuum scale and applies the pore model only to areas of high reactivity. The hybrid model uses a dimension reduction approach to formulate the mathematical exchange of information across scales. Since the location, size, and number of pore regions in the model varies, an adaptive Pore Generator is being implemented to define pore regions at each iteration. A fourth code will provide data transformation from the pore scale back to the continuum scale. These components are coupled into a single hybrid model using the SWIFT workflow system. Our hybrid model workflow simulates a kinetic controlled mixing reaction in which multiple pore-scale simulations occur for every continuum scale timestep. Each pore-scale simulation is itself parallel, thus exhibiting multi-level parallelism. Our workflow manages these multiple parallel tasks simultaneously, with the number of tasks changing across iterations. It also supports dynamic allocation of job resources and visualization processing at each iteration. We discuss the design, implementation and challenges associated with building a scalable, Many Parallel Task, hybrid model to run efficiently on thousands to tens of thousands of processors.

workflow, Many task, Subsurface↗
DOE OSTI2023

Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor

Neuromorphic processors have garnered considerable interest in recent years for their potential in enabling energy-efficient and high-speed computing. The Locally Competative Algorithm (LCA) has been utilized for power efficient sparse coding on neuromophic processors, including the first Loihi processor \cite{appletospikes, loihi1}. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible \cite{loihi2}. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is faster and orders of magnitude more efficient, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent,autonomous, real-time processing on small robots, satellite where there are strict SWaP (small, lightweighr, and low-power) requirement. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.

Parpart, Gavin G.↗
DOE OSTI2024

Designing, Constructing, and Operating an IPv6 Network at SC23: A case study in implementing the IPv6 protocol on a heterogenous network that supports the SC23 conference

IPv6 is the current version of IP, the protocol that is used to route traffic across internet connections. This standard was originally developed as a new approach to mitigate concerns about address exhaustion and allow for near infinite scalability. While this protocol has gained significant support in mobile and broadband networks, as well as being the default for networks in emerging economies, it has yet to be fully adopted as a standard deployment model. Complications include legacy devices unable to support the proposed changes, as well as potential challenges that exist between devices that may not be able to fully implement current standards or configuration norms. The SCinet volunteers who deliver advanced networking to support the SC Conference set an ambitious goal of deploying an IPv6-only network at SC23. While the necessary technology is widely available and understood, the implications of deployment to support more than 15,000 users, each with multiple devices of different operating environments and ages, presents a unique technology and policy challenge. This paper will highlight the effort put into designing, implementing, and operating this innovative IPv6-only environment.

Robinson, Kate↗
DOE OSTI2024

Experiences with implementing Kokkos’ SYCL backend

With the recent diversification of the hardware landscape in the high-performance computing community, performance-portability solutions are becoming more and more important. One of the most popular choices is Kokkos. In this paper, we describe how Kokkos maps to SYCL 2020, how SYCL had to evolve to enable a full Kokkos implementation, and where we still rely on extensions provided by Intel’s oneAPI implementation. Furthermore, we describe how applications can use Kokkos and its ecosystem to already explore upcoming C++ features also when using the SYCL backend. Finally, we are providing some performance benchmarks comparing native SYCL and Kokkos and also discuss hierarchical parallelism in the SYCL 2020 interface.

Arndt, Daniel↗
DOE OSTI2021

Implementation of higher-order velocity mapping between marker particles and grid in the particle-in-cell code XGC

The global total-f gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas or to couple with a core gyrokinetic code while functioning as an edge gyrokinetic code, implements a 5-dimensional (5D) continuum grid to perform the dissipative operations, such as plasma collisions, or to exchange the particle distribution function information with a core code. To transfer the distribution function between marker particles and a rectangular 2D velocity-space grid, XGC employs a bilinear mapping. The conservation of particle density and momentum is accurate enough in this bilinear operation, but the error in the particle energy conservation can become undesirably large and cause non-negligible numerical heating in a steep edge pedestal. In the present work we update XGC to use a novel mapping technique, based on the calculation of a pseudo-inverse, to exactly preserve moments up to the order of the discretization space. We describe the details of the implementation and we demonstrate the reduced interpolation error for a tokamak test plasma by using 1st- and 2nd-order elements with the pseudo-inverse method and comparing to the bilinear mapping.

Fusion Plasma↗
DOE OSTI2019

Python GST Implementation (PyGSTi) v. 0.9

PyGSTi is a Python package for modeling and characterizing noise (errors) in small quantum information processors. In addition to being a basic framework for describing quantum circuits and noise models, it implements mainstream quantum characterization, verification, and validation (QCVV) protocols such as Gate Set Tomography (GST), Randomized Benchmarking (RB), Robust Phase Estimation, and Idle Tomography. It also implements prototype protocols used for timeseries analysis and crosstalk detection, all of which have the goal of better understanding the noise found in existing as-built experimental devices. The central protocol of pyGSTi (from where it derives its name) is Gate Set Tomography. GST is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. In this way, GST provides not only a means of certifying the “goodness” of qubits but also a means of debugging (i.e. improving) them. The other protocols follow this similar pattern in that they use statistical inference and analysis of experimental data to estimate one or more properties of the noise in a device.

Nielsen, Erik↗
DOE OSTI2020

kokkosSZ (kSZ) A Portable Cellerator Implementation of SZ using Kokkos Programming Model

kSZ is a Kokkos-based implementation of the world-widely used SZ lossy compressor. We use Kokkos because it provides abstractions for both parallel execution of code and data management, which can be used to support portable implementation across different accelerator technologies. Kokkos can support OpenMP/OpenMPTarget, oneAPI, Pthreads, and CUDA as backend programming models.

ECP↗
DOE OSTI2019

XPCA - R Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. 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. SAND2019-10591 M

Kincher-Winoto, Kina↗
DOE OSTI2019

XPCA -Python Implementation

Principal component analysis (PCA) is arguably the most popular tool in multivariate exploratory data analysis. We consider the question of how to handle heterogeneous variables that include continuous, binary, and ordinal. In the probabilistic interpretation of low-rank PCA, the data has a normal multivariate distribution and, therefore, normal marginal distributions for each column. If some marginals are continuous but not normal, the semiparametric copula-based principal component analysis (COCA) method is an alternative to PCA that combines a Gaussian copula with nonparametric marginals. If some marginals are discrete or semi-continuous, we present a new extended PCA (XPCA) method that also uses a Gaussian copula and nonparametric marginals and accounts for discrete variables in the likelihood calculation by integrating over appropriate intervals. Like PCA, the factors produced by XPCA can be used to find latent structure in data, build predictive models, and perform dimensionality reduction. We implement the new model, XPCA, and its ancestors PCA and COCA in both R and Python. This software provides implementations of XPCA, COCA, and probabilistic PCA in both R and Python. XPCA is written in arXiv report https://arxiv.org/abs/1808.07510. SAND2019-10591 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.

Kincher-Winoto, Kina↗
DOE OSTI2024

PyTorch Implementation of Log-Additive Convolutional Neural Networks

This code is a collection of python code that defines, trains, and tests Log-Additive Convolutional Neural Networks. The model components and training routine are based on the PyTorch python library. The code implements the Log-Additive Convolutional Neural Networks as described in Pagendam et al. 2023. In addition to the Log-Additive Convolutional Neural Networks, this library also defines the Log-Normal Density loss function as described in Pagendam et al. 2023. Code from this paper is not publicly available, so the Pytorch implementation of this type of model is unique to this library.

Callis, Skylar↗
DOE OSTI2013

Implementation and Initial Evaluation of the Glimmer Community Ice Sheet Model in the Community Earth System Model

The Glimmer Community Ice Sheet Model (Glimmer-CISM) has been implemented in the Community Earth System Model (CESM). Glimmer-CISM is forced by a surface mass balance (SMB) computed in multiple elevation classes in the CESM land model and downscaled to the ice sheet grid. Ice sheet evolution is governed by the shallow-ice approximation with thermomechanical coupling and basal sliding. This paper describes and evaluates the initial model implementation for the Greenland Ice Sheet (GIS). The ice sheet model was spun up using the SMB from a coupled CESM simulation with preindustrial forcing. The model's sensitivity to three key ice sheet parameters was explored by running an ensemble of 100 GIS simulations to quasi equilibrium and ranking each simulation based on multiple diagnostics. With reasonable parameter choices, the steady-state GIS geometry is broadly consistent with observations. The simulated ice sheet is too thick and extensive, however, in some marginal regions where the SMB is anomalously positive. The top-ranking simulations were continued using surface forcing from CESM simulations of the twentieth century (1850–2005) and twenty-first century (2005–2100, with RCP8.5 forcing). In these simulations the GIS loses mass, with a resulting global-mean sea level rise of 16 mm during 1850–2005 and 60 mm during 2005–2100. This mass loss is caused mainly by increased ablation near the ice sheet margin, offset by reduced ice discharge to the ocean. Projected sea level rise is sensitive to the initial geometry, showing the importance of realistic geometry in the spun-up ice sheet.

54 ENVIRONMENTAL SCIENCES↗