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Modern scientific software is daunting in its breadth and complexity. From massively parallel simulations running on the world’s largest supercomputers to visualizations and user support environments that manage ever-growing, complex data collections, the challenges for software engineers are plentiful indeed.
Purpose: To develop a system that analyses and reports the complexity of Volumetric Modulated Arc Therapy (VMAT) plans to aid in the decision making for streamlining patient specific dosimetric quality assurance (QA) tests. Methods: A software system, Delcheck, was developed in-house to calculate VMAT plan and delivery complexity using the treatment delivery file. Delcheck has the functionality to calculate multiple plan complexity metrics including the Li-Xing Modulation Index (LI-MI), multiplicative combination of Leaf Travel and Modulation Complexity Score (LTMCSv), Monitor Units per prescribed dose (MU/D) and the delivery complexity index (MIt) that incorporates the modulation of dose rate, leaf speed and gantry speed. Delcheck includes database functionality to store and compare plan metrics for a specified treatment site. The overall plan and delivery complexity is assessed based on the 95% conformance limit of the complexity metrics as Similar, More or Less complex. The functionality of the software was tested using 42 prostate conventional, 10 prostate SBRT and 15 prostate bed VMAT plans generated for an Elekta linear accelerator. Results: The mean(σ) of LI-MI for conventional, SBRT and prostate bed plans were 1690(486), 3215.4(1294) and 3258(982) respectively. The LTMCSv of the studied categories were 0.334(0.05), 0.325(0.07) and 0.3112(0.09). The MU/D of the studied categories were 2.4(0.4), 2.7(0.7) and 2.5(0.5). The MIt of the studied categories were 21.6(3.4), 18.2(3.0) and 35.9(6.6). The values of the complexity metrics show that LI-MI appeared to resolve the plan complexity better than LTMCSv and MU/D. The MIt value increased as the delivery complexity increased. Conclusion: The developed software was shown to be working as expected. In studied treatment categories Prostate bed plans are more complex in both plan and delivery and SBRT is more complex in plan and less complex in delivery as demonstrated by LI-MI and MIt. This project was funded through a Cancer Council NSW Project Grant (RG14-11)
The power grid is changing and evolving. One aspect of this change is the growing use of smart meters and other devices, which are producing large volumes of useful data. However, in many cases, the data can’t be translated quickly into actionable guidance to improve grid performance. There's a need for innovative tools. The GridOPTICS(TM) Software System, or GOSS, developed through PNNL's Future Power Grid Initiative, is open source and became publicly available in spring 2014. The value of this middleware is that it easily integrates grid applications with sources of data and facilitates communication between them. Such a capability provides a foundation for developing a range of applications to improve grid management.
NO{nu}A is an accelerator-based neutrino oscillation experiment which has a great potential to measure the last unknown mixing angle {theta}{sub 13}, the neutrino mass hierarchy, and the CP-violation phase in lepton sector with (1) 700 kW beam, (2) 14 mrad off the beam axis, (3) 810 km long baseline. The Near Detector on the Surface is fully functioning and taking both NuMI and Booster beam data. The far detector building achieved beneficial occupancy on April 13. This proceeding will focus on the DAQ software system.
Quantum computing promises new opportunities for solving hard computational problems, but harnessing this novelty requires breakthrough concepts in the design, operation, and application of computing systems. We define some of the challenges facing the development of quantum computing systems as well as software-based approaches that can be used to overcome these challenges. Following a brief overview of the state of the art, we present models for the quantum programming and execution models, the development of architectures for hybrid high-performance computing systems, and the realization of software stacks for quantum networking. This leads to a discussion of the role that conventional computing plays in the quantum paradigm and how some of the current challenges for exascale computing overlap with those facing quantum computing.
Abstract Not Provided
This Software Management Plan (SMP) applies to the configurable programmable logic controller (PLC) software configured as the Tank Farms Safety Programmable System (TFSPS). The TFSPS is identified as a safety-significant (SS) system to prevent loss of the safety function of SS structures, systems, and components (SSC). The TFSPS software is classified as grade level A configurable safety software. The TFSPS consists of safety instrumented systems (SIS) and safety instrumented alarm (SIA) systems.The purpose of this management plan is to describe how Washington River Protection Solutions (WRPS) will configure and manage the TFSPS software and the associated software life cycle (SLC). TFSPS software shall be configured by Process and Control Systems Engineering (P&CSE) as grade level A safety software for use by Production Operations as the primary stakeholders.
The XPRESS project is the research and development of a complete software architecture for Exascale computing. The four major R&D thrusts include: 1) an Exascale lightweight kernel operating system (LXK), based on the Kitten operating system (OS), to manage billion way hardware parallelism, management of faults and power, management of global virtual name space, and other features of future system architectures; 2) a runtime system (HPX-5), co-designed with LXK, based on the ParalleX execution model that supports dynamic resource management and task scheduling; 3) system interfaces for interoperability between the runtime system and both the OS and the runtime system (RIOS); 4) compilation strategies and systems to translate MPI and OpenMP legacy codes to a form that can be run with performance at least as good as a native code implementation. The software architecture development has been driven by performance evaluation of mini-applications developed at Department of Energy Co-Design Centers as well as ancillary in-house developed applications and benchmarks. Research on the ParalleX execution model has been driven by the development of formal semantics and model checkers for the execution model. The major components of the software architecture are: ParalleX execution model – guiding principles for co-design of components of the system stack with advances in locality management and task prioritization through introspection; HPX runtime system – support of application dynamic adaptive resource management, task scheduling, and introspective control policies; LXK operating system – lightweight kernel operating system for order-constant scalability and low/no noise to manage resources; RIOS – a realm of Exascale system operation unique in the XPRESS project that includes the relationship between the new generation of lightweight kernel operating systems and runtime system software; XPI advanced programming model – intermediate form and low-level (readable) programming interface reflecting the ParalleX model, providing a target for source-to- source high level parallel language translation, and supporting early direct programming experimentation and measurement; Performance models & metrics – providing parameters and their mutual sensitivities to guide co-design and quantify operational behavior; Legacy migration – ensuring seamless transition of legacy codes and programming methods to the future generation of ParalleX-based Exascale systems; . Experiments and evaluation – critical to determining degree of effectiveness and likelihood of ultimate success as well as guiding corrective design changes to achieve DOE objectives; and Applications – collaborations with Co-Design Centers and other mission critical codes. The exemplar implementation of the ParalleX execution model, HPX-5, has been released along with five fully ported applications (LULESH, HPCG, SSSP, WAMR, and DASHMM) and multiple microbenchmarks. Using these applications, the system architecture demonstrated dynamic load balancing through active migration, legacy code migration, and data-driven approaches to for graph-like applications. Crucially for Exascale and billion way parallelism, the needs and patterns of irregular applications in HPX-5 were addressed using local flow control rather than relying on globally synchronized data structures.
The primary goal for the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage vast experience of Pacific Northwest National Laboratory (PNNL) research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation LLC who plans to use it to scale their building energy efficiency offering. The project plan will include collaboratively working with Intellimation to package a set of solutions into the AEMS system, validate and demonstrate their capability, and value proposition through field demonstrations. If the deployment of the AEMS system optimizes RTUs’ set points, schedules, setbacks and optimal start and results in energy consumption reduction of 20%, the technical potential savings is approximately 675 trillion Btus of site energy savings and 2,000 trillion Btus of source energy. It will also result in carbon reductions of approximately 2.4 MMTCO2 and contribute to the climate change mitigation plans of many cities and states across the United States. Additional cost savings and emissions reduction are possible from management of peak electricity demand. The primary outcome will be an AEMS system that can be deployed at scale on small commercial buildings to improve operating efficiency.
The National Spherical Torus Experiment (NSTX) currently uses a collection of analog signal processing solutions for coil protection. Part of the NSTX Upgrade (NSTX-U) entails replacing these analog systems with a software solution running on a conventional computing platform. The new Digital Coil Protection System (DCPS) will replace the old systems entirely, while also providing an extensible framework that allows adding new functionality as desired.
Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems. Many of these buildings are small (<50,000 square feet), underserved, and use rooftop units for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the rooftop units heating and cooling set points, schedules, setbacks, and optimal start can result in 20 to 25 percent reduction in electricity consumption in small commercial buildings. In addition, improving demand flexibility of these buildings will result additional cost savings for the building owner. Therefore, the Department of Energy’s Building Technologies Office approved a project to address the needs for small commercial buildings. The project is led by Pacific Northwest National Laboratory (PNNL) with Intellimation LLC as the cooperative research and development agreement partner. The primary goal of the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage the vast experience of PNNL research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation, a company that plans to use it to scale their building energy efficiency (EE) and grid services offering. Widespread deployment of the AEMS system will improve the EE and demand flexibility of the building commercial building stock. It should also support cities and states in meeting their climate change mitigation goals. This document describes the various EE and grid service features of the AEMS system, infrastructure and data required to implement those features, and how the features should be automated. It also details how the various features will be tested and validated, including field validation. The document also details what flexibility the users have and how they will be able to leverage those capabilities exercise those. The intent is to create an AEMS system that would support scalable deployment, requires minimal configuration, and is easy to maintain over its expected lifespan. The initial alpha release of AEMS system is planned for March 2023, and the beta release is planned for the summer of 2023. The final release is planned for March 2024. Section 2 of the report documents the relevant building types that AEMS is suitable for. Section 3 documents EE features that will be supported. It will also include the data requirements, hardware requirements, implementation details, and how EE features will be tested and validated. Grid service features will be documented in section 4, including data requirements, hardware requirements, implementation details, and how the services will be tested and validated. Planned next steps are described in section 5.
A dataset of two spectral lighting simulation reference models - one office and one factory hall - is presented. It aims to demonstrate and support full-spectral daylight and electric lighting simulations and facilitate evaluation of non-visual effects of light. The dataset includes Rhino CAD geometry, comprehensive spectral material and light source data and window system BSDF data. Example implementations in the two software tools, Radiance and OWL, enable reproducible workflows and support adoption in other software. The dataset is openly available on Zenodo. The office model reproduces Room 518 at the University of Innsbruck, including a west-facing façade and interior furnishings. The factory hall model follows the proposed geometry in the European standard 15193 for building energy performance. Interior reflectances in the office were measured in-situ using a handheld spectrometer. Exterior spectra and factory hall materials matching specified reflectances were obtained from an online spectral materials database. Glazing transmittance was derived from IGDB data using LBNL Optics/WINDOW. BSDFs for venetian blinds at various tilt angles, and for a diffusing pane adapted from the Complex Glazing Database, were generated in WINDOW. Luminaires in both models are specified with photometric files (Eulumdat/IES) and lamp spectra (Fluorescent 840, 4000 K LED). The provided example implementations (Radiance, OWL) include prepared input data and scripts to run first spectral simulations; example results are also included. The dataset is prepared to support reuse by researchers, designers and software developers for method validation, software engineering and comparison, and development of spectral metrics and controls.
Software used on the Buoy for the DNMS system.
Backgorund: Quantification of myocardial blood flow (MBF) by positron emission tomography (PET) is important for investigation of angina in hypertrophic cardiomyopathy (HCM). Several software programs exist for MBF quantification, but they have been mostly evaluated in patients (with normal cardiac geometry), referred for evaluation of coronary artery disease (CAD). Software performance has not been evaluated in HCM patients who frequently have hyperdynamic LV function, LV outflow tract (LVOT) obstruction, small LV cavity size, and variation in the degree/location of LV hypertrophy. Aim: We compared results of MBF obtained using PMod, which permits manual segmentation, to those obtained by FDA-approved QPET software which has an automated segmentation algorithm. Methods: {sup 13}N-ammonia PET perfusion data were acquired in list mode at rest and during pharmacologic vasodilation, in 76 HCM patients and 10 non-HCM patients referred for evaluation of CAD (CAD group.) Data were resampled to create static, ECG-gated and 36-frame-dynamic images. Myocardial flow reserve (MFR) and MBF (in ml/min/g) were calculated using QPET and PMod softwares. Results: All HCM patients had asymmetric septal hypertrophy, and 50% had evidence of LVOT obstruction, whereas non-HCM patients (CAD group) had normal wall thickness and ejection fraction. PMod yielded significantly higher values for global and regional stress-MBF and MFR than for QPET in HCM. Reasonably fair correlation was observed for global rest-MBF, stress-MBF, and MFR using these two softwares (rest-MBF: r = 0.78; stress-MBF: r = 0.66.; MFR: r = 0.7) in HCM patients. Agreement between global MBF and MFR values improved when HCM patients with high spillover fractions (> 0.65) were excluded from the analysis (rest-MBF: r = 0.84; stress-MBF: r = 0.72; MFR: r = 0.8.) Regionally, the highest agreement between PMod and QPET was observed in the LAD territory (rest-MBF: r = 0.82, Stress-MBF: r = 0.68) where spillover fraction was the lowest. Unlike HCM patients, the non-HCM patients (CAD group) demonstrated excellent agreement in MBF/MFR values, obtained by the two softwares, when patients with high spillover fractions were excluded (rest-MBF: r = 0.95; stress-MBF: r = 0.92; MFR: r = 0.95). Conclusions: Anatomic characteristics specific to HCM hearts contribute to lower correlations between MBF/MFR values obtained by PMod and QPET, compared with non-HCM patients. These differences indicate that PMod and QPET cannot be used interchangeably for MBF/MFR analyses in HCM patients.
The demand for high performance computing (HPC) resources continues to grow, driven by the increasing complexity of modeling and simulation, artificial intelligence (AI), and machine learning (ML) workloads [Porter]. The growing energy consumption demand of these HPC systems is a significant concern, both in terms of operational costs and environmental impact. AI hardware accelerators are expected to reach 1.5% of the world’s power consumption by 2029 [Shah].
The University of Oregon (UO) was a partner in the XPRESS project together with Sandia National Laboratories (SNL), Indiana University (IU), Lawrence Berkeley National Laboratory (LBL), Louisiana State University (LSU), Oak Ridge National Laboratory (ORNL), University of Houston (UH), and University of North Carolina (RENCI). UO's objective in the project was to develop "APEX (Asynchronous Performance Environment for eXascale)": performance measurement, analysis, feedback and control infrastructure to asynchronous tasking runtimes, specifically HPX (LSU) and HPX-5 (IU). During the project period, our efforts focused on the need to measure complex tasking models where the traditional instrumentation methods would not work due to an absent/meaningless call-path context and runtime control of task pre-emption and resumption, usually on a different operating system thread. In addition to the challenges of performance measurement, we developed a framework to provide information from all levels of the stack (hardware, operating system, runtime, application) to a feedback & control mechanism for controlling concurrency, task granularity, networking parameters, and other aspects of execution in order to improve execution performance, throughput and/or efficiency. UO demonstrated these developments on leading edge HPC machines at large scale, including a 2017 Gordon Bell submission in which APEX was used to optimize key bottlenecks in an astrophysics application. The following final report summarizes our accomplishments during the XPRESS project.
The following information is missing from the Funding footnote on the first page of the published article: “This study was partly funded by NIH RO1 HL092985.” The last/corresponding author is incorrectly listed on the first page of the published article: The correct name is Abraham MR.
Abstract not provided