Weak Galerkin based a posteriori error estimates for second order elliptic interface problems on polygonal meshes
Not Available
97 MATHEMATICS AND COMPUTING↗
SEARCH · Search NASA
Results for “Computer interfaces”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
PSI/J: A Portable Interface for Submitting, Monitoring, and Managing Jobs
It is generally desirable for high-performance computing (HPC) applications to be portable between HPC systems, for example to make use of more performant hardware, make effective use of allocations, and to co-locate compute jobs with large datasets. Unfortunately, moving scientific applications between HPC systems is challenging for various reasons, most notably that HPC systems have different HPC schedulers. We introduce PSI/J, a job management abstraction API intended to simplify the construction of software components and applications that are portable over various HPC scheduler implementations. We argue that such a system is both necessary and that no viable alternative currently exists. We analyze similar notable APIs and attempt to determine the factors that influenced their evolution and adoption by the HPC community. We base the design of PSI/J on that analysis. We describe how PSI/J has been integrated in three workflow systems and one application, and also show via experiments that PSI/J imposes minimal overhead.
Hategan, Mihael↗
Bridging molecular-scale interfacial science with continuum-scale models
Solid–water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to oversimplified representations that often fail to predict real-world behavior. This is because these models use fixed parameters derived by averaging across a wide physicochemical range observed at the molecular scale. Recent studies have revealed the stochastic nature of molecular-level surface sites that define a variety of reaction mechanisms, rates, and products even across a single surface. To bridge the molecular knowledge and predictive continuum-scale models, we propose to represent surface properties with probability distributions rather than with discrete constant values derived by averaging across a heterogeneous surface. This conceptual shift in continuum-scale modeling requires exponentially rising computational power. By incorporating our molecular-scale understanding of solid–water interfaces into continuum-scale models we can pave the way for next generation critical technologies and novel environmental solutions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Empowering Scientific Discovery Through Computing at the Advanced Photon Source
This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS Data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership Computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication.
Parraga, Hannah↗
An Interface Debonding Model for Integrity Assessment of Nuclear Fuel Rods
The integrity degradation of nuclear fuel rods due to material embrittlement resulting from hydride formation has been extensively investigated. Finite element based fracture mechanics models have been proposed to assess the reduction of load capacity of nuclear fuel rod structures with several distribution scenarios of hydrides. In these finite element models, individual hydrides are modeled by opened cracks distributed in bulk matrix. These models are indeed valid for cases where hydrides fractures occur at low load levels. It is important for finite element models to take into account the cases where hydride fractures occur at high load levels, at which bulk matrix material may undergo reasonable high stress or yield. This requires hydride/bulk matrix interface models that can accommodate both bonded and debonded (fractured or crack initiation) behaviors of hydride/matrix interfaces. However, direct modeling debonding behaviors between hydride and bulk matrix is a great challenge. This is because for ductile structural failure simulations ligaments of bulk matrix often undergo large plastic deformation. Complete solutions often unavailable for crack initiation or debonding in media with finite plastic strain using popular implicit finite element technologies. This paper proposes a new computational framework based on discontinuous Galerkin (DG) finite element formulations for easily handling hydride/matrix interface debonding. We present structural failure simulation results for a quarter plane strain rod model with zircaloy materials. It is concluded that the proposed DG interface debonding model is powerful in simulating structural failure due to hydride failure in bulk media with finite plastic deformation. (author)
36 MATERIALS SCIENCE↗
Sirepo : an open-source cloud-based software interface for X-ray source and optics simulations
Sirepo , a browser-based GUI for X-ray source and optics simulations, is presented. Such calculations can be performed using SRW ( Synchrotron Radiation Workshop ), which is a physical optics computer code, allowing simulation of entire experimental beamlines using the concept of a `virtual beamline' with accurate treatment of synchrotron radiation generation and propagation through the X-ray optical system. SRW is interfaced with Sirepo by means of a Python application programming interface. Sirepo supports most of the optical elements currently used at beamlines, including recent developments in SRW . In particular, support is provided for the simulation of state-of-the-art X-ray beamlines, exploiting the high coherence and brightness of modern light source facilities. New scientific visualization and reporting capabilities have been recently implemented within Sirepo , as well as automatic determination of electron beam and undulator parameters. Publicly available community databases can be dynamically queried for error-free access to material characteristics. These computational tools can be used for the development and commissioning of new X-ray beamlines and for testing feasibility and optimization of experiments. The same interface can guide simulation on a local computer, a remote server or a high-performance cluster. Sirepo is available online and also within the NSLS-II firewall, with a growing number of users at other light source facilities. Our open source code is available on GitHub.
99 GENERAL AND MISCELLANEOUS↗
Order-disorder behavior at thin film oxide interfaces
Order-disorder processes fundamentally determine the structure and properties of many important oxide systems for energy and computing applications. While these processes have been intensively studied in bulk materials, they are less investigated and understood for nanostructured oxides in highly non-equilibrium conditions. These systems can now be realized through a range of deposition techniques and probed at exceptional spatial and chemical resolution, leading to a greater focus on interface dynamics. In this work, we survey a selection of recent studies of order-disorder behavior at thin film oxide interfaces, with a particular emphasis on the emergence of order during synthesis and disorder in extreme irradiation environments. We summarize key trends and identify directions for future study in this growing research area.
36 MATERIALS SCIENCE↗
Multimetallic Layered Composites (MMLCs) for Rapid, Economical Advanced Reactor Deployment (Final Report)
This project focused on the development of multi-metallic layered composites (MMLCs) for advanced fission reactor technologies. There are many instances where one alloy or material simply cannot meet all the demands thrown at it by a reactor system, or cannot allow it to perform as strongly as one would like. Instead of focusing all our effort on developing one perfect alloy, we seek to leverage the design principle of “separation of functionality,” used in many other arenas in design, to boost performance beyond single alloys alone. One illustrative example shows the power of this approach for molten salt-cooled reactors: A three meter tall, three meter diameter reactor vessel made of Incoloy 800 was quoted at $\$$500k in 2018. A Hastelloy N vessel was quoted at $\$$5M. An MMLC vessel, in which a layer of Hastelloy N would be weld-overlaid onto Incoloy 800, was quoted at $\$$700k, and it would achieve the same performance. The potential economic gains of leveraging this approach are therefore substantial. At a minimum, each MMLC would contain one core structural layer and one coolant-facing corrosion-resistant layer. Sometimes, MMLCs required buffer layers, as the structural and corrosion-resistant layers were metallurgically incompatible. In other words, they didn’t always play nice, thus separating layers compatible with both functioned as intermediaries to keep the composite together. However, in doing so we inevitably produce new interfaces, where new issues can arise. Therefore, this project focused on what happens at these interfaces from a combination of high temperatures, irradiation, corrosion, and time. After all, a reactor makes money when it is operating, and outages of any kind erode its economic viability. First, we set out to experimentally prove that MMLCs for at least two advanced reactor systems can be made, today, in US domestic facilities. In this respect we were successful – one MMLC (a Ni-201/Incoloy 800H composite) was successfully made and drawn into two-inch coolant piping. Others were attempted, though new issues relating to cracking in vanadium layers for one and radiation damage performance of the corrosion-resistant layer in another prevented us from moving further in those specific arenas – these are engineering problems which deserve continued focus after this project. Additional experimental work focused on long-term corrosion testing of the outermost layers of the salt-cooled and liquid lead-cooled MMLC concepts, which would then be fed into predictions of how long the MMLCs could last. Next, computational (thermodynamics and atomistic) simulation studies studied how much we expect the interfaces to “blend,” due to the mixing action of neutron irradiation. This eats into both the margin for the structural layer of each MMLC, as dilution from the corrosion-resistant layer into the structural layer would decrease the total load-bearing capacity of an MMLC of finite size. On the other hand, dilution of the corrosion-resistant layer into the structural layer further reduced the margin of corrodible material, reducing the lifetime of the MMLC or necessitating extra thickness to be imparted to the MMLC to meet its functional requirements. Work here focused on irradiation-induced segregation to predict new phases which may embrittle the MMLCs, as well as quantifying irradiation-induced mixing at each interface. The results showed that mixing is expected, but it is both steady and therefore predictable, and not lifetime-limiting for most MMLC concepts – it simply has to be accounted for in calculations of reactor performance when utilizing an MMLC. Then, full-core simulations using the experimentally-derived corrosion data, the computationally discovered irradiation-induced mixing data (partially validated by experiment), and existing, benchmarked core designs for large and small sized reactor concepts (one salt-cooled, one lead-cooled) were conducted to quantify any expansion of reactor operating envelopes achieved by utilizing these MMLCs. This new framework, called REX (Reactor Envelope Expansion), incorporates a combination of core neutronics, thermal hydraulics, and the material performance data derived from this project to see how using an MMLC expands advanced fission reactor operating envelopes. It was discovered that in some cases, MMLC utilization does indeed increase the maximum operating temperatures and cycle lengths of reactor concepts, while in other cases it does not. Finally, our tech-to-market (T2M) strategy was not necessarily to create specific embodiments of MMLCs for immediate sale (because getting into the nuclear market is incredibly slow and laden with regulation, this is a long-term goal), but rather immediate stimulation of US industry using the design approach of MMLCs derived from this project. In this respect we were successful, as one of the PhD students funded on this project co-founded Allium Engineering, Inc., which created a stainless steel / low-alloy steel MMLC to function as chloride corrosion-resistant rebar for embedding into concrete structures. Allium Engineering continues to be successful, having recently opened their first factory as of this writing.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources
Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.
97 MATHEMATICS AND COMPUTING↗
Electronic Medical Record Integration for Streamlined DXA Reporting
Dual-energy X-ray absorptiometry (DXA) is the most frequently performed examination to assess bone mineral density in clinical practice. Aside from images and graphical displays, many numerical values are part of DXA reports. These values are typically manually entered into the formal report through the electronic medical record or PACS workstation. The process takes time and is prone to errors. Exporting the DXA numerical data via HL7 engine to the electronic medical record was proposed to improve reporting efficiency and accuracy. The output from the DXA unit computer was reconfigured to export the report content via the HL7 interface engine into the electronic medical record. Radiology interpretive reporting was subsequently done directly in the electronic medical record. In the evaluation of errors, 100 preliminary DXA reports before the change and 100 after the change were examined. These reports were analyzed for errors that included decimal change, number transposition, negative number issue, other incorrect number error, and failure to include prior exam for comparison. In addition, report turnaround times were evaluated before and after the changes were made. Reporting time evaluations included 1-year volume prior to change (3915 reports) and 1 month post-change (206 reports). Of 100 DEXA exams before the change, 15 final reports contained 25 numerical errors. After the change, no numerical errors in the reports were identified. Exam end to final report time decreased from 2159 to 625 min on average. Automating data transmittal from the DXA modality for report generation improves accuracy and turnaround time. This approach did not require any third party software, and healthcare information security concerns were negated since we are using our standard workstations. Secondary to the affordability and applicability to the large percentage of the population using electronic medical record systems, this type of automated workflow is recommended.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Computational Approaches for Clean Energy Materials
Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008.
density functional theory↗
Portable Data-Parallel Surface Reconstruction on a Uniform Rectilinear Grid
With the increasing heterogeneity and on-node parallelism of high-performance computing hardware, a major challenge is to develop portable and efficient algorithms and software. In this work, we present our implementation of a portable code to perform surface reconstruction using NVIDIA's Thrust library. Surface reconstruction is a technique commonly used in volume tracking methods for simulations of multimaterial flow with interfaces. Here, we have designed a 3D mesh data structure that is easily mapped to the 1D vectors used by Thrust and at the same time is simple to use and uses familiar data structure terminology (such as cells, faces, vertices, and edges). With this new data structure in place, we have implemented a piecewise linear interface reconstruction algorithm in 3 dimensions that effectively exploits the symmetry present in a uniform rectilinear computational cell. Finally, we report performance results, which show that a single implementation of these algorithms can be compiled to multiple backends (specifically, multi-core CPUs, NVIDIA GPUs, and Intel Xeon Phi processors), making efficient use of the available parallelism on each. We also compare performance of our implementation to a legacy FORTRAN implementation in Message Passing Interface (MPI) and show performance parity on single and multi-core CPU and achieved good parallel speed-ups on GPU. In conclusion, our research demonstrates the advantage of performance portability of the underlying data-parallel programming model.
97 MATHEMATICS AND COMPUTING↗
Computational Studies of Thermal Conductivity of Multiphase Ceramics for Inert Matrix Fuel
Nuclear energy is a major contributor to base load electrical power around the world and is predicted to continue to expand in the coming years to meet the increasing global demand for electricity. As new reactors are being constructed to meet these demands, it is imperative to make power plants intrinsically safer and more cost efficient. A major consideration to improving safety and performance is the enhancement of current ceramic nuclear fuels. By using an inert matrix fuel, additional phases can be added to the fuel to increase both the thermal and mechanical properties of the fuel at room and reactor conditions. In this study, multiphase ceramics and the individual single-phase components, will be investigated to understand the effects of multiple phases, various grain sizes, and interfaces on the temperature dependent thermal conductivity. A coupled approach involving both experimental measurements and computational modeling will give insight into the development of future models for understanding the role interfaces play in thermal transport within multiphase systems. (authors)
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A Fortran–Python interface for integrating machine learning parameterization into earth system models
Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.
54 ENVIRONMENTAL SCIENCES↗
A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models
Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.
54 ENVIRONMENTAL SCIENCES↗
Phase field model of fluid-driven fracture in elastic media: Immersed-fracture formulation and validation with analytical solutions
Propagation of fluid-driven fractures plays an important role in natural and engineering processes, including transport of magma in the lithosphere, geologic sequestration of carbon dioxide, and oil and gas recovery from low-permeability formations, among many others. The simulation of fracture propagation poses a computational challenge as a result of the complex physics of fracture and the need to capture disparate length scales. Phase field models represent fractures as a diffuse interface and enjoy the advantage that fracture nucleation, propagation, branching, or twisting can be simulated without ad hoc computational strategies like remeshing or local enrichment of the solution space. Here we propose a new quasi-static phase field formulation for modeling fluid-driven fracturing in elastic media at small strains. The approach fully couples the fluid flow in the fracture (described via the Reynolds lubrication approximation) and the deformation of the surrounding medium. The flow is solved on a lower dimensionality mesh immersed in the elastic medium. This approach leads to accurate coupling of both physics. We assessed the performance of the model extensively by comparing results for the evolution of fracture length, aperture, and fracture fluid pressure against analytical solutions under different fracture propagation regimes. Thus, the excellent performance of the numerical model in all regimes builds confidence in the applicability of phase field approaches to simulate fluid-driven fracture.
58 GEOSCIENCES↗
Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments
The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.
97 MATHEMATICS AND COMPUTING↗
Apparatus and method for fusion of compute and switching functions of exascale system into a single component by using configurable network-on-chip fabric with distributed dual mode input-output ports and programmable network interfaces
Described is an apparatus which comprises: a Network-On-Chip fabric using crossbar switches, having distributed ingress and egress ports; and a dual-mode network interface coupled to at least one crossbar switch, the dual-mode network interface is to include: a dual-mode circuitry; a controller operable to: configure the dual-mode circuitry to transmit and receive differential signals via the egress and ingress ports, respectively, and configure the dual-mode circuitry to transmit and receive signal-ended signals via the egress and ingress ports, respectively.
97 MATHEMATICS AND COMPUTING↗