Search NASA⌕ Search

SEARCH · Search NASA

Results for “science of scaling”

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.

At least 73 records · Page 4

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗

Visualization: A pathway to enhanced scientific productivity in the expanding missions of Space and Earth Sciences

The movement toward the solution of problems involving large-scale system science, the ever-increasing capabilities of three-dimensional, time-dependent numerical models, and the enhanced capabilities of 'in situ' and remote sensing instruments bring a new era of scientific endeavor that requires an important change in our approach to mission planning and the task of data reduction and analysis. Visualization is at the heart of the requirements for a much-needed enhancement in scientific productivity as we face these new challenges. This article draws a perspective on the problem as it crosses discipline boundaries from solar physics to atmospheric and ocean sciences. It also attempts to introduce visualization as a new approach to scientific discovery and a tool which expedites and improves our insight into physically complex problems. A set of simple illustrations demonstrates a number of visualization techniques and the discussion emphasizes the trial-and-error and search-and-discover modes that are necessary for the techniques to reach their full potential. Further discussions also point to the importance of integrating data access, management, mathematical operations, and visualization into a single system. Some of the more recent developments in this area are reviewed.

Szuszczewicz, E. P.↗

More is definitely different: the zebrafish as witness: Comment on ”Structure and function in artificial, zebrafish and human neural networks” by Peng Ji et al.

We report half a century ago, in his foundation paper More is different, Anderson stated the relevance of including multiple scales of interaction, instead of trying to reduce everything to fundamental principles. By doing so, one may witness phenomena that were not expected by looking at each individual components separately. This idea finds indeed numerous realizations such as the synchronization phenomenon, where a multitude of dynamical systems suddenly start to behave coherently together, or the spreading of diseases over entire populations, to name only two examples. But if one would have to give a single example of a system where the multiplicity of interaction scales is of utmost importance, it would probably be the brain. In their review, Ji et al. give a comprehensive overview of the multiple scales of science required in the investigation of structural and functional brain networks, as well as their interplay.

59 BASIC BIOLOGICAL SCIENCES↗

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗

ARIES/Flatirons Facility - Hydrogen System Capability Buildout

Under the "Advanced Research on Integrated Energy Systems" (ARIES) initiative, hydrogen system capabilities including a MW-scale electrolyzer, storage system, and MW-scale fuel cell generator will be designed and commissioned at NREL's Flatirons Campus. This hydrogen infrastructure will support H2@Scale goals by enabling integrated systems R&D to study the science of scaling for hydrogen energy systems. The system is designed with flexibility to provide a testbed to demonstrate systems integration, grid services, energy storage, direct renewable hydrogen production, and innovative end use applications (e.g. HD transportation, natural gas blending, etc.).

DIRECT ENERGY CONVERSION,HYDROGEN↗

ARIES/Flatirons Facility - Hydrogen System Capability Buildout

Under the "Advanced Research on Integrated Energy Systems" (ARIES) initiative, hydrogen system capabilities including a MW-scale electrolyzer, storage system, and MW-scale fuel cell generator will be designed and commissioned at NREL's Flatirons Campus. This hydrogen infrastructure will support H2@Scale goals by enabling integrated systems R&D to study the science of scaling for hydrogen energy systems.The system is designed with flexibility to provide a testbed to demonstrate systems integration, grid services, energy storage, direct renewable hydrogen production, and innovative end use applications (e.g. HD transportation, natural gas blending, etc.).

Advanced Research on Integrated Energy Systems↗

ARIES / Flatirons Facility - Hydrogen System Capability Buildout

Under the "Advanced Research on Integrated Energy Systems" (ARIES) initiative, hydrogen system capabilities including a MW-scale electrolyzer, storage system, and MW-scale fuel cell generator will be designed and commissioned at NREL's Flatirons Campus. This hydrogen infrastructure will support H2@Scale goals by enabling integrated systems R&D to study the science of scaling for hydrogen energy systems.The system is designed with flexibility to provide a testbed to demonstrate systems integration, grid services, energy storage, direct renewable hydrogen production, and innovative end use applications (e.g. HD transportation, natural gas blending, etc.).

electrical grid↗

Learning to Scale the Summit: AI for Science on a Leadership Supercomputer

The Summit system at Oak Ridge National Lab-oratory (ORNL) has been the world's top AI for science su-percomputer for several years, ranked world's fastest computer at its 2018 launch and currently top system in the US and #2 on the TOP5OO list. Summit's purposeful design to handle both conventional modeling and simulation science and emerging AI workloads has made it a leading destination for AI-powered computational science. We report here on AI for science usage on Summit near the midpoint of its lifespan. We review AI usage across the many science projects that have used Summit. We then examine in detail a set of applications scaling AI to full system as well as projects implementing AI-coordinated science discovery workflows on Summit. Finally, we offer some observations regarding the future of advancing scientific knowledge and understanding via AI, especially in the context of leadership-class scientific computing.

Joubert, Wayne↗

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗

The Computing and Data Grid Approach: Infrastructure for Distributed Science Applications

With the advent of Grids - infrastructure for using and managing widely distributed computing and data resources in the science environment - there is now an opportunity to provide a standard, large-scale, computing, data, instrument, and collaboration environment for science that spans many different projects and provides the required infrastructure and services in a relatively uniform and supportable way. Grid technology has evolved over the past several years to provide the services and infrastructure needed for building 'virtual' systems and organizations. We argue that Grid technology provides an excellent basis for the creation of the integrated environments that can combine the resources needed to support the large- scale science projects located at multiple laboratories and universities. We present some science case studies that indicate that a paradigm shift in the process of science will come about as a result of Grids providing transparent and secure access to advanced and integrated information and technologies infrastructure: powerful computing systems, large-scale data archives, scientific instruments, and collaboration tools. These changes will be in the form of services that can be integrated with the user's work environment, and that enable uniform and highly capable access to these computers, data, and instruments, regardless of the location or exact nature of these resources. These services will integrate transient-use resources like computing systems, scientific instruments, and data caches (e.g., as they are needed to perform a simulation or analyze data from a single experiment); persistent-use resources. such as databases, data catalogues, and archives, and; collaborators, whose involvement will continue for the lifetime of a project or longer. While we largely address large-scale science in this paper, Grids, particularly when combined with Web Services, will address a broad spectrum of science scenarios. both large and small scale.

Johnston, William E.↗

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗

Reusability First: Toward FAIR Workflows

The FAIR principles of open science (Findable, Accessible, Interoperable, and Reusable) have had transformative effects on modern large-scale computational science. In particular, they have encouraged more open access to and use of data, an important consideration as collaboration among teams of researchers accelerates and the use of workflows by those teams to solve problems increases. How best to apply the FAIR principles to workflows themselves, and software more generally, is not yet well understood. We argue that the software engineering concept of technical debt management provides a useful guide for application of those principles to workflows, and in particular that it implies reusability should be considered as ‘first among equals’. Moreover, our approach recognizes a continuum of reusability where we can make explicit and selectable the tradeoffs required in workflows for both their users and developers.To this end, we propose a new abstraction approach for reusable workflows, with demonstrations for both synthetic workloads and real-world computational biology workflows. Through application of novel systems and tools that are based on this abstraction, these experimental workflows are refactored to rightsize the granularity of workflow components to efficiently fill the gap between end-user simplicity and general customizability. Our work makes it easier to selectively reason about and automate the connections between trade-offs across user and developer concerns when exposing degrees of freedom for reuse. Additionally, by exposing fine-grained reusability abstractions we enable performance optimizations, as we demonstrate on both institutional-scale and leadership-class HPC resources.

Wolf, Matthew↗

Fractal Characterization of Multitemporal Scaled Remote Sensing Data

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the ingestion, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISS. Scale is fundamental to the characterization of geo-spatial data as represented in GISS, but we have relatively little insight on the effects of, or how to measure the effects of, scale in representing multiscaled data; i.e., data that are acquired in different formats (e.g., map, digital) and exist in varying spatial, temporal, and in the case of remote sensing data, radiometric, configurations. This is particularly true in the emerging era of Integrated GISs (IGIS), wherein spatial data in a variety of formats (e.g., raster, vector) are combined with multiscaled remote sensing data, capable of performing highly sophisticated space-time data analyses and modeling. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective; i.e., "scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which can lead to substantive confusion in the input, manipulation, analyses, and output of IGISs (Quattrochi, 1993). Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale" across a wide number of spatial disciplines that use GISs.

Quattrochi, Dale A.↗

Fractal Characterization of Multitemporal Remote Sensing Data

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the capture, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISs. Scale is fundamental to the characterization of geo-spatial data as represented in GISs, but we have relatively little insight on how to measure the effects of scale in representing data that are acquired in different formats and exist in varying spatial, temporal and radiometric configurations. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective. "Scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which lead to substantial confusion in the input, manipulation, analysis, and output operations. Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale' across all disciplines that use GISs.

Quattrochi, Dale A.↗

Fractal Characterization of Multitemporal Remote Sensing Data

Scale is an 'innate' concept in geographical information systems (GIS). It is recognized as something that is intrinsic to the capture, storage, manipulation, analysis, modelling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GIS becomes more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GIS. Scale is fundamental to the characterization of geo-spatial data as represented in GIS, but we have relatively little insight on how to measure the effects of scale in representing data that are acquired in different formats and exist in varying spatial, temporal and radiometric configurations. Moreover, the complexities associated with the integration of multi-scaled data sets in a multitude of formats are exacerbated by the confusion of what the term 'scale' means from a multidisciplinary perspective. 'Scale' takes on significantly different meanings depending upon one's disciplinary background and spatial perspective, which can lead to substantial confusion in the input, manipulation, analysis, and output operations. Hence, we must begin to look at the universality of scale and begin to develop the theory, methods and techniques necessary to advance knowledge on the 'Science of scale' across all disciplines that use GIS.

Quattrochi, Dale A.↗

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

54 ENVIRONMENTAL SCIENCES↗

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗