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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Adaptive job and resource management for the growing quantum cloud

As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing.This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations. Altogether, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

The Namibia Early Flood Warning System, A CEOS Pilot Project

Over the past year few years, an international collaboration has developed a pilot project under the auspices of Committee on Earth Observation Satellite (CEOS) Disasters team. The overall team consists of civilian satellite agencies. For this pilot effort, the development team consists of NASA, Canadian Space Agency, Univ. of Maryland, Univ. of Colorado, Univ. of Oklahoma, Ukraine Space Research Institute and Joint Research Center(JRC) for European Commission. This development team collaborates with regional , national and international agencies to deliver end-to-end disaster coverage. In particular, the team in collaborating on this effort with the Namibia Department of Hydrology to begin in Namibia . However, the ultimate goal is to expand the functionality to provide early warning over the South Africa region. The initial collaboration was initiated by United Nations Office of Outer Space Affairs and CEOS Working Group for Information Systems and Services (WGISS). The initial driver was to demonstrate international interoperability using various space agency sensors and models along with regional in-situ ground sensors. In 2010, the team created a preliminary semi-manual system to demonstrate moving and combining key data streams and delivering the data to the Namibia Department of Hydrology during their flood season which typically is January through April. In this pilot, a variety of moderate resolution and high resolution satellite flood imagery was rapidly delivered and used in conjunction with flood predictive models in Namibia. This was collected in conjunction with ground measurements and was used to examine how to create a customized flood early warning system. During the first year, the team made use of SensorWeb technology to gather various sensor data which was used to monitor flood waves traveling down basins originating in Angola, but eventually flooding villages in Namibia. The team made use of standardized interfaces such as those articulated under the Open Cloud Consortium (OGC) Sensor Web Enablement (SWE) set of web services was good [1][2]. However, it was discovered that in order to make a system like this functional, there were many performance issues. Data sets were large and located in a variety of location behind firewalls and had to be accessed across open networks, so security was an issue. Furthermore, the network access acted as bottleneck to transfer map products to where they are needed. Finally, during disasters, many users and computer processes act in parallel and thus it was very easy to overload the single string of computers stitched together in a virtual system that was initially developed. To address some of these performance issues, the team partnered with the Open Cloud Consortium (OCC) who supplied a Computation Cloud located at the University of Illinois at Chicago and some manpower to administer this Cloud. The Flood SensorWeb [3] system was interfaced to the Cloud to provide a high performance user interface and product development engine. Figure 1 shows the functional diagram of the Flood SensorWeb. Figure 2 shows some of the functionality of the Computation Cloud that was integrated. A significant portion of the original system was ported to the Cloud and during the past year, technical issues were resolved which included web access to the Cloud, security over the open Internet, beginning experiments on how to handle surge capacity by using the virtual machines in the cloud in parallel, using tiling techniques to render large data sets as layers on map, interfaces to allow user to customize the data processing/product chain and other performance enhancing techniques. The conclusion reached from the effort and this presentation is that defining the interoperability standards in a small fraction of the work. For example, once open web service standards were defined, many users could not make use of the standards due to security restrictions. Furthermore, once an interoperable sysm is functional, then a surge of users can render a system unusable, especially in the disaster domain.

Mandl, Daniel↗
DOE OSTI2021

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.

14 SOLAR ENERGY↗
DOE OSTI2026

How Cloud is Accelerating Research at NREL

This presentation coincides with AWS's announcement of their new Parallel Computing Service (PCS) which allows for easy creation of HPC-style clusters in their AWS cloud computing platform. I helped them beta test this service before it was made generally available in August. AWS asked if we would be interested in discussing our experience with the PCS service, and our experience with HPC workloads in the cloud in general, so this slideshow discusses a brief history of scientific computing at NREL and shares a bit of our experiences and approach to utilizing cloud services for HPC-style workloads.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Methods of editing cloud and atmospheric layer affected pixels from satellite data

Practical methods of computer screening cloud-contaminated pixels from data of various satellite systems are proposed. Examples are given of the location of clouds and representative landscape features in HCMM spectral space of reflectance (VIS) vs emission (IR). Methods of screening out cloud affected HCMM are discussed. The character of subvisible absorbing-emitting atmospheric layers (subvisible cirrus or SCi) in HCMM data is considered and radiosonde soundings are examined in relation to the presence of SCi. The statistical characteristics of multispectral meteorological satellite data in clear and SCi affected areas are discussed. Examples in TIROS-N and NOAA-7 data from several states and Mexico are presented. The VIS-IR cluster screening method for removing clouds is applied to a 262, 144 pixel HCMM scene from south Texas and northeast Mexico. The SCi that remain after cluster screening are sited out by applying a statistically determined IR limit.

Nixon, P. R.↗
DOE OSTI2025

If We Build Them, They Will Run: Automated HPC Apps Deployment and Profiling with eBPF in Cloud

The high performance computing (HPC) community is in a period of transition. The rise of AI/ML coupled with a changing landscape of resources deems portability a new metric of performance, and methods to move between on-premises and cloud environments and assess compatibility are paramount. Here we design and test a strategy for bridging the gap between traditional HPC and Kubernetes environments – first containerizing applications, providing automated orchestration to run studies, and packaging the setup with automated means to assess performance using low overhead eXtended Berkeley Packet Filter (eBPF) programs. We first assess different designs for eBPF collection, demonstrating a tradeoff between number of programs deployed on a node and overhead added. We develop 5 low overhead eBPF programs that combine with streaming ML models to assess CPU, futex, TCP, shared memory, and file access across four different builds of an HPC application for CPU and GPU. We use eBPF data to generate insights into the possible underlying etiology of scaling issues. We then assess compatibility of a well-known benchmark, HPCG, across matrices of micro-architectures and optimization levels (217 containers across 24 instance types and over 7500 runs). We provide to the community 30 applications to deploy in our automated setup and perform a scaling study from 4 to a maximum of 256 nodes for both CPU and GPU applications. Finally, we use our gained knowledge about performance to generate compatibility artifacts that are used by a newly developed Kubernetes controller to intelligently select instance type based on optimizing a figure of merit. Along with insights to scaling in this environment with a collection of applications and templates to work from, we provide an overall strategy for approaching HPC application deployment and image selection based on compatibility in cloud.

Computer science↗
NASA NTRSDate not supplied

Progress Towards a Common CERES Cloud Mask Algorithm for MODIS and VIIRS: Evaluation using CALIOP Data

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has endeavored to produce a long-term global climate data record for detecting changes in the Earth’s radiation budget and to improve understanding of how clouds contribute to those changes. CERES incorporates cloud information derived from passive narrowband satellite imaging radiometers, and over the course of the project different instruments have contributed to this effort, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS).Cloud properties have been derived from MODIS on the Aqua satellite for CERES since 2002,and VIIRS on the NOAA-20 satellite launched in 2017 will continue the cloud record once Aqua-MODIS reaches the end of its operational lifetime. However, MODIS and VIIRS have different spectral capabilities and characteristics which complicates our ability to seamlessly transition the record from MODIS to VIIRS. This study evaluates some of the recent algorithm modifications incorporated by the CERES Cloud Working Group towards developing a unified cloud mask algorithm for MODIS and VIIRS that utilizes a set of spectral bands common to both instruments. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products are used to evaluate MODIS and VIIRS regional cloud fraction estimates and identify areas where improvements can be made. The cloud mask algorithm relies on computed estimates of cloud-free top-of-atmosphere radiances to differentiate cloudy and cloud-free imager pixels. The unified cloud mask algorithm currently under development uses a reduced number of spectral bands common to both MODIS and VIIRS and so relies on the accuracy of these simulated cloud-free radiances more heavily than predecessor algorithms, i.e., the CERES Edition 4 cloud mask, which use as much spectral information as available on each imager with less consideration for cross-platform consistency. Our validation strategy using CALIOP data is also described. The CALIOP observations are critical for assessing cloud detection accuracies and for independently confirming cloud-free conditions which enables a more robust evaluation of the simulated cloud-free radiances for biases due to factors such as view angle and the spectral dependence of water vapor absorption. These effects are known to differ for the two satellite instruments. Comparisons of MODIS and VIIRS cloud fractions are presented in context with estimates from CALIOP and from the CERES Edition 4 cloud mask algorithm to gauge current progress in developing accurate and consistent MODIS and VIIRS cloud properties for CERES.

CERES↗
NASA NTRS2005

Global, Multi-Year Analysis of Clouds and Earth's Radiant Energy System Terra Observations and Radiative Transfer Calculations

An extended record of the Terra Surface and Atmosphere Radiation Budget (SARB) computed by CERES (Clouds and Earth s Radiant Energy System) is produced in gridded form, facilitating an investigation of global scale direct aerosol forcing. The new gridded version (dubbed FSW) has a spacing of 1 at the Equator. A companion document (Rutan et al. 2005) focuses on advances to (and validation of) the ungridded, footprint scale calculations (dubbed CRS), primarily in clear-sky conditions. While mainly intended to provide observations of fluxes at the top of atmosphere (TOA), CERES (Wielicki et al. 1996) includes a program to also compute the fluxes at TOA, within the atmosphere and at the surface, and also to validate the results with independent ground based measurements (Charlock and Alberta 1996). ARM surface data has been a focus for this component of CERES. To permit the user to infer cloud forcing and direct aerosol forcing with the computed SARB, CERES includes surface and TOA fluxes that have been computed for cloud-free (clear) and aerosol free (pristine) footprints; this accounts for aerosol effects (SW scattering and absorption, and LW scattering, absorption and emission) to both clear and cloudy skies.

Charlock, T. P.↗
NASA NTRS1990

Estimating integrated cloud liquid water from extended time observations of solar irradiance

An analysis technique used to estimate the integrated liquid water content (LWC) from the measured solar irradiance is described. The cloud transmittance is computed by dividing the irradiance measured at some time by a clear sky value obtained at the same time on a cloudless day. From the transmittance and the zenith angle, the cloud LWC is computed using the radiative transfer parameterizations of Stephens et al., (1984). The results are compared with 17 days of mm-wave (20.6 and 31.65 GHz) radiometer measurements made during the First ISCCP Regional Experiment (FIRE) Intensive Field Observation (IFO) in July of 1987.

Fairall, C. W.↗
NASA NTRS1990

An eight-month climatology of marine stratocumulus cloud fraction, albedo, and integrated liquid water

As part of the FIRE/Extended Time Observations (ETO) program, extended time observations were made at San Nicolas Island (SNI) from March to October, 1987. Hourly averages of air temperature, relative humidity, wind speed and direction, solar irradiance, and downward longwave irradiance were recorded. The radiation sensors were standard Eppley pyranometers (shortwave) and pyrgeometers (longwave). The SNI data were processed in several ways to deduce properties of the stratocumulus covered marine boundary layer (MBL). For example, from the temperature and humidity the lifting condensation level, which is an estimate of the height of the cloud bottom, can be computed. A combination of longwave irradiance statistics can be used to estimate fractional cloud cover. An analysis technique used to estimate the integrated cloud liquid water content (W) and the cloud albedo from the measured solar irradiance is also described. In this approach, the cloud transmittance is computed by dividing the irradiance measured at some time by a clear sky value obtained at the same hour on a cloudless day. From the transmittance and the zenith angle, values of cloud albedo and W are computed using the radiative transfer parameterizations of Stephens (1978). These analysis algorithms were evaluated with 17 days of simultaneous and colocated mm-wave (20.6 and 31.65 GHz) radiometer measurements of W and lidar ceilometer measurements of cloud fraction and cloudbase height made during the FIRE IFO. The algorithms are then applied to the entire data set to produce a climatology of these cloud properties for the eight month period.

Fairall, C. W.↗
NASA NTRS1997

Parameterizing Grid-Averaged Longwave Fluxes for Inhomogeneous Marine Boundary Layer Clouds

This paper examines the relative impacts on grid-averaged longwave flux transmittance (emittance) for Marine Boundary Layer (MBL) cloud fields arising from horizontal variability of optical depth tau and cloud sides, First, using fields of Landsat-inferred tau and a Monte Carlo photon transport algorithm, it is demonstrated that mean all-sky transmittances for 3D variable MBL clouds can be computed accurately by the conventional method of linearly weighting clear and cloudy transmittances by their respective sky fractions. Then, the approximations of decoupling cloud and radiative properties and assuming independent columns are shown to be adequate for computation of mean flux transmittance. Since real clouds have nonzero geometric thicknesses, cloud fractions A'(sub c) presented to isotropic beams usually exceed the more familiar vertically projected cloud fractions A(sub c). It is shown, however, that when A(sub c)less than or equal to 0.9, biases for all-sky transmittance stemming from use of A(sub c) as opposed to A'(sub c) are roughly 2-5 times smaller than, and opposite in sign to, biases due to neglect of horizontal variability of tau. By neglecting variable tau, all-sky transmittances are underestimated often by more than 0.1 for A(sub c) near 0.75 and this translates into relative errors that can exceed 40% (corresponding errors for all-sky emittance are about 20% for most values of A(sub c). Thus, priority should be given to development of General Circulation Model (GCM) parameterizations that account for the effects of horizontal variations in unresolved tau, effects of cloud sides are of secondary importance. On this note, an efficient stochastic model for computing grid-averaged cloudy-sky flux transmittances is furnished that assumes that distributions of tau, for regions comparable in size to GCM grid cells, can be described adequately by gamma distribution functions. While the plane-parallel, homogeneous model underestimates cloud transmittance by about an order of magnitude when 3D variable cloud transmittances are less than or equal to 0.2 and by approx. 20% to 100% otherwise, the stochastic model reduces these biases often by more than 80%.

Barker, Howard W.↗
DOE OSTI2021

Large scale multi-node simulations of $\mathbb{Z}_2$ gauge theory quantum circuits using Google Cloud Platform

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units -- Application-specific Integrated Circuits (ASICs) specialized for Machine Learning -- may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating $\mathbb{Z}_2$ quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.

Gustafson, Erik↗
NASA NTRS2016

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↗
NASA NTRS2019

Prognostics As-A-Service: A Scalable Cloud Architecture for Prognostics

Comprehensive aircraft system health-state awareness is critical for maintaining safe, efficient growth in global operations, enabling higher levels of autonomy, and facilitating new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous systems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical composite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degradation to preserve safety and efficiency. Providing this information in a reliable manner in computationally constrained environments and across a wide range of vehicles and systems continues to be a challenge. This challenge can be partially resolved through cloud computing, where the execution of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A-Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.

Prognostics↗
NASA NTRS1980

Collapse and equilibrium of rotating, adiabatic clouds

A numerical hydrodynamics computer code analysis of the collapse and establishment of equilibrium of adiabatic gas clouds restricted to axial symmetry found that the clouds are originally uniform in density and rotation. The method can compare the dynamic collapse and approach to equilibrium with the data on incompressible uniformly rotating equilibrium clouds and on equilibrium structures of differentially rotating polytropes. It is concluded that the stellar formation theory indicates that the low alpha fragments produced at the termination of the dynamic isothermal collapse phase of interstellar clouds may undergo significant dynamic collapse in an adiabatic regime leading to transitory ring formation and additional fragmentation on a smaller scale.

Boss, A. P.↗
NASA NTRS1993

The three-dimensional structure of cumulus clouds over the ocean. 1: Structural analysis

Thermal channel (channel 6, 10.4-12.5 micrometers) images of five Landsat thematic mapper cumulus scenes over the ocean are examined. These images are thresholded using the standard International Satellite Cloud Climatology Project (ISCCP) thermal threshold algorithm. The individual clouds in the cloud fields are segmented to obtain their structural statistics which include size distribution, orientation angle, horizontal aspect ratio, and perimeter-to-area (PtA) relationship. The cloud size distributions exhibit a double power law with the smaller clouds having a smaller absolute exponent. The cloud orientation angles, horizontal aspect ratios, and PtA exponents are found in good agreement with earlier studies. A technique also is developed to recognize individual cells within a cloud so that statistics of cloud cellular structure can be obtained. Cell structural statistics are computed for each cloud. Unicellular clouds are generally smaller (less than or equal to 1 km) and have smaller PtA exponents, while multicellular clouds are larger (greater than or equal to 1 km) and have larger PtA exponents. Cell structural statistics are similar to those of the smaller clouds. When each cell is approximated as a quadric surface using a linear least squares fit, most cells have the shape of a hyperboloid of one sheet, but about 15% of the cells are best modeled by a hyperboloid of two sheets. Less than 1% of the clouds are ellipsoidal. The number of cells in a cloud increases slightly faster than linearly with increasing cloud size. The mean nearest neighbor distance between cells in a cloud, however, appears to increase linearly with increasing cloud size and to reach a maximum when the cloud effective diameter is about 10 km; then it decreases with increasing cloud size. Sensitivity studies of threshold and lapse rate show that neither has a significant impact upon the results. A goodness-of-fit ratio is used to provide a quantitative measure of the individual cloud results. Significantly improved results are obtained after applying a smoothing operator, suggesting the eliminating subresolution scale variations with higher spatial resolution may yield even better shape analyses.

Kuo, Kwo-Sen↗
NASA NTRS1981

Infrared radiative transfer through a regular array of cuboidal clouds

Infrared radiative transfer through a regular array of cuboidal clouds is studied and the interaction of the sides of the clouds with each other and the ground is considered. The theory is developed for black clouds and is extended to scattering clouds using a variable azimuth two-stream approximation. It is shown that geometrical considerations often dominate over the microphysical aspects of radiative transfer through the clouds. For example, the difference in simulated 10 micron brightness temperature between black isothermal cubic clouds and cubic clouds of optical depth 10, is less than 2 deg for zenith angles less than 50 deg for all cloud fractions when viewed parallel to the array. The results show that serious errors are made in flux and cooling rate computations if broken clouds are modeled as planiform. Radiances computed by the usual practice of area-weighting cloudy and clear sky radiances are in error by 2 to 8 K in brightness temperature for cubic clouds over a wide range of cloud fractions and zenith angles. It is also shown that the lapse rate does not markedly affect the exiting radiances for cuboidal clouds of unit aspect ratio and optical depth 10.

HARSHVARDHAN↗