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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

SANs and Large Scale Data Migration at the NASA Center for Computational Sciences

Evolution and migration are a way of life for provisioners of high-performance mass storage systems that serve high-end computers used by climate and Earth and space science researchers: the compute engines come and go, but the data remains. At the NASA Center for Computational Sciences (NCCS), disk and tape SANs are deployed to provide high-speed I/O for the compute engines and the hierarchical storage management systems. Along with gigabit Ethernet, they also enable the NCCS's latest significant migration: the transparent transfer of 300 Til3 of legacy HSM data into the new Sun SAM-QFS cluster.

Salmon, Ellen M.↗
NASA NTRS2004

Real-Time Very High-Resolution Regional 4D Assimilation in Supporting CRYSTAL-FACE Experiment

To better understand tropical cirrus cloud physical properties and formation processes with a view toward the successful modeling of the Earth's climate, the CRYSTAL-FACE (Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment) field experiment took place over southern Florida from 1 July to 29 July 2002. During the entire field campaign, a very high-resolution numerical weather prediction (NWP) and assimilation system was performed in support of the mission with supercomputing resources provided by NASA Center for Computational Sciences (NCCS). By using NOAA NCEP Eta forecast for boundary conditions and as a first guess for initial conditions assimilated with all available observations, two nested 15/3 km grids are employed over the CRYSTAL-FACE experiment area. The 15-km grid covers the southeast US domain, and is run two times daily for a 36-hour forecast starting at 0000 UTC and 1200 UTC. The nested 3-km grid covering only southern Florida is used for 9-hour and 18-hour forecasts starting at 1500 and 0600 UTC, respectively. The forecasting system provided more accurate and higher spatial and temporal resolution forecasts of 4-D atmospheric fields over the experiment area than available from standard weather forecast models. These forecasts were essential for flight planning during both the afternoon prior to a flight day and the morning of a flight day. The forecasts were used to help decide takeoff times and the most optimal flight areas for accomplishing the mission objectives. See more detailed products on the web site http://asd-www.larc.nasa.gov/mode/crystal. The model/assimilation output gridded data are archived on the NASA Center for Computational Sciences (NCCS) UniTree system in the HDF format at 30-min intervals for real-time forecasts or 5-min intervals for the post-mission case studies. Particularly, the data set includes the 3-D cloud fields (cloud liquid water, rain water, cloud ice, snow and graupe/hail).

Wang, Donghai↗
NASA NTRS2004

Data Management as a Cluster Middleware Centerpiece

Through earth and space modeling and the ongoing launches of satellites to gather data, NASA has become one of the largest producers of data in the world. These large data sets necessitated the creation of a Data Management System (DMS) to assist both the users and the administrators of the data. Halcyon Systems Inc. was contracted by the NASA Center for Computational Sciences (NCCS) to produce a Data Management System. The prototype of the DMS was produced by Halcyon Systems Inc. (Halcyon) for the Global Modeling and Assimilation Office (GMAO). The system, which was implemented and deployed within a relatively short period of time, has proven to be highly reliable and deployable. Following the prototype deployment, Halcyon was contacted by the NCCS to produce a production DMS version for their user community. The system is composed of several existing open source or government-sponsored components such as the San Diego Supercomputer Center s (SDSC) Storage Resource Broker (SRB), the Distributed Oceanographic Data System (DODS), and other components. Since Data Management is one of the foremost problems in cluster computing, the final package not only extends its capabilities as a Data Management System, but also to a cluster management system. This Cluster/Data Management System (CDMS) can be envisioned as the integration of existing packages.

Zero, Jose↗
NASA NTRS2013

Hadoop for High-Performance Climate Analytics: Use Cases and Lessons Learned

Scientific data services are a critical aspect of the NASA Center for Climate Simulations mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyber infrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model inter-comparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.

analytics↗
NASA NTRS2018

Evolving Storage and Cyber Infrastructure at the NASA Center for Climate Simulation

This talk will describe recent developments at the NASA Center for Climate Simulation, which is funded by NASAs Science Mission Directorate, and supports the specialized data storage and computational needs of weather, ocean, and climate researchers, as well as astrophysicists, heliophysicists, and planetary scientists. To meet requirements for higher-resolution, higher-fidelity simulations, the NCCS augments its High Performance Computing (HPC) and storage retrieval environment. As the petabytes of model and observational data grow, the NCCS is broadening data services offerings and deploying and expanding virtualization resources for high performance analytics.

Cyberinfrastructure↗
NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗
NASA NTRS2018

File Specification for M2AMIP Products: Version Number - 1.0

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis computed with the Goddard Earth Observing (EOS) System, Version 5.12.4 (GEOS) data assimilation system (Gelaro et al., 2017). To supplement the reanalysis, the GEOS General Circulation Model (GCM) used in MERRA-2 has been used to generate a 10-member ensemble of simulations, configured following the convention of the Atmospheric Model Intercomparison Project (AMIP; Gates et al., 1992). Each ensemble member was initialized using meteorological fields from a different date in November 1979. The AMIP simulations used the sea-surface temperature (SST) and sea-ice boundary conditions that were used in MERRA-2 (Bosilovich et al., 2016). This 10-member ensemble of AMIP simulations, denoted M2AMIP, is available for download in a group of self-describing files, which are documented in this office note. All data collections are provided on the same horizontal grid as MERRA-2. This grid has 576 points in the longitudinal direction and 361 points in the latitudinal direction, corresponding to a resolution of 0.625 degrees by 0.5 degrees. Although data collections are available at this grid, all fields are computed on a cubed-sphere grid with an approximate resolution of 50 km by 50 km and are then spatially interpolated to the latitude-longitude grid. There are no changes in the vertical grids used: variables are provided on either the native vertical grid of 72 model layers, or interpolated to 42 standard pressure levels. Unlike MERRA, no data collections are available at the vertical layer edges. More details on the grid are provided in Section 4. MERRA-2 introduced observation-based precipitation forcing for the land surface parameterization and the corresponding variable PRECTOTCORR in the MERRA-2 FLX (surface turbulent fluxes and related quantities) and LFO (land-surface forcing) collections (see Section 6; Reichle et al., 2017). While this variable is still available for M2AMIP, there was no observation-based forcing, making the value identical to the model derived precipitation, PRECTOT. Similarly, without data assimilation, the values for the analysis increments, D*DTANA, in the tendency and vertically integrated file collections are zero. The M2AMIP data are available for download online through the NASA Center for Climate Simulation (NCCS) DataPortal (https://portal.nccs.nasa.gov/datashare/gmao_m2amip/). Data are arranged in subdirectories based on ensemble member, followed by year and month. Control files that are compatible with the Grid Analysis and Display System (GrADS) are available in the ctl_daily and ctl_monthly directories for the hourly, three hourly, and monthly mean data. Control files for the monthly mean diurnal cycle can be found in the ctl_diurnal subdirectory within the directory for each individual ensemble member.

M2AMIP↗
NASA NTRS1998

Chinks in Solar Dynamo Theory: Turbulent Diffusion, Dynamo Waves and Magnetic Helicity

In this first year of our investigation we explored the role of compressibility and stratification in the dissipation of magnetic fields. The predictions of Mean Field Electrodynamics have been questioned because of the strong feedback of small scale magnetic structure on the velocity fields. In 2-D, this nonlinear feedback results in a lengthening of the turbulent decay time. In 3-D alpha-quenching is predicted. Previous studies assumed a homogeneous fluid. This first year we present recent results from 2-D compressible MHD decay simulations in a highly stratified atmosphere that more closely resembles to solar convection zone. We have applied for NCCS T3E time to assist in the performance of our 3-D calculations.

DeLuca, E. E.↗
NASA NTRS2002

Low-Level Jets: The Data Assimilation Office and Reanalysis

Data assimilation brings together atmospheric observations and atmospheric models-what we can measure of the atmosphere with how we expect it to behave. NASA's Data Assimilation Office (DAO) sponsors research projects in data reanalysis, which take several years of observational data and analyze them with a fixed assimilation system, to create an improved data set for use in atmospheric studies. Using NCCS computers, one group of NASA researchers employs reanalysis to examine the role of summertime low-level jet (LLJ) winds in regional seasonal climate. Prevailing winds that blow strongly in a fixed direction within a vertically and horizontally confined region of the atmosphere are known as jets. Jets can dominate circulation and have an enormous impact on the weather in a region. Some jets are as famous as they are influential. The jet stream over North America, for instance, is the wind that blows eastward across the continent, bringing weather from the west coast and increasing the speed of airplanes flying to the east coast. The jet stream, while varying in intensity and location, is present in all seasons at the very high altitude of 200-300 millibars - more than 6 miles above Earth's surface.

Source record↗
NASA NTRS2000

The Finer Details: Climate Modeling

If you want to know whether you will need sunscreen or an umbrella for tomorrow's picnic, you can simply read the local weather report. However, if you are calculating the impact of gas combustion on global temperatures, or anticipating next year's rainfall levels to set water conservation policy, you must conduct a more comprehensive investigation. Such complex matters require long-range modeling techniques that predict broad trends in climate development rather than day-to-day details. Climate models are built from equations that calculate the progression of weather-related conditions over time. Based on the laws of physics, climate model equations have been developed to predict a number of environmental factors, for example: 1. Amount of solar radiation that hits the Earth. 2. Varying proportions of gases that make up the air. 3. Temperature at the Earth's surface. 4. Circulation of ocean and wind currents. 5. Development of cloud cover. Numerical modeling of the climate can improve our understanding of both the past and, the future. A model can confirm the accuracy of environmental measurements taken. in, the past and can even fill in gaps in those records. In addition, by quantifying the relationship between different aspects of climate, scientists can estimate how a future change in one aspect may alter the rest of the world. For example, could an increase in the temperature of the Pacific Ocean somehow set off a drought on the other side of the world? A computer simulation could lead to an answer for this and other questions. Quantifying the chaotic, nonlinear activities that shape our climate is no easy matter. You cannot run these simulations on your desktop computer and expect results by the time you have finished checking your morning e-mail. Efficient and accurate climate modeling requires powerful computers that can process billions of mathematical calculations in a single second. The NCCS exists to provide this degree of vast computing capability.

Source record↗
NASA NTRS2000

Burning in Outer Space: Microgravity

A better understanding of combustion can lead to significant technological advances, such as less polluting, more fuel-efficient vehicles. Unfortunately, gravity can interfere with the study of combustion. Gravity drags down gases that are cooler- and, therefore, denser-than heated gases. This movement mixes the fuel and the oxidizer substance that promotes burning. Because of this mixing, an observer cannot necessarily distinguish what is happening as a result of the natural combustion process and what is caused, by the pull of gravity. To remove this uncertainty, scientists can conduct experiments that simulate the negation of gravity through freefall. This condition is known as a microgravity environment. A micro-gravity experiment may take place in a chamber that is dropped down a hole or from a high-speed drop tower. The experiment also be conducted in an airplane or a rocket during freefall in a parabolic flight path. This method provides less than a minute of microgravity at most. An experiment that requires the prolonged absence of gravity may necessitate the use of an orbiting spacecraft as a venue. However, access to an orbital laboratory is difficult to acquire. High-end computing centers such as the NCCS can provide a practical alternative to operating in microgravity. Scientists can model phenomena such as combustion without gravitys observational interference. The study of microgravity combustion produces important benefits beyond increased observational accuracy. Certain valuable materials that are produced through combustion can be formed with a more uniform crystal structure-and, therefore, improved structural quality-when the pull of gravity is removed. Furthermore, understanding how fires propagate in the absence of gravity can improve fire safety aboard spacecraft.

Matkowsky, Bernard↗
NASA NTRS2000

NASA Center for Computational Sciences: History and Resources

The Nasa Center for Computational Sciences (NCCS) has been a leading capacity computing facility, providing a production environment and support resources to address the challenges facing the Earth and space sciences research community.

Source record↗
NASA NTRS2004

Hierarchical Storage Management at the NASA Center for Computational Sciences: From UniTree to SAM-QFS

This paper presents the data management issues associated with a large center like the NCCS and how these issues are addressed. More specifically, the focus of this paper is on the recent transition from a legacy UniTree (Legato) system to a SAM-QFS (Sun) system. Therefore, this paper will describe the motivations, from both a hardware and software perspective, for migrating from one system to another. Coupled with the migration from UniTree into SAM-QFS, the complete mass storage environment was upgraded to provide high availability, redundancy, and enhanced performance. This paper will describe the resulting solution and lessons learned throughout the migration process.

Salmon, Ellen↗
NASA NTRS2010

Discontinuous Inter-Granular Separations (DIGS) in the Gas Nitride Layer of ISS Race Rings

The starboard solar alpha rotary joint (SARJ) race ring on the International space station (ISS) failed due to severe spalling of the outer diameter, 45 degree (outer canted) nitrided surface. Subsequent analysis at NASA-KSC revealed that almost all of the debris generated due to the failure was nitrided 15-5 stainless steel. Subsequent analysis of the nitride control coupons (NCC) at NASA-JSC revealed the presence of discontinuous inter-granular separations (DIGS) in the gas nitride layer. These DIGS were present in the inter-granular networking located in the top 2 mils of the nitride layer. The manufacturer's specification requires the maximum white structure to be 0.0003 inches and intergranular networking below the allowable white structure depth to be cause for rejection; a requirement that the NCCs did not meet. Subsequent testing and analysis revealed that lower DIGS content significantly lowered the probability of nitride spalling in simulated, dry condition runs. One batch of nitride samples with DIGS content similar to the port SARJ (did not fail on orbit) which exhibited almost no nitride spalling after being run on one test rig. Another batch of nitride samples with DIGS content levels similar to the starboard SARJ exhibited significant nitride spalling on the same test rig with the same load under dry conditions. Although DIGS were not the root cause of starboard race ring failure, testing indicates that increased DIGS reduced the robustness of the gas nitride layer under dry operating conditions.

Figert, John↗
NASA NTRS2011

Graphics Processing Unit (GPU) Acceleration of the Goddard Earth Observing System Atmospheric Model

The Goddard Earth Observing System 5 (GEOS-5) is the atmospheric model used by the Global Modeling and Assimilation Office (GMAO) for a variety of applications, from long-term climate prediction at relatively coarse resolution, to data assimilation and numerical weather prediction, to very high-resolution cloud-resolving simulations. GEOS-5 is being ported to a graphics processing unit (GPU) cluster at the NASA Center for Climate Simulation (NCCS). By utilizing GPU co-processor technology, we expect to increase the throughput of GEOS-5 by at least an order of magnitude, and accelerate the process of scientific exploration across all scales of global modeling, including: The large-scale, high-end application of non-hydrostatic, global, cloud-resolving modeling at 10- to I-kilometer (km) global resolutions Intermediate-resolution seasonal climate and weather prediction at 50- to 25-km on small clusters of GPUs Long-range, coarse-resolution climate modeling, enabled on a small box of GPUs for the individual researcher After being ported to the GPU cluster, the primary physics components and the dynamical core of GEOS-5 have demonstrated a potential speedup of 15-40 times over conventional processor cores. Performance improvements of this magnitude reduce the required scalability of 1-km, global, cloud-resolving models from an unfathomable 6 million cores to an attainable 200,000 GPU-enabled cores.

Putnam, Williama↗
NASA NTRS2012

The Virtual Climate Data Server (vCDS): An iRODS-Based Data Management Software Appliance Supporting Climate Data Services and Virtualization-as-a-Service in the NASA Center for Climate Simulation

Scientific data services are becoming an important part of the NASA Center for Climate Simulation's mission. Our technological response to this expanding role is built around the concept of a Virtual Climate Data Server (vCDS), repetitive provisioning, image-based deployment and distribution, and virtualization-as-a-service. The vCDS is an iRODS-based data server specialized to the needs of a particular data-centric application. We use RPM scripts to build vCDS images in our local computing environment, our local Virtual Machine Environment, NASA s Nebula Cloud Services, and Amazon's Elastic Compute Cloud. Once provisioned into one or more of these virtualized resource classes, vCDSs can use iRODS s federation capabilities to create an integrated ecosystem of managed collections that is scalable and adaptable to changing resource requirements. This approach enables platform- or software-asa- service deployment of vCDS and allows the NCCS to offer virtualization-as-a-service: a capacity to respond in an agile way to new customer requests for data services.

Schnase, John L.↗
NASA NTRS2015

GEOS-5 Chemistry Transport Model User's Guide

The Goddard Earth Observing System version 5 (GEOS-5) General Circulation Model (GCM) makes use of the Earth System Modeling Framework (ESMF) to enable model configurations with many functions. One of the options of the GEOS-5 GCM is the GEOS-5 Chemistry Transport Model (GEOS-5 CTM), which is an offline simulation of chemistry and constituent transport driven by a specified meteorology and other model output fields. This document describes the basic components of the GEOS-5 CTM, and is a user's guide on to how to obtain and run simulations on the NCCS Discover platform. In addition, we provide information on how to change the model configuration input files to meet users' needs.

GCM↗