SEARCH · Search NASA

Results for “Experience replay”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

25 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS1993

Progressive retry for software error recovery in distributed systems

In this paper, we describe a method of execution retry for bypassing software errors based on checkpointing, rollback, message reordering and replaying. We demonstrate how rollback techniques, previously developed for transient hardware failure recovery, can also be used to recover from software faults by exploiting message reordering to bypass software errors. Our approach intentionally increases the degree of nondeterminism and the scope of rollback when a previous retry fails. Examples from our experience with telecommunications software systems illustrate the benefits of the scheme.

Wang, Yi-Min↗

NASA NTRS2018

Impacts of Land Carbon Flux Variability on Variations in Atmospheric CO2

To better understand the role of atmospheric CO2 in the global carbon cycle, it is important to understand the factors that control its variability in time and space. Here we investigate these factors using a coupled land-atmosphere modeling system (the NASA GEOS-5 model) fitted with land carbon physics and atmospheric CO2 transport. To separate the influences of land carbon flux variability and atmospheric transport variability on the variability of CO2 concentrations, we conducted two carbon-fitted GEOS-5 AGCM simulations run in replay mode (a technique that guides the model's weather to match that of the MERRA-2 reanalysis): (i) a control simulation of 15-year carbon cycle dynamics and climate, and (ii) a simulation in which the climatological seasonal cycles of net biosphere production (NBP), as determined from diagnostics produced in the control experiment, are applied at the surface to the atmosphere instead. The impact of land flux variability on atmospheric CO2 variability is then isolated by subtracting the variability inherent in the second simulation (as induced, e.g., by strong El Nino conditions) from that in the control. These results are also presented in the context of supplemental simulations that have examined the impact of imposed drought in a free running AGCM simulation on carbon fluxes and atmospheric CO2 variability.

Lee, Eunjee↗

NASA NTRS1980

Instant-replay data system for combustion tests

A portable, inexpensive, easily programmed, and flexible pressure measuring and display system is described and evaluated in a multi-port combustion model. The data reduction system consists of a desk-top graphics display system and calculator with data tape storage and standard data communications interfacing capabilities, while a hard copy unit provides a permanent record of all plots and tables. The system offers the advantages of recording and comparing a large number of pressures at high data rates along with the display of the results in a meaningful graphical form for near real time analysis. Some discussion of combustor and engine experiments are included as examples of the data system application.

Trexler, C. A.↗

NASA NTRS2002

STS 110 Mission Highlights Resource Tape

A continuation of 'STS 110 Mission Highlights Resource Tape'. This video, Part 2 of 4, shows footage from flight days 3 through 5 of STS-110. The flight crew includes Michael J. Bloomfield, Commander; Stephen N. Frick, Pilot; Jerry L. Ross, Mission Specialist; Steven L. Smith, Mission Specialist; Ellen Ochoa, Mission Specialist; Lee M.E. Morin, Mission Specialist; Rex J. Walheim, Mission Specialist. The coverage from flight day 3 includes docking replays of Atlantis and the International Space Station (ISS), and postdocking procedures, as well as intermingling of the flight crew with the Expedition 4 crew (Yury I. Onufrienko, Commander; Daniel W. Bursch, Flight Engineer; Carl E. Walz, Flight Engineer) of the ISS. Flight day 4 includes an EVA (extravehicular activity) in which Walheim and Smith lift the S0 Truss from the payload bay, and temporarily clamp it onto the Destiny laboratory. On flight day 5 a suite of spaceborne experiments (not shown) arrives at Destiny, including protein crystal growth and wheat plant growth experiments. Notable footage includes Hawaii, New Zealand, and sunrise on Atlantis. An unknown object steaks across the field of view during the video, with the Earth in the background. The activities of the other flight days can be seen on 'STS 110 Mission Highlights Resource Tape, Part 1 of 4' (internal ID 2002137575), 'STS 110 Mission Highlights Resource Tape, Part 3 of 4' (internal ID 2002137574), and 'STS 110 Mission Highlights Resource Tape, Part 4 of 4' (internal ID 2002137517).

Source record↗

NASA NTRSDate not supplied

Extreme Precipitation in the Southern US Great Plains in the Spring of 2015: Mechanisms and Prediction

During May of 2015, the southern US Great Plains and adjacent Gulf Coast region experienced more than twice the long-term mean precipitation, making it the wettest May since 1895. We investigate the physical mechanisms associated with this event using a suite of large-ensemble regional replay AGCM simulations from the NASA-GEOS model. In these simulations, certain regions of the globe are constrained to closely follow observations while the remainder of the domain is free running, allowing for the isolation of the remote regions that were important for the event. Preliminary analysis provides evidence that the extreme southern US precipitation was linked in part to positive precipitation anomalies in the central and eastern tropical Pacific via a wave train, which ultimately caused anomalous moisture flux from the Gulf of Mexico. An analysis of Subseasonal Experiment (SubX) model output was conducted to explore the subseasonal prediction skill of the event. Several models are able the predict the presence of positive precipitation anomalies in or near the southern US at lead times exceeding 10 days, albeit with errors in the locations and magnitude of the heaviest precipitation anomalies. A more thorough investigation with version 2 of NASA’s GEOS-S2S model shows that the prediction skill stems from the model’s ability to reasonably predict the positive tropical Pacific precipitation anomalies and the initiation of the Rossby wave train that is believed to be linked to the event. The potential causes for limitations in the prediction skill of this event will be explored.

Great Plains↗

DOE OSTI2020

Logic in Memory Emulator

Logic in Memory Emulator (LiME) is a hardware/software tool specially designed for memory system evaluation and experiment. Emerging memories display a wide range of bandwidths, latencies, and capacities, making it challenging for the computer architect to navigate the design space of potential memory configurations, and for the application developer to assess performance implications of using such memories. With the LiME framework, architectural ideas can be prototyped in great detail yet with sufficient performance to support realistic evaluation on long running applications. LiME consists of two fundamental components: 1) the hardware and OS infrastructure for the emulator, and 2) a suite of benchmark applications to assist in characterizing the performance of current and future computer architectures. Some of the applications have been collected from other open source projects. Uses: Logging, replay and analysis of an application's memory behavior Evaluate impact of emerging memory technology on application performance. Emulate complex memory interactions in whole applications orders of magnitude faster than software simulation. Emulate acceleration hardware co-located with the memory subsystem. Features: Capture and log external memory accesses to a separate off-chip memory device without affecting application execution. Memory traces include the address, length, timestamp, and optionally the data for each transaction. Captured trace data can be saved to an SD card for off-line analysis. Configure a wide range of memory latencies in sub-nanosecond increments that encompass highbandwidth and storage class memories. Specify regions of interest (ROI) in applications to reduce the amount of trace data captured for analysis. Currently supports execution on Xilinx Zynq SoC which integrates an ARM processor with FPGA logic on a single device. Applications can be run under Linux or in bare metal mode on the ARM cores.

Jain, AbhishekK↗

DOE OSTI2024

The CXSFIT spectral fitting code: Past, present and future

Magnetically confined plasma experiments generate a wealth of spectroscopic data. The first step toward extracting physical parameters is to fit a spectral model to the often complex spectra. The CXSFIT (Charge eXchange Spectroscopy FITting) spectral fitting code was originally developed for fitting charge exchange spectra on JET from the late 1980s onward and has been further developed over decades to keep up with the needs of the users. The primary use is to efficiently fit a large number of spectra with many constrained Gaussian spectral lines of which the physical parameters can be coupled in a user-friendly manner. More recent additions to the code include time-dependent couplings between parameters, flexible background subtraction, and a non-linear coupling scheme between fit parameters. The latter was a pre-requisite for implementing Zeeman and motional Stark effect multiplets in the library of spectral features. The ability to save and replay “fit recipes,” even when multiple iterations are required, has ensured the traceability of the results and is one of the keys to the longevity and success of the code. The code is also in use on other tokamaks (AUG, ST-40) and to fit data from other spectroscopic diagnostics on JET. In this paper, we document the current capabilities and philosophy behind the structure of the code, including some of the algorithms used to calculate spectral features numerically efficiently. We also provide an outline of how CXSFIT could be transferred into a framework that would be able to meet the spectral fitting requirements of future devices, such as ITER.

Delabie, Ephrem G.↗