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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

NASA Tech Briefs, February 2010

Topics covered include: Insulation-Testing Cryostat With Lifting Mechanism; Optical Testing of Retroreflectors for Cryogenic Applications; Measuring Cyclic Error in Laser Heterodyne Interferometers; Self-Referencing Hartmann Test for Large-Aperture Telescopes; Measuring a Fiber-Optic Delay Line Using a Mode-Locked Laser; Reconfigurable Hardware for Compressing Hyperspectral Image Data; Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array; High-Speed Ring Bus; Nanoionics-Based Switches for Radio-Frequency Applications; Lunar Dust-Tolerant Electrical Connector; Compact, Reliable EEPROM Controller; Quad-Chip Double-Balanced Frequency Tripler; Ka-Band Waveguide Two-Way Hybrid Combiner for MMIC Amplifiers; Radiation-Hardened Solid-State Drive; Use of Nanofibers to Strengthen Hydrogels of Silica, Other Oxides, and Aerogels; Two Concepts for Deployable Trusses; Concentric Nested Toroidal Inflatable Structures; Investigating Dynamics of Eccentricity in Turbomachines; Improved Low-Temperature Performance of Li-Ion Cells Using New Electrolytes; Integrity Monitoring of Mercury Discharge Lamps; White-Light Phase-Conjugate Mirrors as Distortion Correctors; Biasable, Balanced, Fundamental Submillimeter Monolithic Membrane Mixer; ICER-3D Hyperspectral Image Compression Software; and Context Modeler for Wavelet Compression of Spectral Hyperspectral Images.

Source record↗
NASA NTRSDate not supplied

Compression of Solar Spectroscopic Observations: a Case Study of MgII k Spectral Line Profiles Observed by NASA’s IRIS Satellite

In this study we extract the deep features and investigate the compression of the MgII k spectral line profiles observed in quiet Sun regions by NASA’s IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual MgII k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4DN average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising.

SMD↗
DOE OSTI2022

Calibrating the SPECTACULAR constitutive model using legacy Sandia data for two filled epoxy systems: 828/CTBN/DEA/GMB and 828/DEA/GMB

The SPECTACULAR model is a development extension of the Simplified Potential Energy Clock (SPEC) model. Both models are nonlinear viscoelastic constitutive models used to predict a wide range of time-dependent behaviors in epoxies and other glass-forming materials. This report documents the procedures used to generate SPECTACULAR calibrations for two particulate-filled epoxy systems, 828/CTBN/DEA/GMB and 828/DEA/GMB. No previous SPECTACULAR or SPEC calibration exists for 828/CTBN/DEA/GMB, while a legacy SPEC calibration exists for 828/DEA/GMB. To generate the SPECTACULAR calibrations, a step-by-step procedure was executed to determine parameters in groups with minimal coupling between parameter groups. This procedure has often been deployed to calibrate SPEC, therefore the resulting SPECTACULAR calibration is backwards compatible with SPEC (i.e. none of the extensions specific to SPECTACULAR are used). The calibration procedure used legacy Sandia experimental data stored on the Polymer Properties Database website. The experiments used for calibration included shear master curves, isofrequency temperature sweeps under oscillatory shear, the bulk modulus at room temperature, the thermal strain during a temperature sweep, and compression through yield at multiple temperatures below the glass transition temperature. Overall, the calibrated models fit the experimental data remarkably well. However, the glassy shear modulus varies depending on the experiment used to calibrate it. For instance, the shear master curve, isofrequency temperature sweep under oscillatory shear, and the Young's modulus in glassy compression yield values for the glassy shear modulus at the reference temperature that vary by as much as 15 %. Also, for 828/CTBN/DEA/GMB, the temperature dependence of the glassy shear modulus when fit to the Young's modulus at different temperatures is approximately four times larger than when it is determined from the isofrequency temperature sweep under oscillatory shear. For 828/DEA/GMB, the temperature dependence of the shear modulus determined from the isofrequency temperature sweep under oscillatory shear accurately predicts the Young's modulus at different temperatures. When choosing values for the shear modulus, fitting the glassy compression data was prioritized. The new and legacy calibrations for 828/DEA/GMB are similar and appear to have been calibrated from the same data. However, the new calibration improves the fit to the thermal strain data. In addition to the standard calibrations, development calibrations were produced that take advantage of development features of SPECTACULAR , including an updated equilibrium Helmholtz free energy that eliminates undesirable behavior found in previous work. In addition to the previously mentioned experimental data, the development calibrations require data for the heat capacity during a stress-free temperature sweep to calibrate thermal terms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Genetic algorithm optimization of a chemical kinetic mechanism for propane at engine relevant conditions

Propane has demonstrated significant potential for reductions in greenhouse gas and pollutant emissions in medium- and heavy-duty engine applications, but further improvements require accurate, compact, and scalable chemical kinetic mechanisms to design the next generation of propane fueled engines, particularly at the boosted operating conditions necessary to meet the power density demand of medium- and heavy-duty applications. In this work, six key chemical reactions were identified in a reduced mechanism with 70 species and 352 reactions through a sensitivity analysis performed at conditions typical of thermodynamic trajectories observed in a high compression ratio, long stroke engine operated on propane from throttled to boosted operating conditions. While the original mechanism was validated against rapid compression machine (RCM) data, it was found to overpredict experimental autoignition tendencies in 2-zone, 0-D SI engine simulations performed in Chemkin Pro. Subsequently, a genetic algorithm approach was used to optimize the six reaction rate parameters within established uncertainty bounds by performing RCM simulations and comparing to two independent sets of literature ignition delay times for propane, thus generating two new kinetic mechanisms. The first optimization achieved a mean absolute percent error (MPE) reduction in 2nd stage ignition delay of 61.4% in seven generations, while the second optimization utilized a newer experimental RCM dataset, and achieved MPE reduction of 56.7% in seven generations, and further marginal improvement to 57.8% reduction in 34 generations. Finally, the two mechanisms were then evaluated again in the 2-zone 0-D SI engine model in Chemkin Pro comparing typical mean and knocking cycle trajectories, and it was found that the second optimized mechanism provided better prediction of knock onset at the representative conditions evaluated in this work, particularly for higher load operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advanced Polymer Characterization: Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry encodes structural information across diverse homo- and copolymer ensembles, yet decrypting these spectra requires a systematic analytical approach. We introduce Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)─a general cipher algorithm that applies modular arithmetic to filter monomer-derived mass contributions and cluster MALDI peaks by nonconstitutional repeating units (non-CRUs). MOSAIC performs sequential modular operations using monomer mass differences as base units to compress complex spectral data, revealing end-group distributions and comonomer incorporation. As a demonstration, we applied MOSAIC to five copolymers formed by two different polymerization mechanisms. Furthermore, the resulting remainder–mass plots clearly resolve polymer homologs with distinct non-CRUs into visually apparent clusters, enabling intuitive assignment of mass spectral features.

Wang, Hanlin M. [University of Illinois at Urbana−↗
DOE OSTI2024

Simultaneous compression of NaCl, Au, and ruby: toward mutually consistent pressure scales

Here, we evaluate pressure consistency of equations of state (EOS) for NaCl and Au at 300 K. The simultaneous measurements of unit-cell volumes (V) with ruby R1 line shifts (Δλ) in a helium (He) loaded diamond cell effectively remove potential systematic errors. Compression and decompression data were automatically collected at 1 sec interval, yielding a dense dataset with >8,000 (V, Δλ) pairs each for NaCl and Au. Solidification of He has noticeable effects on both V and Delta lambda (hence P). Only data up to similar to 14 GPa, or 6000 (V, P) pairs, can be considered hydrostatic within the resolution. The P-V data are fitted to the Rydberg-Vinet and the 3 rd order Birch-Murnaghan EOS. Predicted pressures of these EOSs agree with those given by the Ruby2020 ruby scale to within +/- 0.05 GPa. Overall, pressures predicted by the Rydberg-Vinet EOS are in better agreement with the average of commonly used NaCl and Au pressure scales.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

In situ synchrotron diffraction of pressure-induced phase transition in DyPO 4 under variable hydrostaticity

In situ synchrotron x-ray diffraction was conducted on polycrystalline DyPO 4 to elucidate the details of the pressure-induced transition from the xenotime polymorph to the monazite polymorph. We used three different pressure-transmitting media (neon, a 16:3:1 methanol-ethanol-water mixture, and potassium chloride) to investigate the effect of hydrostaticity on the phase behavior. Specifically, our data clearly show a hydrostatic onset pressure of the xenotime-monazite transition of 9.1 GPa, considerably lower than the 15.3 GPa previously determined by Raman spectroscopy. Based on (quasi)hydrostatic data taken in a neon environment, third-order Birch-Murnaghan equation-of-state fits give a xenotime bulk modulus of 144 GPa and a monazite bulk modulus of 180 GPa (both with pressure derivatives of 4.0). Structural data and axial compressibilities show that DyPO 4 is sensitive to shear and has an anisotropic response to pressure. More highly deviatoric conditions cause the onset of the transition to shift to pressures at least as low as 7.0 GPa. We attribute early transition to shear-induced distortion of the PO4 tetrahedra. Finally, our characterization of the high-pressure behavior of DyPO 4 under variable hydrostaticity is critical for advancing rare earth orthophosphate fiber coating applications in ceramic matrix composites and may inform future tailoring of phase composition for controlled shear and pressure applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Model-independent versus model-dependent interpretation of the SDSS-III BOSS power spectrum: Bridging the divide

The traditional clustering analyses of galaxy redshift surveys compress the clustering data into a set of late-time physical variables in a model-independent way. This approach has recently been extended by an additional shape variable encoding early-time physics information. Here we apply this new technique, ShapeFit, to SDSS-III BOSS data and show that it matches the constraining power of alternative, model-dependent approaches, which directly constrain the model’s parameters adopting a cosmological model ab initio. ShapeFit is ~30 times faster, model independent, naturally splits early- and late-time variables, and enables a better control of observational systematics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control

In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global metrics on the dataset. To improve on these two aspects, we propose a method that aims to divide the original dataset into blocks based on their smoothness and refactors each block separately with the most relevant interpolation order. We show the relevance of such a method on tailored datasets and the benefits and challenges when applying it to large scientific data.

Vidal, Nicolas [ORNL]↗
DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗
DOE OSTI2026

Scientific Discovery with Physics-Informed System Identification (Abbreviated Report)

My fellowship research focused on making physics-based simulations faster and more useful through machine learning. Many problems in science and engineering are governed by partial differential equations, but high-fidelity simulations are often too expensive to run repeatedly. I worked on improving Latent Space Dynamics Identification (LaSDI), a reduced-order modeling framework that compresses large simulation data sets into a smaller representation and then learns how that representation evolves over time. The motivation was to develop reduced models that remain accurate for more challenging systems, especially when predictions must remain reliable over long time intervals or when the underlying dynamics are more complicated than standard methods can easily handle. I also contributed to related work on Quandary, a high-performance software effort for simulation and control of open quantum systems, before focusing primarily on Latent Space Dynamics Identification methods. The main outcomes of the fellowship were two new algorithms (both of which were published), Rollout-LaSDI and Higher-Order LaSDI, together with supporting work on multi-stage Latent Space Dynamics Identification. Rollout-LaSDI improved long-term prediction by training the model to stay accurate over extended time horizons, and Higher-Order LaSDI broadened the method so it could model systems with higher-order time dynamics. My contributions to multistage Latent Space Dynamics Identification also helped show that its later training stages could be simplified without losing effectiveness, and that this behavior held across different model architectures and training strategies. Taken together, these advances improved the accuracy, flexibility, and practical value of reduced-order modeling tools for computational science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Full Spectrum, 350 - 2500 nm, Leaf and Canopy Spectral Reflectance, Seward Peninsula, Alaska, 2017

Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council within the larger Seward Peninsula, Alaska region. The data were collected in the months of July and August of 2017 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). This data package includes 26 files in a variety of formats including processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA110_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Full spectrum (350-2500 nm) canopy spectral reflectance, Seward Peninsula, Alaska, 2018

Full-range (350-2500 nm) canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council, as well as the supplemental Kougarok Mile 80 site within the larger Seward Peninsula, Alaska region. The data were collected in July 2018 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). Species included in the spectra and functional trait dataset include Alnus viridis, Betula nana, Betula glandulosa, Arctous alpina, and Salix pulchra. This data package includes 35 files in a variety of formats including processed canopy spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA208_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Full spectrum (350-2500 nm) leaf and canopy spectral reflectance, Seward Peninsula, Alaska, 2019

Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra. These data were collected in support of ongoing NASA ABoVE AVIRIS data synthesis work. The datasets were collected at the NGEE-Arctic watershed sites, Kougarok MM64 and MM80, and Teller MM27 on the Seward Peninsula, Alaska. The data were collected in July 2019 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see leaf trait data package for these data). Canopy spectra were obtained for 23 different species, and leaf spectra for 8 species. This data package includes processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA212_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NLR HPC Eagle Node Power Data

Power time series captured from all Eagle nodes using iLO (Integrated Lights Out) The Eagle HPC operated at NLR from 2019 through 2024. Eagle was a 2,000-node, 8-petaflop system. This dataset is a comprehensive time series of instantaneous snapshots of power usage at 1 minute intervals from all nodes at the node level. Data provided in compressed Hive dataset/Parquet format. iLO Power Time Series Fields ts: Timestamp dv: Device / Node - Rack and Unit - r103u17 == r(ack)103u(nit)17 vl: Value - Value in watts (instantaneous value at sampling time) day month year

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

NLR HPC Eagle GPU Node Metrics

Ganglia node metrics and iLO (Integrated Lights Out) power data captured from six representative Eagle GPU nodes The Eagle HPC operated at NLR from 2019 through 2024. Eagle was a 2,000-node, 8-petaflop system. This dataset is a representative sample of metrics for 6 of the GPU nodes. Each GPU node contained 2 CPUs and 2 GPUs. Data provided in compressed CSV format. Ganglia and iLO Power Time Series Fields ts: Timestamp dv: Device / Node - Rack and Unit - r103u17 == r(ack)103u(nit)17 mt: Metric (only present for Ganglia) vl: Value - Value in watts for iLO power (instantaneous value at sampling time) or specified Ganglia metric below Ganglia Metrics Metric name -- Metric description -- Unit cpu_aidle -- Percent of time since boot idle CPU -- Percent cpu_idle -- Percent CPU idle -- Percent cpu_nice -- Percent CPU nice -- Percent cpu_speed -- Speed in MHz of CPU -- MHz cpu_user -- Percent CPU user -- Percent cpu_wio -- The percentage of CPU Wait I/O -- Percent gpu0_bar1_memory -- Used GPU bar1 memory -- MB gpu0_decoder_util -- GPU decoder utilization -- Percent gpu0_ecc_db_error -- Total ECC error counts for the GPU -- Number gpu0_encoder_util -- GPU encoder utilization -- Percent gpu0_fan -- Fan speed -- RPM gpu0_fb_memory -- Used GPU framebuffer memory -- MB gpu0_graphics_clock_report -- Current clock speeds for the device -- MHz gpu0_mem_total -- Memory total -- MB gpu0_mem_util -- Memory utilization -- Percent gpu0_power_usage_report -- Power usage report -- Watts gpu0_temp -- GPU 1 temperature -- Celsius gpu1_bar1_memory -- Used GPU bar1 memory -- MB gpu1_decoder_util -- GPU decoder utilization -- Percent gpu1_ecc_db_error -- Total ECC error counts for the GPU -- Number gpu1_encoder_util -- GPU encoder utilization -- Percent gpu1_fan -- Fan speed -- RPM gpu1_fb_memory -- Used GPU framebuffer memory -- MB gpu1_graphics_clock_report -- Current clock speeds for the GPU -- MHz gpu1_mem_total -- Memory total -- MB gpu1_mem_util -- Memory utilization -- MB gpu1_power_usage_report -- Power usage report -- Watts gpu1_temp -- GPU 1 temperature -- Celsius ipmi_cpu1_temp -- CPU 1 temperature -- Celsius ipmi_cpu2_temp -- CPU 2 temperature -- Celsius ipmi_inlet_ambient_temp -- Temperature measured at intake -- Celsius ipmi_vr_p1_temp -- CPU 1 voltage regulator temperature -- Celsius ipmi_vr_p2_temp -- CPU 2 voltage regulator temperature -- Celsius mem_buffers -- Amount of buffered memory -- Bytes mem_cached -- Amount of cached memory -- Bytes mem_free -- Amount of available memory -- Bytes mem_shared -- Amount of shared memory -- Bytes mem_total -- Amount of available memory -- Bytes

97 MATHEMATICS AND COMPUTING↗