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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.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

AMRZone: A Runtime AMR Data Sharing Framework For Scientific Applications

Frameworks that facilitate runtime data sharing across multiple applications are of great importance for scientific data analytics. Although existing frameworks work well over uniform mesh data, they can not effectively handle adaptive mesh refinement (AMR) data. Among the challenges to construct an AMR-capable framework include: (1) designing an architecture that facilitates online AMR data management; (2) achieving a load-balanced AMR data distribution for the data staging space at runtime; and (3) building an effective online index to support the unique spatial data retrieval requirements for AMR data. Towards addressing these challenges to support runtime AMR data sharing across scientific applications, we present the AMRZone framework. Experiments over real-world AMR datasets demonstrate AMRZone's effectiveness at achieving a balanced workload distribution, reading/writing large-scale datasets with thousands of parallel processes, and satisfying queries with spatial constraints. Moreover, AMRZone's performance and scalability are even comparable with existing state-of-the-art work when tested over uniform mesh data with up to 16384 cores; in the best case, our framework achieves a 46% performance improvement.

Zhang, Wenzhao↗
DOE OSTI2019

Cybersecurity Vulnerability Mitigation Framework through Empirical Paradigm (CyFEr): Prioritized Gap Analysis

Cybersecurity vulnerability assessment tools, frameworks, methodologies, and processes are commonly used to understand the cybersecurity maturity and posture of a system or a facility. Although, those tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy, the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we have been developing a framework and a software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). The efficacy of CyFEr was evaluated by implementing it on the NIST Cybersecurity Framework (CSF). This paper provides a detailed architecture of CyFEr and demonstrates its application to CSF by testing against a real-world cyber-attack that targeted industrial control systems in a critical infrastructure facility.

Gourisetti, Sri Nikhil G.↗
DOE OSTI2022

Emulation Framework for Distributed Large-Scale Systems Integration

Recent trends in systems engineering include integration of very large-scale systems, which entails significant challenges when they are geographically dispersed. In these scenarios, intelligent integration of distributed large-scale systems requires significant coordination among hardware elements as well as all software components. The approach of integrated systems (both computing platform and experimental equipment) for end-to-end orchestration is called federation. Virtual frameworks can aid in the testing, assessment, and implementation of a functional system of interconnected resources. We present an emulation framework that replicates the software environments of multi-site federations of computing systems and instruments. Our emulation framework allows systems engineers to reduce developmentcost and avoid disruptions to production infrastructure. Our framework was effectively used to develop and test software modules for various tasks including container orchestration and instrument access. For performance assessment, however, the emulated framework is severely limited in providing accurate network and IO measurements at 10 Gbps and higher data rates. The data transfer performance profiles estimated using these emulated measurements are usually inaccurate for high bandwidth and high latency connections, since emulation does not accurately reflect the critical network transport dynamics.We utilize measurements from a physical testbed with hardware network emulators to obtain data transfer profiles that closely match the expected profiles for the emulated federations. We show the effectiveness of our approach by an illustrative example of integrated (federated) multi-site ultra large-scale systems that are connected via high speed wide area networks.

Imam, Neena↗
DOE OSTI2023

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

To take unit commitment (UC) decisions under uncertain load, most existing stochastic optimization (SO) frameworks adopt a generic representation of uncertainty. While load levels that materialize on a particular day are influenced by various covariates (such as the day of the week or temperature), SO frameworks typically disregard such side observations, wasting actionable information that could significantly enhance decision quality. Here, this article proposes a contextual SO (CSO) framework for UC under uncertain load, which can effectively exploit covariate observations in conjunction with a class of machine learning (ML) algorithms to improve the out-of-sample performance of UC decisions. It shows how three ML algorithms, adaptive boosting, gradient boosted trees, and extreme gradient boosting, can be used to this end, constituting the first application of these algorithms in any CSO framework. Using real-world data harvested from the New York ISO grid, we measure the out-of-sample performance of the framework in terms of total operation cost, shed load values, locational marginal prices, and total payments by the loads, against several benchmark methods proposed in the literature. The article has an online companion (Yurdakul et al.), wherein we present additional results and lay out further mathematical formulations used in this work.

42 ENGINEERING↗
DOE OSTI2026

A Comprehensive Calibration Framework for the Northwest River Forecast Center

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in under 10 min. We found that model performance metrics for this new approach match the quality of the NWRFC's previous labor-intensive manual calibrations. The model performance also rivals that of a state-of-the-art deep learning model at a fraction of the computational cost. This framework presents a new standard for the quality of calibrations possible with lumped conceptual hydrologic models, combining careful data curation, an objective calibration framework, and expert local knowledge. In addition, we have made software packages available for the entire suite of National Weather Service River Forecast System models, including SAC-SMA, SNOW-17, and Lag-K. These modern interfaces are intended to increase accessibility and facilitate future research.

Forecasting↗
DOE OSTI2018

An integrated phenology modelling framework in r

Abstract Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network ( USA ‐ NPN ), the Pan European Phenology Project ( PEP 725), MODIS phenology ( MCD 12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared ( RMSE ) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.

Hufkens, Koen↗
DOE OSTI2020

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon-climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool-based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool-based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. In this study, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Framework for Optimal Placement of Rooftop Photovoltaic: Maximizing Solar Production and Operational Cost Savings in Residential Communities

Optimizing the placement of photovoltaic (PV) panels on residential buildings has the potential to significantly increase energy efficiency benefits to both homeowners and communities. Strategic PV placement can lower electricity costs by reducing the electricity fed from the grid during on-peak hours, while maintaining PV panel efficiency in terms of the amount of solar radiation received. In this article, we present a framework that identifies the ideal location of PV panels on residential rooftops. Our framework combines energy and environmental simulation, parametric modeling, and optimization to inform PV placement as it relates to and affects the entire community (in terms of both energy use and financial cost), as well as individual buildings. Ensuring that our framework accounts for shading from nearby buildings, different utility rate structures, and different buildings’ energy demand profiles means that existing communities and future housing developments can be optimized for energy savings and PV efficiency. The framework comprises two workflows, each contributing to optimal PV placement with a unique target: (a) maximizing PV panel efficiency (i.e., solar generation) and (b) minimizing operational energy cost considering utility rate structures for operational energy. We apply our framework to a residential community in Fort Collins, Colorado, to demonstrate the optimal PV placement, considering the two workflow targets. Here, we present our results and illustrate the effect of PV location and orientation on solar energy production efficiency and operational energy cost.

14 SOLAR ENERGY↗
DOE OSTI2014

SU-E-I-02: A Framework to Perform Batch Simulations of Computational Voxel Phantoms to Study Organ Doses in Computed Tomography Using a Commercial Monte Carlo Software Package

Purpose: ImpactMC (CT Imaging, Erlangen, Germany) is a Monte Carlo (MC) software package that offers a GPU enabled, user definable and validated method for 3D dose distribution calculations for radiography and Computed Tomography (CT). ImpactMC, in and of itself, offers limited capabilities to perform batch simulations. The aim of this work was to develop a framework for the batch simulation of absorbed organ dose distributions from CT scans of computational voxel phantoms. Methods: The ICRP 110 adult Reference Male and Reference Female computational voxel phantoms were formatted into compatible input volumes for MC simulations. A Matlab (The MathWorks Inc., Natick, MA) script was written to loop through a user defined set of simulation parameters and 1) generate input files required for the simulation, 2) start the MC simulation, 3) segment the absorbed dose for organs in the simulated dose volume and 4) transfer the organ doses to a database. A demonstration of the framework is made where the glandular breast dose to the adult Reference Female phantom, for a typical Chest CT examination, is investigated. Results: A batch of 48 contiguous simulations was performed with variations in the total collimation and spiral pitch. The demonstration of the framework showed that the glandular dose to the right and left breast will vary depending on the start angle of rotation, total collimation and spiral pitch. Conclusion: The developed framework provides a robust and efficient approach to performing a large number of user defined MC simulations with computational voxel phantoms in CT (minimal user interaction). The resulting organ doses from each simulation can be accessed through a database which greatly increases the ease of analyzing the resulting organ doses. The framework developed in this work provides a valuable resource when investigating different dose optimization strategies in CT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

A Monte Carlo simulation framework for electron beam dose calculations using Varian phase space files for TrueBeam Linacs

Purpose: To develop a framework for accurate electron Monte Carlo dose calculation. In this study, comprehensive validations of vendor provided electron beam phase space files for Varian TrueBeam Linacs against measurement data are presented. Methods: In this framework, the Monte Carlo generated phase space files were provided by the vendor and used as input to the downstream plan-specific simulations including jaws, electron applicators, and water phantom computed in the EGSnrc environment. The phase space files were generated based on open field commissioning data. A subset of electron energies of 6, 9, 12, 16, and 20 MeV and open and collimated field sizes 3 × 3, 4 × 4, 5 × 5, 6 × 6, 10 × 10, 15 × 15, 20 × 20, and 25 × 25 cm{sup 2} were evaluated. Measurements acquired with a CC13 cylindrical ionization chamber and electron diode detector and simulations from this framework were compared for a water phantom geometry. The evaluation metrics include percent depth dose, orthogonal and diagonal profiles at depths R{sub 100}, R{sub 50}, R{sub p}, and R{sub p+} for standard and extended source-to-surface distances (SSD), as well as cone and cut-out output factors. Results: Agreement for the percent depth dose and orthogonal profiles between measurement and Monte Carlo was generally within 2% or 1 mm. The largest discrepancies were observed within depths of 5 mm from phantom surface. Differences in field size, penumbra, and flatness for the orthogonal profiles at depths R{sub 100}, R{sub 50}, and R{sub p} were within 1 mm, 1 mm, and 2%, respectively. Orthogonal profiles at SSDs of 100 and 120 cm showed the same level of agreement. Cone and cut-out output factors agreed well with maximum differences within 2.5% for 6 MeV and 1% for all other energies. Cone output factors at extended SSDs of 105, 110, 115, and 120 cm exhibited similar levels of agreement. Conclusions: We have presented a Monte Carlo simulation framework for electron beam dose calculations for Varian TrueBeam Linacs. Electron beam energies of 6 to 20 MeV for open and collimated field sizes from 3 × 3 to 25 × 25 cm{sup 2} were studied and results were compared to the measurement data with excellent agreement. Application of this framework can thus be used as the platform for treatment planning of dynamic electron arc radiotherapy and other advanced dynamic techniques with electron beams.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-AB-303-07: Computational Monitoring Framework of Four-Dimensional Dose Distributions During Treatment Time Based On a 2D/3D Registration with Adaptive Transformation Parameters in Lung Stereotactic Body Radiotherapy

Purpose: The aim of this study was to develop a computational framework for monitoring four-dimensional (4D) dose distributions during treatment time based on a 2D/3D registration with adaptive transformation parameters (ATPs) in lung stereotactic body radiotherapy (SBRT). Methods: The 4D dose distributions during the SBRT were calculated by applying a pencil beam convolution (PBC) algorithm to simulated 4D-computed tomography (CT) images during treatment time (hereinafter referred to as “treatment” 4D-CT images). The “treatment” 4D-CT images were derived by deforming 3D planning CT images by using transformation parameters so that 2D planning portal dose images (PDIs) can resemble 2D dynamic clinical PDIs in each frame, which were derived from electronic portal imaging device (EPID) images. The transformation parameters were optimized by the Levenberg- Marquardt (LM) algorithm. The optimized transformation parameters in a certain frame were adaptively employed as initial transformation parameters for optimization of the parameters in the consecutive frame. Gamma pass rates (3mm/3%) between dynamic clinical PDIs and dynamic “treatment” PDIs derived from the “treatment” 4D-CT images were measured for comparing the proposed frameworks without and with the ATPs. These proposed frameworks were applied to the EPID dynamic images (40 frames) of two patients with lung cancer, who underwent the SBRT. Results: Gamma pass rates without and with the ATPs were 83.01±4.42% and 89.31±3.18% on average, respectively (p<0.05). Moreover, percentage errors between prescribed doses and estimated doses at an isocenter by the proposed frameworks without and with the ATPs were 10.67±5.66% and 6.34±0.47% on average, respectively (p<0.05). Conclusion: The proposed framework with ATPs could be useful to ensure the quality of the SBRT by monitoring 4D dose distributions during treatment time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed framework might be useful for tasks involving feature-based image registration in range-image guided radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-IeP1-05: A Framework for the Analytic Calculation of Patient-Specific Dose Distribution Due to CBCT Scan for IGRT

Purpose: To investigate the feasibility of an analytic framework to estimate patients’ absorbed dose distribution owing to daily cone-beam CT scan for image-guided radiation treatment. Methods: To compute total absorbed dose distribution, we separated the framework into primary and scattered dose calculations. Using the source parameters such as voltage, current, and bowtie filtration, for the primary dose calculation, we simulated the forward projection from the source to each voxel of an imaging object including some inhomogeneous inserts. Then we calculated the primary absorbed dose at each voxel based on the absorption probability deduced from the HU values and Beer’s law. In sequence, all voxels constructing the phantom were regarded as secondary sources to radiate scattered photons for scattered dose calculation. Details of forward projection were identical to that of the previous step. The secondary source intensities were given by using scatter-to- primary ratios provided by NIST. In addition, we compared the analytically calculated dose distribution with their Monte Carlo simulation results. Results: The suggested framework for absorbed dose estimation successfully provided the primary and secondary dose distributions of the phantom. Moreover, our analytic dose calculations and Monte Carlo calculations were well agreed each other even near the inhomogeneous inserts. Conclusion: This work indicated that our framework can be an effective monitor to estimate a patient’s exposure owing to cone-beam CT scan for image-guided radiation treatment. Therefore, we expected that the patient’s over-exposure during IGRT might be prevented by our framework.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree of GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Three-dimensional nanoscale metal, metal oxide, and semiconductor frameworks through DNA-programmable assembly and templating

Controlling the three-dimensional (3D) nanoarchitecture of inorganic materials is imperative for enabling their novel mechanical, optical, and electronic properties. Here, by exploiting DNA-programmable assembly, we establish a general approach for realizing designed 3D ordered inorganic frameworks. Through inorganic templating of DNA frameworks by liquid- and vapor-phase infiltrations, we demonstrate successful nanofabrication of diverse classes of inorganic frameworks from metal, metal oxide and semiconductor materials, as well as their combinations, including zinc, aluminum, copper, molybdenum, tungsten, indium, tin, and platinum, and composites such as aluminum-doped zinc oxide, indium tin oxide, and platinum/aluminum-doped zinc oxide. The open 3D frameworks have features on the order of nanometers with architecture prescribed by the DNA frames and self-assembled lattice. Structural and spectroscopic studies reveal the composition and organization of diverse inorganic frameworks, as well as the optoelectronic properties of selected materials. The work paves the road toward establishing a 3D nanoscale lithography.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2010

New isoformula potassium borosilicates with different zeolite frameworks: Monoclinic centrosymmetric boroleucite K(BSi{sub 2})O{sub 6} and chiral KBSi{sub 2}O{sub 6}

The structures of new isoformula potassium borosilicates, obtained under hydrothermal conditions, namely, the monoclinic modification of the cubic boroleucite K(BSi{sub 2})O{sub 6} (space group P2{sub 1}/a), which was previously unknown, and chiral KBSi{sub 2}O{sub 6} (space group P2{sub 1}2{sub 1}2{sub 1})) are studied. A framework is formed both in the monoclinic and orthorhombic new structure in which each two tetrahedra share a vertex, as is typical of silicate frameworks. Since the two compounds have the same chemical formula, they could be spoken of as polymorphic modifications, but they are essentially different in structure: in the orthorhombic phase, the B and Si atoms are distributed over the tetrahedra in an ordered fashion, whereas B together with Si occupy statistically all tetrahedra in the monoclinic modification of boroleucite. The frameworks of both compounds are similar, although the framework is slightly denser in the orthorhombic borosilicate. Large rings consisting of six, seven, or eight tetrahedra determine the zeolite type of the frameworks, whose voids accommodate K atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation

In this paper,we aim to present a novel sequential framework containing two deep network architectures for baseline energy prediction, applied to a simulated commercial building dataset. We have observed that this sequential framework helps to improve long term prediction accuracy, there by mitigating prediction error accumulation over time. The proposed framework utilizes convolution layers to extract features from the input data space without changing spatial relations between variables. These features are memorized by tensor train based gated recurrent units for an accurate long-term prediction. Although, architecture comprising the amalgamation of convolution layers and memory cell have shown promising results in domains such as social media analysis, language modeling, video frame prediction and image recognition, this paper extends its scope to the context of energy applications. Furthermore, the addition of tensor train based gated recurrent units is motivated by the necessity of computational time reduction during the training process, there by making this framework more suitable for future in-field deployment. Lastly, results show the current framework outperforms other existing machine learning based methods, in both short-term and long-term prediction categories.

Chakraborty, Indrasis↗
DOE OSTI2023

Mixed-Precision S/DGEMM Using the TF32 and TF64 Frameworks on Low-Precision AI Tensor Cores

Using NVIDIA graphics processing units (GPUs) equipped with Tensor Cores has enabled the significant acceleration of general matrix multiplication (GEMM) for applications in machine learning (ML) and artificial intelligence (AI) and in high-performance computing (HPC) generally. The use of such power-efficient, specialized accelerators can provide a performance increase between 8 × and 20 ×, albeit with a loss in precision. However, a high level of precision is required in many large scientific and HPC applications, and computing in single or double precision is still necessary for many of these applications to maintain accuracy. Fortunately, mixed-precision methods can be employed to maintain a higher level of numerical precision while also taking advantage of the performance increases from computing with lower-precision AI cores. With this in mind, we extend the state of the art by using NVIDIA’s new TF32 framework. This new framework not only burdens some constraints of the previous frameworks, such as costly 32 16-bit castings but also provides an equivalent precision and performance by using a much simpler approach. We also propose a new framework called TF64 that attempts double-precision arithmetic with low-precision Tensor Cores. Although this framework does not exist yet, we validated the correctness of this idea and achieved an equivalent of 64-bit precision on 32-bit hardware.

Valero Lara, Pedro↗