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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fast truncated SVD of sparse and dense matrices on graphics processors

We investigate the solution of low-rank matrix approximation problems using the truncated singular value decomposition (SVD). For this purpose, we develop and optimize graphics processing unit (GPU) implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which can be assembled using numerical kernels from existing high-performance linear algebra libraries. Furthermore, the experiments with several sparse matrices arising in representative real-world applications and synthetic dense test matrices reveal a performance advantage of the block Lanczos algorithm when targeting the same approximation accuracy.

Computer Science↗
DOE OSTI2023

Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units

We present a solution of sparse ACOPF analysis on GPU. In particular, we discuss the performance bottlenecks and detail our efforts to accelerate the linear solver, a core component of ACOPF that dominates the computational time. ACOPF solutions of two large-scale systems, synthetic Northeast (25,000 buses) and Eastern (70,000 buses) \cite{birchfield2017tamu-cases} on GPU show promising speed-up compared to CPU based solution using a state-of-the-art solver. To our knowledge, this is the first result demonstrating acceleration of sparse ACOPF on GPUs.

Power grid analysis, GPU↗
DOE OSTI2021

Graphic layers and related methods for incorporation of graphic layers into solar modules

In some aspects, graphic layers for depicting a visible representation of an image along a surface of a photovoltaic module can include a plurality of substantially opaque isolated regions; and at least one substantially transparent contiguous region surrounding the substantially opaque isolated regions, wherein an outer surface of the at least one substantially transparent contiguous region comprises a matte surface finish.

Occidentale, Anthony John↗
DOE OSTI2018

Graphics processing unit management system for computed tomography

A method and apparatus for managing operation of graphics processing units in a computer system. A processing thread in the computer system controls processing of a set of sinogram data by a graphics processing unit to form a set of graphics data. An output thread in the computer system controls writing of the set of graphics data to a storage system. The graphics processing unit is available to perform processing of another set of sonogram data while the set of graphics data is written to the storage system such that the increased availability of a plurality of graphics processing units is present.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Graphics processor unit with opportunistic inter-path reconvergence

A graphics processing unit and methods for comping and executing instructions with opportunistic inter-path reconvergence are provided. A graphics processing unit may access computer executable instructions mapped to code blocks of a control flow for a warp. The code blocks may include an immediate dominator block and an intermediate post dominator block. The graphics processing unit may store a first thread mask associated with the first code block. The first thread mask may include a plurality of bits indicative of the active or non-active status for the threads of the warp, respectively. The graphics processing unit may a second thread mask corresponding to an intermediate code block between the immediate dominator block and intermediate post dominator block. The graphics processing unit may execute, with threads indicated as active by the first thread mask, instructions of the intermediate code block with a first operand or a second operand depending on the second thread mask.

Kulkarni, Milind↗
DOE OSTI2022

Introduction to Graphics Processing Units [Slides]

Graphics Processing Units are designed for fast graphics processing. Graphics are a form of arithemetic and have gradually evolved a design that is also usefule for non-graphics computing. They are not standalone, but work alongside a CPU(host)-coprocessor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A probabilistic graphical model foundation for enabling predictive digital twins at scale

A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work proposes a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset–twin system as a set of coupled dynamical systems, evolving over time through their respective state spaces and interacting via observed data and control inputs. The formal definition of this coupled system as a probabilistic graphical model enables us to draw upon well-established theory and methods from Bayesian statistics, dynamical systems and control theory. The declarative and general nature of the proposed digital twin model make it rigorous yet flexible, enabling its application at scale in a diverse range of application areas. Here, we demonstrate how the model is instantiated to enable a structural digital twin of an unmanned aerial vehicle (UAV). The digital twin is calibrated using experimental data from a physical UAV asset. Its use in dynamic decision-making is then illustrated in a synthetic example where the UAV undergoes an in-flight damage event and the digital twin is dynamically updated using sensor data. The graphical model foundation ensures that the digital twin calibration and updating process is principled, unified and able to scale to an entire fleet of digital twins.

42 ENGINEERING↗
DOE OSTI2020

Gauges, loops, and polynomials for partition functions of graphical models

Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is #P-hard that is of an exponential complexity with respect to the number of variables. Here, aimed at approximating the partition function, we consider multi-graph models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge function technique from Chertkov and Chernyak with the technique developed in Anari and Oveis Gharan 2017 arXiv:1702.02937; Gurvits 2011 arXiv:1106.2844; Straszak and Vishnoi 2017 55th Annual Allerton Conf. on Communication, Control, and Computing, based on the recent progress in the field of real stable polynomials. We show that the Gauge function, representing a single-out term in a finite sum expression for the partition function which achieves extremum at the so-called belief-propagation gauge, has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, Gauge function can be used to recover the partition function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists of the application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination/contraction of edges resulting in multi-graph models on decreasing in size (number of edges) graphs with the same partition function as in the original multi-graph model. Even though the complexity of computing factors in the sequence of the derived multi-graph models and respective Gauge functions grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that BP estimations in the sequence do not decrease, each low-bounding the partition function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Rejoinder to “Graphics to facilitate informative discussion and team decision-making”

We would like to thank Dr Dae-Heung Jang and Dr Jennifer Van Mullekom for their thoughtful discussions. We are encouraged that they share our vision for highlighting and expanding the role of graphics for improving team discussion and decision-making. Here we would like to respond to several comments that they made in their discussions: (1) how the availability of the third dimension in graphics can improve team members' ability to properly visualize and connect with the data, (2) the importance of developing dynamic and interactive graphics to allow exploration of questions as they emerge during the discussion, (3) the role of process and relationships in helping reach good decisions based on the developed objectives, and (4) the benefits of building a body of case studies on which those new to the methods can draw understanding.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Computationally efficient Bayesian estimation of graphical networks for omics data

Graphical networks are useful, widely-used modeling approaches to represent complex biological processes with biological measurements generated by platforms such as mass spectrometry. Bayesian analyses of graphical networks for omics data have several advantages over their frequentist counterparts, such as the inclusion of prior knowledge in the estimation of models. However, Bayesian approaches to date have only been feasible for data with a couple hundred biomolecules due to prohibitive computational time, but omics data often contains tens of thousands of biomolecules. Here, we present and illustrate a more computationally efficient approach named BPlane (Bayesian PseudoLikelihood-based Algorithm for Network Estimation) to extend Bayesian modeling capabilities for larger-sized datasets, such as most untargeted proteomics data. Via simulation, we demonstrate that BPlane produces substantial computational savings over a current state-of-the-art Bayesian algorithm while maintaining competitive edge detection accuracy. On a SARS-CoV2 proteomics data with 7000 proteins, the competing algorithm takes three times as long to complete the first iteration as BPlane takes to converge after over 100 iterations.

EM algorithm↗
DOE OSTI2018

Graphic statics and their potential for digital design and fabrication with concrete

The paper describes the influence of the lower bound theorem of theory of plasticity on the design of structures during the last 70 years. Starting with the fundamental theorems developed in the middle of the last century, the influence of rigid plastic (discontinuous) stress fields on the design of reinforced beam, wall, slab and shell structures is worked out. The potential of graphic statics for digital design and fabrication is illustrated by the results of several research projects on form finding and design of reinforced concrete structures carried out during the last decade. It is shown that the design methods allow for simultaneous considerations about structure and architecture. This leads to leaner structure and reduced material consumption. However, while a current limitation of manufacturing objects in these ''nonstandard'' shapes is presently cost limiting, digital fabrication may change this and open a much greater scope for the use and application of graphic statics in a near future.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Implementation of the Molecular Electrostatic Potential over Graphics Processing Units

The molecular electrostatic potential (MEP) generated by quantum chemistry methods and Gaussian functions is evaluated over graphics processing units (GPUs). This implementation is based on full-range Rys polynomials with nodes and weights obtained in each thread of a GPU. For high angular moments, the corresponding integral is solved using a one dimension vertical recurrence relation. Thus, we computed the MEP with minimal approximations. We show that this implementation is stable and very efficient since the time consumed over GPUs is quite small compared with similar implementations over CPUs. The implementation was done by using CUDA-C programming techniques within the Graphics Processing Units for Atoms and Molecules (GPUAM) project, which has been designed to analyze quantum chemistry fields over heterogeneous computational resources. With this new scalar field GPUAM is a useful application for the quantum chemistry community, in particular for people interested in chemical reactivity analysis.

Cruz-Monterrosas, Julio-Cesar↗
DOE OSTI2025

gRASPA

GPU Monte Carlo Simulation Code with a taste of RASPA We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.

Li, Zhao [Purdue/Northwestern/Notre Dame Universit↗
DOE OSTI2020

The Sylvester Graphical Lasso (SyGlasso)

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. Here we apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. Here, we demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.

Wang, Yu↗
DOE OSTI2019

Representations of Shavitt Graphs Within the Graphical Unitary Group Approach

The Shavitt graph is a visual representation of a distinct row table (DRT) within the graphical unitary group approach. The DRT is a compact representation of the entire configuration state function expansion space within a molecular electronic structure calculation. Each node of the graph is associated with an integer triple (a k , b k , c k ). These integers may be mapped to other quantum numbers, including the number of orbitals, number of electrons, and spin quantum number, and used to display Shavitt graphs in various ways that emphasize different aspects of the expansion space or that reveal different aspects of computed wave functions. In conclusion, the features of several graph density plots are discussed, including electron-hole symmetries and the bonding-antibonding wave function character.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models

The conductor–like polarizable continuum model (C–PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in quantum chemistry. We have previously implemented C–PCM solvation for Hartree–Fock (HF) and density functional theory (DFT) on graphical processing units (GPUs), enabling the quantum mechanical treatment of large solvated biomolecules. Here, we first propose a GPU–based algorithm for the PCM conjugate gradient linear solver that greatly improves the performance for very large molecules. The overhead for PCM–related evaluations now consumes less than 15% of the total runtime for DFT calculations on large molecules. Second, we demonstrate that our algorithms tailored for ground state C–PCM are transferable to excited state properties. Using a single GPU, our method evaluates the analytic gradient of the linear response PCM time–dependent density functional theory energy up to 80× faster than a conventional central processing unit (CPU)–based implementation. In addition, our C–PCM algorithms are transferable to other methods that require electrostatic potential (ESP) evaluations. For example, we achieve speed–ups of up to 130× for restricted ESP–based atomic charge evaluations, when compared to CPU–based codes. As a result, we also summarize and compare the different PCM cavity discretization schemes used in some popular quantum chemistry packages as a reference for both users and developers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗