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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Use of Continuous Integration Tools for Application Performance Monitoring

High performance computing systems are becom- ing increasingly complex, both in node architecture and in the multiple layers of software stack required to compile and run applications. As a consequence, the likelihood is increasing for application performance regressions to occur as a result of routine upgrades of system software components which interact in complex ways. The purpose of this study is to evaluate the effectiveness of continuous integration tools for application performance monitoring on HPC systems. In addition, this paper also describes a prototype system for application perfor- mance monitoring based on Jenkins, a Java-based continuous integration tool. The monitoring system described leverages several features in Jenkins to track application performance results over time. Preliminary results and lessons learned from monitoring applications on Cray systems at the Oak Ridge Leadership Computing Facility are presented.

Vergara Larrea, Veronica G↗
DOE OSTI2025

Elastic Stochastic Full Waveform Inversion (eSFWI)

This collaboration between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Chevron USA Inc., acting through its Chevron Technical Center division, aimed at developing next-generation computational methods for the Elastic Stochastic Full Waveform Inversion (eSFWI). Seismic imaging is heavily used in the oil and gas industry for identifying and operating subsurface reservoirs. Improved seismic imaging methods can improve productivity, lower costs, and improve operational and environmental safety. This CRADA demonstrated that new high-performance computing (HPC) architectures being rolled out over the next five years can enable unprecedented seismic imaging resolution when using eSFWI techniques to process active seismic data. An open-source computational mini-application was developed, capable of demonstrating near-peak performance for eSFWI algorithms on CPU and GPU enabled HPC platforms. Performance was demonstrated on LLNL HPC systems such as Lassen, as well as on Chevron systems. This project benefited Chevron USA Inc. by demonstrating the potential computational efficiency of their full waveform inversion capabilities used to characterize oil/gas reservoirs, which in turn benefits the public through potential increases in capabilities to perform analysis of leasing sites.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

Particle–Continuum Coupling and its Scaling Regimes: Theory and Applications

Abstract This work is motivated by the goal of designing simulation software for technical devices that, at their functional core, rely on atomistic‐scale processes embedded in a larger‐scale fluid environment. The core of the problem is the conceptual and technical approach for coupling particle and continuum representations of a fluid. The state of the art for key aspects including physical modeling, mathematical formalization, computational implementation, and applications, is discussed and organized in a consistent picture across the relevant physical regimes.

Delle Site, Luigi↗
DOE OSTI2019

Automatic Differentiation for Adjoint Stencil Loops

Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil loops are easy to parallelise, and their fast execution is aided by compilers, libraries, and domain-specific languages. Reverse-mode automatic differentiation, also known as algorithmic differentiation, autodiff, adjoint differentiation, or back-propagation, is sometimes used to obtain gradients of programs that contain stencil loops. Unfortunately, conventional automatic differentiation results in a memory access pattern that is not stencil-like and not easily parallelisable. In this paper we present a novel combination of automatic differentiation and loop transformations that preserves the structure and memory access pattern of stencil loops, while computing fully consistent derivatives. The generated loops can be parallelised and optimised for performance in the same way and using the same tools as the original computation. We have implemented this new technique in the Python tool PerforAD, which we release with this paper along with test cases derived from seismic imaging and computational fluid dynamics applications.

Automatic Differentiation↗
DOE OSTI2020

ASCR@40: Highlights and Impacts of ASCR’s Programs

A report compiled by the ASCAC Subcommittee on the 40-year history of ASCR for the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. The Office of Advanced Scientific Computing Research (ASCR) sits within the Office of Science in the Department of Energy (DOE). Per their web pages, “the mission of the ASCR program is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” This succinct statement encompasses a wide range of responsibilities for computing and networking facilities; for procuring, deploying, and operating high performance computing, networking, and storage resources; for basic research in mathematics and computer science; for developing and sustaining a large body of software; and for partnering with organizations across the Office of Science and beyond. While its mission statement may seem very contemporary, the roots of ASCR are quite deep—long predating the creation of DOE. Applied mathematics and advanced computing were both elements of the Theoretical Division of the Manhattan Project. In the early 1950s, the Manhattan Project scientist and mathematician John von Neumann, then a commissioner for the AEC (Atomic Energy Commission), advocated for the creation of a Mathematics program to support the continued development and applications of digital computing. Los Alamos National Laboratory (LANL) scientist John Pasta created such a program to fund researchers at universities and AEC laboratories. Under several organizational name changes, this program has persisted ever since, and would eventually grow to become ASCR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Automated Model for Improved Mapping of Country-Scale High-Resolution Coastal Bathymetry

Accurate and up-to-date maps of coastal bathymetry are critical for a variety of sectors from vessel navigation to port construction and are increasingly vital to inform coastal infrastructure management amid accelerating sea-level rise and more frequent and severe storm-surge events. The primary challenges to accurate and efficient bathymetric mapping from optical remote sensing are: accurately modeling light attenuation in both the atmosphere and water column; and inefficient data processing pipelines for highresolution satellite imagery. To address the effects of light attenuation within the water-column we have developed a novel physics-based bathymetry model that accounts for variations in water-column inherent optical properties on a pixel-by-pixel basis in optically-shallow aquatic environments, which represents a major advancement. For applications to satellite imagery, we have developed a software package that automatically applies the robust 6S atmospheric correction algorithm, estimates the bottom depth from the physics-based bathymetry model, which corrects for attenuation by suspended particulate matter and colored dissolved organic matter in the water column on a pixel-by-pixel basis, and leverages highperformance computing for rapid application to region-scale (e.g., CONUS) 2-meter resolution imagery datasets. Our approach is tailored to sensors that collect data at two-meter resolution with high return times for frequent updates. We mapped 591 WorldView satellite images for Florida, North Carolina, and Alaska and validated the maps against LiDAR-derived bathymetry data. Results show median RMSE of 1.75, 1.59, and 2.67 meters, respectively. By combining the strengths of Oak Ridge National Laboratory in high-performance computing and remote-sensing science with the water-column modeling expertise of Scripps Institution of Oceanography we advance the state of bathymetric mapping capability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms

The quantum approximate optimization algorithm (QAOA) has enjoyed increasing attention in noisy, intermediate-scale quantum computing with its application to combinatorial optimization problems. QAOA has the potential to demonstrate a quantum advantage for NP-hard combinatorial optimization problems. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem in which the simulation outcomes are attainable only through a quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method derived from a derivative-free, adaptive sampling trust-region optimization method intended to efficiently solve the classical optimization problem in QAOA by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function and a model of the variance of the objective function. Exploiting the variance model allows us to restrict the number of communications with the quantum computer and also helps navigate the nonconvex objective landscapes typical in QAOA optimization problems. In conclusion, we numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit when we consider a metric of computational burden that explicitly accounts for communication costs.

Derivative-free Optimization↗
DOE OSTI2022

Accelerating Flash-X Simulations with Asynchronous I/O

Most high-fidelity physics simulation codes, such as Flash-X, need to save intermediate results (checkpoint files) to restart or gain insights into the evolution of the simulation. These simulation codes save such intermediate files synchronously, where computation is stalled while the data is written to storage. Depending on the problem size and computational requirements, this file write time can be a substantial portion of the total simulation time. In order to hide the I/O latency of checkpointing, asynchronous I/O methods have been introduced. These methods use background threads for performing I/O while the main threads continue with the simulation. The usage of background threads can compete for resources on the node as well as with communication. In this paper, we evaluate the overheads and the overall benefit of asynchronous I/O in HDF5 to simulations. Results from real-world high-fidelity simulations on the Summit supercomputer show that I/O operation is overlapped with application communication or computation or both, effectively hiding some or all of the I/O latency. Our evaluation shows that while using asynchronous I/O adds overhead to the application, the I/O time reduction is more significant, resulting in overall up to 1.5X performance speedup.

Jain, Rajeev↗
DOE OSTI2024

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

MiniApp for Density Matrix Renormalization Group Hamiltonian Application Kernel

We present two miniapps that implement the core computational kernel of the DMRG++ application, a generic C++ code that implements the Density Matrix Renormalization Group (DMRG) algorithm. The DMRG++ core Kronecker multiplication kernel is formulated using a batched BLAS approach, with implementation that targets both multi-core CPUs using OpenMP and GPGPU using the MAGMA library. The kernel evaluates the matrix-vector multiplication of the target Hamiltonian matrix used in Lanczos algorithm for computing the lowest eigenvalue and eigenvector. The Hamiltonian matrix is expressed compactly as sums of Kronecker products of small dense matrices. We demonstrate improved performance of the miniapp on synthetic problem, and show the performance of the DMRG++ application using a plugin based on the miniapp. We also present an OpenMP miniapp that explores the use of nested parallel constructs to implement the Kronecker multiplication kernel, exploring the use of nested OpenMP worksharing and tasking abstractions to implement the multi-level parallel multiplication algorithm. The miniapp has been used as a co-design vehicle for evaluating features in the OpenMP-4.5 and upcoming OpenMP-5.0 standards.

Elwasif, Wael↗
DOE OSTI2020

Quantum Computing for Neutrino-Nucleus Scattering

Neutrino-nucleus cross section uncertainties are expected to be a dominant systematic in future accelerator neutrino experiments. The cross sections are determined by the linear response of the nucleus to the weak interactions of the neutrino, and are dominated by energy and distance scales of the order of the separation between nucleons in the nucleus. These response functions are potentially an important early physics application of quantum computers. Here we present an analysis of the resources required and their expected scaling for scattering cross section calculations. The current estimates of Trotter steps needed to achieve an energy resolution of 10 MeV and the number of CNOT gates for analyzing Ar40 highlights the need for significant improvements in algorithms. We also examine simple small-scale neutrino-nucleus models on modern quantum hardware. In this paper, we use variational methods to obtain the ground state of a three nucleon system (the triton) and then implement the relevant time evolution. To tame the errors in present-day NISQ devices, we explore the use of different error-mitigation techniques to increase the fidelity of the calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

OpenMP 4.5 Validation and Verification Suite for Device Offload

OpenMP has been widely adopted for shared memory systems for over a decade. With the heterogeneity trend in architectures rapidly growing, the programming model needed to evolve such that applications could not only be ported to traditional CPUs but also to accelerators often acting as discrete or integrated devices to CPUs. To that end, OpenMP started to provide support for heterogeneous systems since 2013 when the version 4.0 of the specification was ratified. OpenMP 4.5 is being enhanced to cover major requirements of Exascale Computing Project (ECP) applications. As a result it is time-critical to ensure that the implementations of the 4.5 features are correct and conforming to the specification. This paper focuses on building a Validation and Verification testsuite that will test and present results for several offloading features implemented in compilers such as Clang, IBM XL C/C++, CCE, and GCC. We have results for our testsuite on TITAN, Summitdev and Summit at the Oak Ridge National Lab. We will highlight some of the ambiguities we encountered in the process of validating and verifying feature implementations. We also make the testsuite available for anyone to use and will walk the readers through the infrastructure and the workflow of the testsuite. A website has been built to capture our efforts narrated in this paper https://crpl.cis.udel.edu/ompvvsollve.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

DS-TIDE: Harnessing Dynamical Systems for Efficient Time-Independent Differential Equation Solving

Time-Independent Differential Equations (TIDEs) are central to modeling equilibrium behavior across a wide range of scientific and engineering domains, from electrostatics to porous media flow. Conventional numerical solvers offer reliable solutions but incur significant computational costs due to fine-grained discretization and iterative procedures. Machine learning-based approaches address this by replacing iterative solving processes with one-time inference; however, their sophisticated models require extensive training resources that often exceed those of traditional solvers. Consequently, designing a TIDE solver that achieves high accuracy, broad applicability, and exceptional computational efficiency remains a fundamental challenge. In this paper, we propose DS-TIDE, a novel hardware solver that is inspired by, and subsequently leverages, the intrinsic connection between Dynamical Systems (DS) and Differential Equations (DEs) to efficiently and accurately solve TIDEs. DS-TIDE employs a CMOS-compatible DS-based processor, whose physical states evolve under carefully designed DE-driven dynamics and naturally converge to equilibrium -- the solution of the target TIDE -- within ~1µs on a ~1-watt DS-TIDE processor. To enhance expressivity, DS-TIDE incorporates Heterogeneous Dynamics with Temporal Layering (HDTL), which solves TIDEs through a three-stage DS evolution -- conditioning, solving, and decoding -- each governed by specialized dynamics. The entire evolution process is analogous to an infinitely deep neural network temporally unrolled, offering the system the capability of representing complex equations. Furthermore, DS-TIDE is equipped with an on-device DS-DE Auto-Alignment mechanism that dynamically adapts intrinsic hardware dynamics within milliseconds, effectively aligning the system’s dynamics to diverse target DEs. Experimental results across TIDEs from a wide range of scientific and engineering domains demonstrate that DS-TIDE achieves ~10^3× speedup, ~10^5× energy savings, and competitive or superior accuracy compared to state-of-the-art numerical and ML-based solvers.

Liu, Chuan↗
DOE OSTI2016

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification of Thiourea-Based Inhibitors of the B-Cell Lymphoma 6 BTB Domain via NMR-Based Fragment Screening and Computer-Aided Drug Design

Protein–protein interactions (PPI) between the transcriptional repressor B-cell lymphoma 6 (BCL6) BTB domain (BCL6 BTB ) and its corepressors have emerged as a promising target for anticancer therapeutics. However, identification of potent, drug-like inhibitors of BCL6 BTB has remained challenging. Using NMR-based screening of a library of fragment-like small molecules, we have identified a thiourea compound (7CC5) that binds to BCL6 BTB . From this hit, the application of computer-aided drug design (CADD), medicinal chemistry, NMR spectroscopy, and X-ray crystallography has yielded an inhibitor, 15f, that demonstrated over 100-fold improved potency for BCL6 BTB . This gain in potency was achieved by a unique binding mode that mimics the binding mode of the corepressor SMRT in the aromatic and the HDCH sites. Overall, the structure–activity relationship based on these new inhibitors will have a significant impact on the rational design of novel BCL6 inhibitors, facilitating the identification of therapeutics for the treatment of BCL6-dependent tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

OpenMP 4.5 Validation and Verification Suite for Device Offload

OpenMP has been widely adopted for shared memory systems for over a decade. With the heterogeneity trend in architectures rapidly growing, the programming model needed to evolve such that applications could not only be ported to traditional CPUs but also to accelerators often acting as discrete or integrated devices to CPUs. To that end, OpenMP started to provide support for heterogeneous systems since 2013 when the version 4.0 of the specification was ratified. OpenMP 4.5 is being enhanced to cover major requirements of Exascale Computing Project (ECP) applications. As a result it is time-critical to ensure that the implementations of the 4.5 features are correct and conforming to the specification. This paper focuses on building a Validation and Verification testsuite that will test and present results for several offloading features implemented in compilers such as Clang, IBM XL C/C++, CCE, and GCC. We have results for our testsuite on TITAN, Summitdev and Summit at the Oak Ridge National Lab. We will highlight some of the ambiguities we encountered in the process of validating and verifying feature implementations. We also make the testsuite available for anyone to use and will walk the readers through the infrastructure and the workflow of the testsuite. A website has been built to capture our efforts narrated in this paper https://crpl.cis.udel.edu/ompvvsollve.

Monsalve Diaz, Jose↗