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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

An Efficient Algorithm for Mapping Imaging Data to 3D Unstructured Grids in Computational Biomechanics

Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: 1) the mapping of MRI diffusion tensor data to an unstuctured ventricular grid; 2) the mapping of serial cyro-section histology data to an unstructured mouse brain grid; and 3) the mapping of CT-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case.

Einstein, Daniel R.↗
DOE OSTI2017

Evaluating the networking characteristics of the Cray XC-40 Intel Knights Landing-based Cori supercomputer at NERSC

There are many potential issues associated with deploying the Intel Xeon Phi™ (code named Knights Landing [KNL]) manycore processor in a large-scale supercomputer. One in particular is the ability to fully utilize the high-speed communications network, given that the serial performance of a Xeon Phi TM core is a fraction of a Xeon®core. In this paper, we take a look at the trade-offs associated with allocating enough cores to fully utilize the Aries high-speed network versus cores dedicated to computation, e.g., the trade-off between MPI and OpenMP. In addition, we evaluate new features of Cray MPI in support of KNL, such as internode optimizations. We also evaluate one-sided programming models such as Unified Parallel C. We quantify the impact of the above trade-offs and features using a suite of National Energy Research Scientific Computing Center applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Large language model evaluation for high–performance computing software development

We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, CUDA.jl, AMDGPU.jl, and KernelAbstractions.jl). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple + + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple “prompt engineering” follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and CUDA.jl programming models. Finally, we expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Endowing Polythioester Vitrimer with Intrinsic Crystallinity and Chemical Recyclability

Abstract Technologically important thermosets face a long‐standing end‐of‐life (EoL) problem of non‐reprocessability, a more sustainable solution of which has resolved to nascent vitrimers that can merge the robust material properties of thermosets and the reprocessability of thermoplastics. However, the lifecycle of vitrimers is still finite, as they often suffer from significant deterioration of mechanical performance following multiple reprocessing cycles, analogous to mechanical recycling, and they often show undesired creep under working conditions. To address these two key limitations, we have developed a cross‐linked semi‐crystalline polythioester with both dynamic covalent bonds and intrinsic crystallinity and chemical recyclability, affording a vitrimeric system that exhibits not only reprocessability and crystallinity‐restricted creep but also complete chemical recyclability to initial monomer by catalyzed depolymerization in solution or bulk. Therefore, reported herein is an “infinite” vitrimer system that is empowered with a facile closed‐loop EoL option once serial reprocessing deteriorates performance and the material can no longer meet the application requirements. Specifically, the polythioester vitrimer was constructed by copolymerization of a bicyclic thioester with a bis‐dithiolane, producing dynamically cross‐linked polythioesters with excellent property tunability, from amorphous to semi‐crystalline states and melting transition temperatures from 91 to 178 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Speedup of UEDGE Parameter Scans Using Machine-Learning Optimized OpenMP Parallelization and a Continuation Solver

This article presents the OpenMP parallelization of the preconditioning Jacobian assembly and right‐hand side residual evaluation in UEDGE. A continuation algorithm, utilizing the internal NKSOL implicit Jacobian‐Free Newton‐Krylov solver to efficiently scan physical parameters, is also presented. The implemented parallelization reduces the computational time for a benchmark scan run on 32 threads by compared to the serial version when using trained random forest regression models to identify the optimal decomposition of the system of equations. Random forest regression models applied to the UEDGE time‐dependent and continuation solver algorithms did not yield meaningful improvement in computational performance. A benchmark DIII‐D gas injection rate scan in the 0.35–0.75 kA interval, performed on a test cluster using the parallelized code and continuation solver, produced 1066 steady‐state solutions with a 22 s average wall‐clock computational time per steady‐state solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Performance analysis of a 3D unstructured mesh hydrodynamics code on multi‐core and many‐core architectures

SUMMARY Several next generation high performance computing platforms are or will be based on the so‐called many‐core architectures, which represent a significant departure from commodity multi‐core architectures. A key issue in transitioning large‐scale simulation codes from multi‐core to many‐core systems is closing the serial performance gap, that is, overcoming the large difference in single‐core performance between multi‐core and many‐core systems. In this paper, we discuss how this problem was addressed for a 3D unstructured mesh hydrodynamics code, describe how Amdahl's law can be used to estimate performance targets and guide optimization efforts, and present timing studies performed on multi‐core and many‐core platforms. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Waltz, J.↗
DOE OSTI2015

Comparison of Oncentra® Brachy IPSA and graphical optimisation techniques: a case study of HDR brachytherapy head and neck and prostate plans

There are a number of different dwell positions and time optimisation options available in the Oncentra® Brachy (Elekta Brachytherapy Solutions, Veenendaal, The Netherlands) brachytherapy treatment planning system. The purpose of this case study was to compare graphical (GRO) and inverse planning by simulated annealing (IPSA) optimisation techniques for interstitial head and neck (HN) and prostate plans considering dosimetry, modelled radiobiology outcome and planning time. Four retrospective brachytherapy patients were chosen for this study, two recurrent HN and two prostatic boosts. Manual GRO and IPSA plans were generated for each patient. Plans were compared using dose–volume histograms (DVH) and dose coverage metrics including; conformity index (CI), homogeneity index (HI) and conformity number (CN). Logit and relative seriality models were used to calculate tumour control probability (TCP) and normal tissue complication probability (NTCP). Approximate planning time was also recorded. There was no significant difference between GRO and IPSA in terms of dose metrics with mean CI of 1.30 and 1.57 (P > 0.05) respectively. IPSA achieved an average HN TCP of 0.32 versus 0.12 for GRO while for prostate there was no significant difference. Mean GRO planning times were greater than 75 min while average IPSA planning times were less than 10 min. Planning times for IPSA were greatly reduced compared to GRO and plans were dosimetrically similar. For this reason, IPSA makes for a useful planning tool in HN and prostate brachytherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

A comparison of preconditioned Krylov subspace methods for large‐scale nonsymmetric linear systems

Summary Preconditioned Krylov subspace (KSP) methods are widely used for solving large‐scale sparse linear systems arising from numerical solutions of partial differential equations (PDEs). These linear systems are often nonsymmetric due to the nature of the PDEs, boundary or jump conditions, or discretization methods. While implementations of preconditioned KSP methods are usually readily available, it is unclear to users which methods are the best for different classes of problems. In this work, we present a comparison of some KSP methods, including GMRES, TFQMR, BiCGSTAB, and QMRCGSTAB, coupled with three classes of preconditioners, namely, Gauss–Seidel, incomplete LU factorization (including ILUT, ILUTP, and multilevel ILU), and algebraic multigrid (including BoomerAMG and ML). Theoretically, we compare the mathematical formulations and operation counts of these methods. Empirically, we compare the convergence and serial performance for a range of benchmark problems from numerical PDEs in two and three dimensions with up to millions of unknowns and also assess the asymptotic complexity of the methods as the number of unknowns increases. Our results show that GMRES tends to deliver better performance when coupled with an effective multigrid preconditioner, but it is less competitive with an ineffective preconditioner due to restarts. BoomerAMG with a proper choice of coarsening and interpolation techniques typically converges faster than ML, but both may fail for ill‐conditioned or saddle‐point problems, whereas multilevel ILU tends to succeed. We also show that right preconditioning is more desirable. This study helps establish some practical guidelines for choosing preconditioned KSP methods and motivates the development of more effective preconditioners.

Ghai, Aditi↗
DOE OSTI2022

Weighted relaxation for multigrid reduction in time

Current trends in computer architectures now mean that faster computation speed must come primarily from increased concurrency, not faster clock speeds, which are stagnating. Thus, this situation creates bottlenecks for serial algorithms, including the well-known bottleneck for sequential time-integration, where each individual time-value (i.e., time-step) is computed sequentially. One approach to alleviate this and achieve parallelism in time is with multigrid. Here, in this work, we consider multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time dimension that computes multiple time-steps in parallel. Like all multigrid methods, MGRIT relies on the complementary relationship between relaxation on a fine-grid and a correction from the coarse grid to solve the problem. All current MGRIT implementations are based on unweighted-Jacobi relaxation; here we introduce the concept of weighted relaxation to MGRIT. We derive new convergence bounds for weighted relaxation, and use this analysis to guide the selection of relaxation weights. Numerical results then demonstrate that by choosing appropriate non-unitary relaxation weights, one can achieve faster convergence rates and lower iteration counts for MGRIT when compared with unweighted relaxation. In most cases, weighted relaxation yields a 10%–20% saving in iterations, which is significant when using large high-performance computers. For A-stable integration schemes, results also illustrate that under-relaxation can restore convergence in some cases where unweighted relaxation is not convergent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Bounding the solutions of parametric weakly coupled second-order semilinear parabolic partial differential equations

Here in this paper, two novel techniques for bounding the solutions of parametric weakly coupled second-order semilinear parabolic partial differential equations are developed. The first provides a theorem to construct interval bounds, while the second provides a theorem to construct lower bounds convex and upper bounds concave in the parameter. The convex/concave bounds can be significantly tighter than the interval bounds because of the wrapping effect suffered by interval analysis in dynamical systems. Both types of bounds are computationally cheap to construct, requiring solving auxiliary systems twice and four times larger than the original system, respectively. An illustrative numerical example of bound construction and use for deterministic global optimization within a simple serial branch-and-bound algorithm, implemented numerically using interval arithmetic and a generalization of McCormick's relaxation technique, is presented. Finally, problems within the important class of reaction-diffusion systems may be optimized with these tools.

42 ENGINEERING↗
DOE OSTI2016

Extraction efficiency and implications for absolute quantitation of propranolol in mouse brain, liver and kidney thin tissue sections using droplet-based liquid microjunction surface sampling-HPLC ESI-MS/MS

Currently, absolute quantitation aspects of droplet-based surface sampling for thin tissue analysis using a fully automated autosampler/HPLC-ESI-MS/MS system are not fully evaluated. Knowledge of extraction efficiency and its reproducibility is required to judge the potential of the method for absolute quantitation of analytes from thin tissue sections. Methods: Adjacent thin tissue sections of propranolol dosed mouse brain (10- μm-thick), kidney (10- μm-thick) and liver (8-, 10-, 16- and 24- μm-thick) were obtained. Absolute concentration of propranolol was determined in tissue punches from serial sections using standard bulk tissue extraction protocols and subsequent HPLC separations and tandem mass spectrometric analysis. These values were used to determine propranolol extraction efficiency from the tissues with the droplet-based surface sampling approach. Results: Extraction efficiency of propranolol using 10- μm-thick brain, kidney and liver thin tissues using droplet-based surface sampling varied between ~45-63%. Extraction efficiency decreased from ~65% to ~36% with liver thickness increasing from 8 μm to 24 μm. Randomly selecting half of the samples as standards, precision and accuracy of propranolol concentrations obtained for the other half of samples as quality control metrics were determined. Resulting precision ( ±15%) and accuracy ( ±3%) values, respectively, were within acceptable limits. In conclusion, comparative quantitation of adjacent mouse thin tissue sections of different organs and of various thicknesses by droplet-based surface sampling and by bulk extraction of tissue punches showed that extraction efficiency was incomplete using the former method, and that it depended on the organ and tissue thickness. However, once extraction efficiency was determined and applied, the droplet-based approach provided the required quantitation accuracy and precision for assay validations. Furthermore, this means that once the extraction efficiency was calibrated for a given tissue type and drug, the droplet-based approach provides a non-labor intensive and high-throughput means to acquire spatially resolved quantitative analysis of multiple samples of the same type.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Engineering Supramolecular Hybrid Architectures with Directional Organofluorine Bonds

Understanding how chemical modifications alter the atomic‐scale organization of materials is of fundamental importance in materials engineering and the target of considerable efforts in computational prediction. Incorporating covalent and noncovalent interactions in designing crystals while “piggybacking” on the driving force of molecular self‐assembly has augmented efforts to understand the emergence of complex structures using directed synthesis. In this work, microcrystalline powders of the silver 2‐, 3‐, and 4‐fluorobenzenethiolates are prepared and their structures are resolved by small‐molecule serial femtosecond X‐ray crystallography. These three compounds enable the emergence and role of supramolecular synthons in the crystal structures of 3D metal‐organic chalcogenolates to be examined. The unique divergence in their optoelectronic, morphological, and structural behaviors is assessed. The extent of CHF interactions and their influence on the structure and the observed trends in the thermal stability of the crystals are quantified through theoretical calculations and thermogravimetric analysis.

Kotei, Patience A.↗
DOE OSTI2013

Effects of error covariance structure on estimation of model averaging weights and predictive performance

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cek resolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

error correlation↗
DOE OSTI2019

Compile-Time Library Call Detection Using CAASCADE and XALT

CAASCADE — Compiler-Assisted Application Source Code Analysis and DatabasE—is a tool that summarizes the use of parallel programming language features in application source code using compiler technology. This paper discusses the library detection capability within CAASCADE to find information about the usage of scientific libraries within the source code. The information that CAASCADE collects provides insights into the usage of library calls in an applications. CAASCADE can classify the APIs by scientific libraries (e.g. LAPACK, BLAS, FFTW, etc). It can also detect the context in which a library API is being invoked, for example within a serial or multi-threaded region. To collect this information, CAASCADE uses compiler plugins that summarize procedural information and uses Apache Spark to do inter-procedural analysis to reconstruct call chains. In addition to this, we also integrated CAASCADE to work with XALT to collect library information based on linkage and modules installed on a system.

Zhao, Jisheng↗
DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗
DOE OSTI2013

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.

Finney, Charles↗
DOE OSTI2014

Fast Multiresolution Reads of Massive Simulation Datasets

Today’s massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. Here, we demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Computational Nuclear Physics and Post Hartree-Fock Methods

We present a computational approach to infinite nuclear matter employing Hartree-Fock theory, many-body perturbation theory and coupled cluster theory. These lectures are closely linked with those of chapters 9, 10 and 11 and serve as input for the correlation functions employed in Monte Carlo calculations in chapter 9, the in-medium similarity renormalization group theory of dense fermionic systems of chapter 10 and the Green's function approach in chapter 11. We provide extensive code examples and benchmark calculations, allowing thereby an eventual reader to start writing her/his own codes. We start with an object-oriented serial code and end with discussions on strategies for porting the code to present and planned high-performance computing facilities.

Lietz, Justin↗