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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Resident Block-Structured Adaptive Mesh Refinement on Thousands of Graphics Processing Units, In: 2015 44th International Conference on Parallel Processing

Block-structured adaptive mesh refinement (AMR) is a technique that can be used when solving partial differential equations to reduce the number of cells necessary to achieve the required accuracy in areas of interest. These areas (shock fronts, material interfaces, etc.) are recursively covered with finer mesh patches that are grouped into a hierarchy of refinement levels. Despite the potential for large savings in computational requirements and memory usage without a corresponding reduction in accuracy, AMR adds overhead in managing the mesh hierarchy, adding complex communication and data movement requirements to a simulation. In this paper, we describe the design and implementation of a resident GPU-based AMR library, including: the classes used to manage data on a mesh patch, the routines used for transferring data between GPUs on different nodes, and the data-parallel operators developed to coarsen and refine mesh data. We validate the performance and accuracy of our implementation using three test problems and two architectures: an 8 node cluster, and 4,196 nodes of Oak Ridge National Laboratory's Titan supercomputer. Our GPU-based AMR hydrodynamics code performs up to 4.87× faster than the CPU-based implementation, and is scalable on 4,196 K20x GPUs using a combination of MPI and CUDA.

Computer Science↗
DOE OSTI2014

WE-E-BRE-05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk

Purpose: We propose a prior knowledge-based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier. Methods: We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow-up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre-RT) and during RT (mid-RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dose-volumetric parameters were also included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1-regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature-based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)-based RP risk classifier. Results: The LASSO selected the following 7 RP covariates: (1) pre-RT concentration level of α2M, (2) α2M level mid- RT/pre-RT, (3) pre-RT IL6 level, (4) IL6 level mid-RT/pre-RT, (5) ACE mid-RT/pre-RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50. Conclusion: We demonstrated a probabilistic ensemble method to detect robust associations between RP covariates and its potential to improve RP prediction accuracy. Our Bayesian approach to incorporate prior knowledge can enhance efficiency in searching of such associations from data. The authors acknowledge partial support by: 1) CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and 2) The Terry Fox Foundation Strategic Training Initiative for Excellence in Radiation Research for the 21st Century (EIRR21)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-201-11: Sensitivity and Specificity of Verification Methods Based On Total Reference Air Kerma (TRAK) Or On User Provided Dose Points for Graphically Planned Skin HDR Brachytherapy

Purpose: To investigate the sensitivity and specificity of a novel verification methodology for image-guided skin HDR brachytherapy plans using a TRAK-based reasonableness test, compared to a typical manual verification methodology. Methods: Two methodologies were used to flag treatment plans necessitating additional review due to a potential discrepancy of 3 mm between planned dose and clinical target in the skin. Manual verification was used to calculate the discrepancy between the average dose to points positioned at time of planning representative of the prescribed depth and the expected prescription dose. Automatic verification was used to calculate the discrepancy between TRAK of the clinical plan and its expected value, which was calculated using standard plans with varying curvatures, ranging from flat to cylindrically circumferential. A plan was flagged if a discrepancy >10% was observed. Sensitivity and specificity were calculated using as a criteria for true positive that >10% of plan dwells had a distance to prescription dose >1 mm different than prescription depth (3 mm + size of applicator). All HDR image-based skin brachytherapy plans treated at our institution in 2013 were analyzed. Results: 108 surface applicator plans to treat skin of the face, scalp, limbs, feet, hands or abdomen were analyzed. Median number of catheters was 19 (range, 4 to 71) and median number of dwells was 257 (range, 20 to 1100). Sensitivity/specificity were 57%/78% for manual and 70%/89% for automatic verification. Conclusion: A check based on expected TRAK value is feasible for irregularly shaped, image-guided skin HDR brachytherapy. This test yielded higher sensitivity and specificity than a test based on the identification of representative points, and can be implemented with a dedicated calculation code or with pre-calculated lookup tables of ideally shaped, uniform surface applicators.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

DaMoScope and its internet graphics for the visual control of adjusting mathematical models describing experimental data

The experience of using the dynamic atlas of the experimental data and mathematical models of their description in the problems of adjusting parametric models of observable values depending on kinematic variables is presented. The functional possibilities of an image of a large number of experimental data and the models describing them are shown by examples of data and models of observable values determined by the amplitudes of elastic scattering of hadrons. The Internet implementation of an interactive tool DaMoScope and its interface with the experimental data and codes of adjusted parametric models with the parameters of the best description of data are schematically shown. The DaMoScope codes are freely available.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Tensor Algebra Library for NVidia Graphics Processing Units

This is a general purpose math library implementing basic tensor algebra operations on NVidia GPU accelerators. This software is a tensor algebra library that can perform basic tensor algebra operations, including tensor contractions, tensor products, tensor additions, etc., on NVidia GPU accelerators, asynchronously with respect to the CPU host. It supports a simultaneous use of multiple NVidia GPUs. Each asynchronous API function returns a handle which can later be used for querying the completion of the corresponding tensor algebra operation on a specific GPU. The tensors participating in a particular tensor operation are assumed to be stored in local RAM of a node or GPU RAM. The main research area where this library can be utilized is the quantum many-body theory (e.g., in electronic structure theory).

Liakh, Dmitry↗
DOE OSTI2019

WinGridder-A graphic grid generator for TOUGH2 simulators (WinGridder) v3.0

WINGRIDDER V3.0 is a Windows-based software for designing and generating numerical grids for numerical simulators that are based on the "integral finite difference" or the "control volume" numerical scheme (e.g., TOUGH2, Pruess et al., 1996). The user can design and generate grid that properly represents the stratigraphic features, inclined faults, and repository. WINGRIDDER V3.0 is an upgrade from WINGRIDDER V2.0. This revision includes testable requirements as listed in the Requirements Document (RD), 10024-RD-3.0-00, Section 2. With new features, WINGRIDDER V3.0 adds the ability to generate a multiple-interactive-continuum (MINC) grid.

Pan, Lehua↗
DOE OSTI2025

Graphical Model For Network Anomaly Detection

The Graph Neural Network model computes a graph based view of the network logs and detects anomalous traffic within the local graph context.

Quach, Anna [Idaho National Laboratory (INL), Idah↗
DOE OSTI2023

HipBone: A performance-portable graphics processing unit-accelerated C++ version of the NekBone benchmark

We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.

Computer Science↗
DOE OSTI2024

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗