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At least 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Adapting CLUTCH methodology to multigroup TSUNAMI-3D for eigenvalue sensitivity calculations

The sensitivity of the eigenvalue to uncertainties in nuclear data and its evaluation are important for nuclear criticality safety. TSUNAMI-3D sequences within the SCALE code system offer several options to the user community for calculating eigenvalue sensitivity coefficients with multigroup (MG) and continuous energy (CE) 3D transport capabilities. TSUNAMI-3D sequences implement the adjoint-based perturbation theory with MG KENO code, the Contributon Linked eigenvalue sensitivity/Uncertainty estimation via Track length importance CHaracterization (CLUTCH) method with CE KENO code, and the Iterated Fission Probability (IFP) method with CE KENO and Shift codes. Each method has benefits and limitations depending on the problem that is run. The work presented here aims to adapt the CLUTCH method, which enables the Contributon method's mesh-free, memory-efficient approach for calculating adjoint-weighted tallies for sensitivity calculations, to the MG TSUNAMI-3D sequence. This application would eliminate the explicit adjoint KENO calculation, as well as the memory-consuming mesh flux moment tallies required by the conventional MG TSUNAMI-3D. Smaller memory footprints in the CLUTCH methodology and relatively shorter runtimes in MG KENO transport can make MG TSUNAMI-3D a viable method for some complex problems. Moreover, this adaptation allows MG sensitivity calculations with Shift, ORNL's next-generation high-performance Monte Carlo transport code, which currently does not offer any sensitivity capabilities with MG particle transport simulations. Initial implementation of the new MG TSUNAMI-3D sequence and its preliminary results with a selected critical benchmark experiment in the Verified, Archived Library of Inputs and Data (VALID) are presented in this study.

KENO↗
DOE OSTI2013

Radioactive Iodine and Krypton Control for Nuclear Fuel Reprocessing Facilities

Nuclear fission results in the production of fission products and activation products, some of which tend to be volatile during used fuel reprocessing. These can evolve in volatile species in the reprocessing facility off-gas streams, depending on the separations and reprocessing technologies that are used. Radionuclides that have been identified as “volatile radionuclides” are noble gases (most notably isotopes of Kr and Xe); 3H; 14C; and 129I. Radionuclides that tend to form volatile species that evolve into reprocessing facility off-gas systems are more challenging to efficiently control compared to radionuclides that tend to stay in solid or liquid phases. Future used fuel reprocessing facilities in the United States can require efficient capture of some volatile radionuclides in their off-gas streams to meet regulatory emission requirements. In aqueous reprocessing, these radionuclides are most commonly expected to evolve into off-gas streams in tritiated water [3H2O (T2O) and 3HHO (THO)], radioactive CO2, noble gases, and gaseous HI, I2, or volatile organic iodides. The fate and speciation of these radionuclides from a non-aqueous fuel reprocessing facility is less well known at this time, but active investigations are in progress. An Off-Gas Sigma Team was formed in late FY 2009 to integrate and coordinate the Fuel Cycle Research and Development (FCR&D) activities directed towards the capture and sequestration of the these volatile radionuclides (Jubin 2012a). The Sigma Team concept was envisioned to bring together multidisciplinary teams from across the DOE complex that would work collaboratively to solve the technical challenges and to develop the scientific basis for the capture and immobilization technologies such that the sum of the efforts was greater than the individual parts. The Laboratories currently participating in this effort are Argonne National Laboratory (ANL), Idaho National Laboratory (INL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), and Sandia National Laboratories (SNL). This article focuses on control of volatile radionuclides that evolve during aqueous reprocessing of UNF. In particular, most of the work by the Off-gas Sigma Team has focused on the capture and sequestration of 129I and 85Kr, mainly because, as discussed below, control of 129I can require high efficiencies to meet regulatory requirements, and control of 85Kr using cryogenic processing, which has been the technology demonstrated and used commercially to date, can add considerable cost to a reprocessing facility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

SystemConfidence

SystemConfidence is a benchmark developed at ORNL which can measure statistical variation in which the user can plot. The portions of the code which manage the collection of the histograms and computing statistics on the histograms were designed with the intent that we could use these functions in other codes.

Josh Lothian, JeffKuehn↗
DOE OSTI2013

Spot-Hadoop

Set of scripts (Python and Bash) to help users configure, run, and benchmark Hadoop clusters on ORNL computing infrastructure.

Horey, James↗
DOE OSTI2018

ExaTN

A challenging scientific problem that will benefit from exascale computing is predictive simulation of two- and three-dimensional quantum many-body Hamiltonians in condensed matter physics, together with the related strong entanglement problems in quantum chemistry and quantum computing. Efficient numerical simulations will require new insights into wavefunction compression algorithms that scale on extremely heterogeneous HPC architectures. To address this need, we propose to develop a scalable math library of parallel multi-linear algebraic numerical primitives for hierarchical tensor compression. Our library will subsequently be used for building an efficient, performance-portable HPC framework for simulating strongly correlated quantum systems. In particular, our will will be the first to deliver a massively parallel implementation of the multiscale entanglement renormalization ansatz (MERA), a hierarchical tensor decomposition quantum scheme capable of expressing local expectation values of strongly entangled higher-dimensional quantum systems efficiently. To address the extreme complexity and high computational cost of such schemes, we will automate their parallel implementation by delivering an integrated software framework, ExaTN (Exascale Tensor Networks), that will process hierarchical tensor representations on exascale HPC systems via an asymchronous task-based programming model. As a specific application, our framework will provide scientists with a capability of simulating strongly entangled quantum systems, thus positioning ORNL as a strategic leader in modeling of strongly correlated materials.

McCaskey, AlexanderJ↗
DOE OSTI2018

PathRepHAN: Hierarchical attention networks for pathology report classification

Developers at ORNL explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1–G4. Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques.

Ramanathan, Arvind↗
DOE OSTI2021

Soar Survey Scoring

ORNL conducted an experiment to test COTS (commercial off the shelf) SOAR tools. Vendors interested in entering their tool into the experiment were asked to submit information videos about their tool for phase 1 to down-select which tools should make it through to the testing phase. SOC operators were assigned to watch a subset of the videos such that each video was assigned to the same number of operators. After watching all the videos, operators completed surveys which asked them to grade basic aspects about the tool as best as they could based on the provided video, and asked them to rank the order of their interest in the vendors they were assigned to watch videos on. The problem solved was how do we aggregate the results of the surveys and each of the operator's ranked lists into an overall ranking of all the tools. In addition, it was desirable to be able to work on this code before final results were collected from operators, so code to generate fake data was developed to output in the same output as the survey results.

Bridges, Robert↗
DOE OSTI2023

MOOSE-Python Binder

This code is a wrapper for Python code so that it can be accessed by MOOSE and the two can pass variables back and forth. Since this code was developed by ORNL, it must be independently approved before being added to the MOOSE repository.

Cheniour, Amani↗
DOE OSTI2021

NWQ-sim

NWQSim is a quantum circuit simulation environment developed at PNNL. It currently includes two major components: a state-vector simulator (SV-Sim) and a density matrix simulator (DM-Sim) and we may add more components, such as a Clifford simulator, in the future effort. NWQSim has two language interface: C/C++ and Python. It supports Q#/QDK frontend through QIR and QIR-runtime. It supports Qiskit and Cirq frontends through OpenQASM. NWQSim runs on several backends: Intel-CPU, Intel-Xeon-Phi, AMD-CPU, AMD-GPU, NVIDIA-GPU, and IBM-CPU. It supports three modes: (1) single processor, such as a single CPU (with and without AVX2 and AVX512 acceleration), a single NVIDIA GPU or a single AMD GPU; (2) single-node-multi-processors, such as multi-CPUs/Xeon-Phis, multi-NVIDA/AMD GPUs; (3) multi-nodes, such as a CPU cluster, a Xeon-Phi cluster (e.g., ANL Theta, NERSC Cori), an NVIDIA cluster (e.g., ORNL Summit, NERSC Perlmutter).

Li, Ang↗
DOE OSTI2024

ENDF/B-VIII.1: Neutron Reaction Sublibrary

The neutrons sublibrary aims to describe nuclear reactions between incident neutron particles and different nuclei. For ENDF/B-VIII.1, many neutron files were re-evaluated or received major changes. A new 239Pu evaluation was jointly-produced by IAEA, LANL, LLNL and ORNL bringing important updates to fission neutron multiplicity, Prompt Fission Neutron Spectrum, resonance and fast regions. Around one third of the new neutron evaluations were performed as part of the INDEN collaboration, including 16,18 O, 19 F, 28,29,30 Si, 63,65 Cu, 50,51,52,53,54 Cr, 55 Mn, 54,56,57 Fe, 139 La, 233,235,238 U, 240,241 Pu. Important non-INDEN evaluations include 234,236 U, 206,207,208 Pb, 181 Ta, 88 Sr, 140,142 Ce, Pt and Dy isotopes, and many others. Also, dosimetry reactions from IRDFF-II were adopted for many materials.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Evaluating asynchronous Schwarz solvers on GPUs

With the commencement of the exascale computing era, we realize that the majority of the leadership supercomputers are heterogeneous and massively parallel. Even a single node can contain multiple co-processors such as GPUs and multiple CPU cores. For example, ORNL’s Summit accumulates six NVIDIA Tesla V100 GPUs and 42 IBM Power9 cores on each node. Synchronizing across compute resources of multiple nodes can be prohibitively expensive. Hence, it is necessary to develop and study asynchronous algorithms that circumvent this issue of bulk-synchronous computing. In this study, we examine the asynchronous version of the abstract Restricted Additive Schwarz method as a solver. We do not explicitly synchronize, but allow the communication between the sub-domains to be completely asynchronous, thereby removing the bulk synchronous nature of the algorithm. We accomplish this by using the one-sided Remote Memory Access (RMA) functions of the MPI standard. We study the benefits of using such an asynchronous solver over its synchronous counterpart. We also study the communication patterns governed by the partitioning and the overlap between the sub-domains on the global solver. Finally, we show that this concept can render attractive performance benefits over the synchronous counterparts even for a well-balanced problem.

Nayak, Pratik↗
DOE OSTI2021

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗
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↗
DOE OSTI2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research

BackgroundCurrent multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines.ResultsThis paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks.ConclusionsInitial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Graduate Training at the Interface of Computational and Experimental Biology: An Outcome Report from a Partnership of Volunteers between a University and a National Laboratory

Leading voices in the biological sciences have called for a transformation in graduate education leading to the PhD degree. One area commonly singled out for growth and innovation is cross-training in computational science. In 1998, the University of Tennessee (UT) founded an intercollegiate graduate program called the UT-ORNL Graduate School of Genome Science and Technology in partnership with the nearby Oak Ridge National Laboratory. Here, we report outcome data that attest to the program’s effectiveness in graduating computationally enabled biologists for diverse careers. Among 77 PhD graduates since 2003, the majority came with traditional degrees in the biological sciences, yet two-thirds moved into computational or hybrid (computational–experimental) positions. We describe the curriculum of the program and how it has changed. We also summarize how the program seeks to establish cohesion between computational and experimental biologists. This type of program can respond flexibly and dynamically to unmet training needs. In conclusion, this study from a flagship, state-supported university may serve as a reference point for creating a stable, degree-granting, interdepartmental graduate program in computational biology and allied areas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Toward more mechanistic representations of biogeochemical processes in stream and river networks: Implementation and demonstration of a multiscale model : Modeling Archive

This Modeling Archive supports the ORNL-SFA and IDEAS-Waterhsed publication submitted to Environmental Modeling and Software. A recently introduced multiscale model for representing the combined effect of hyporheic exchange flows and small-scale hyporheic-zone biogeochemical processes is extended from the reach scale to river network scales. The ADELS (Advection Dispersion Equation with Lagrangian Subgrid) model uses a one-dimensional advection-dispersion-reaction equation for the channel network and couples that equation at each channel location to a one-dimensional advection-reaction subgrid model representing an ensemble of streamlines that are diverted into the hyporheic zone before returning to the channel. The subgrid model is written in a computationally advantageous Lagrangian form with hyporheic age replacing the hyporheic travel distance. In the paper, we summarized implementation in the integrated surface/subsurface hydrology modeling system Advanced Terrestrial Simulator (ATS).

East Fork Poplar Creek↗