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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A performance spectrum for parallel computational frameworks that solve PDEs

Summary Important computational physics problems are often large‐scale in nature, and it is highly desirable to have robust and high performing computational frameworks that can quickly address these problems. However, it is no trivial task to determine whether a computational framework is performing efficiently or is scalable. The aim of this paper is to present various strategies for better understanding the performance of any parallel computational frameworks for solving PDEs. Important performance issues that negatively impact time‐to‐solution are discussed, and we propose a performance spectrum analysis that can enhance one's understanding of critical aforementioned performance issues. As proof of concept, we examine commonly used finite element simulation packages and software and apply the performance spectrum to quickly analyze the performance and scalability across various hardware platforms, software implementations, and numerical discretizations. It is shown that the proposed performance spectrum is a versatile performance model that is not only extendable to more complex PDEs such as hydrostatic ice sheet flow equations but also useful for understanding hardware performance in a massively parallel computing environment. Potential applications and future extensions of this work are also discussed.

Chang, J.↗
DOE OSTI2018

A PETSc Parallel-in-Time Solver based on MGRIT algorithm

We address the development of a modular implementation of the MGRIT (MultiGrid-In-Time) algorithm to solve linear and nonlinear systems that arise from the discretization of evolutionary models with a parallel-in-time approach in the context of the PETSc (the Portable, Extensible Toolkit for Scientific computing) library. Our aim is to give the opportunity of predicting the performance gain achievable when using the MGRIT approach instead of the Time Stepping integrator (TS). To this end, we analyze the performance parameters of the algorithm that provide a-priori the best number of processing elements and grid levels to use to address the scaling of MGRIT, regarded as a parallel iterative algorithm proceeding along the time dimension.

Mele, Valeria↗
DOE OSTI2019

Parallel membership queries on very large scientific data sets using bitmap indexes

© 2019 John Wiley & Sons, Ltd. Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating-point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word-Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

Yildiz, Beytullah↗
DOE OSTI2020

OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems

This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions.

Zheng, Weijian↗
DOE OSTI2013

A 2nd Generation Parallel Advancing Front Grid Generator

A scalable, parallel advancing grid generation technique has been developed for complex geometries and meshes with large size variations. The key innovation compared to previous techniques is the use of a domain-defining grid that has the same fine surface triangulation as the final mesh desired, but a much coarser interior mesh. In this way, the domain to be gridded is uniquely defined, overcoming a shortcoming of previous approaches. This domain-defining grid is then partitioned according to the estimated number of elements to be generated, allowing for a balanced distribution of work among the processors. The domain defining grid is also used to redistribute the elements and points after grid generation, and during the subsequent mesh improvement. Timings show that the proposed approach is scalable and able to produce large grids of high quality in a modest amount of clocktime. With the proposed parallel grid generator, a major impediment to a completely scalable simulation pipeline (grid generation, solvers, post-processing) has been removed, opening the way for truly large-scale computations using unstructured, body-fitted grids.

Grid Generation↗
DOE OSTI2013

ViennaX: a parallel plugin execution framework for scientific computing

We present the free open source plugin execution framework ViennaX for modularizing and parallelizing scientific simulations. In general, functionality is abstracted by the notion of a task, which is implemented as a plugin. The plugin system facilitates the utilization of both, already available functionality as well as new implementations. Each task can define arbitrary data dependencies which are used by ViennaX to build a task graph. The framework supports the execution of this dependence graph based on the message passing interface in either a serial or a parallel fashion. The applied modular approach allows for defining highly flexible simulations, as plugins can be easily exchanged. The framework's general design as well as implementation details are discussed. Applications based on the Mandelbrot set and the solution of a partial differential equation are investigated, and performance results are shown.

Weinbub, Josef↗
DOE OSTI2016

Parallel anisotropic mesh adaptation with boundary layers for automated viscous flow simulations

This paper presents a set of parallel procedures for anisotropic mesh adaptation accounting for mixed element types used in boundary layer meshes, i.e., the current procedures operate in parallel on distributed boundary layer meshes. The procedures accept anisotropic mesh metric field as an input for the desired mesh size field and apply local mesh modifications to adapt the mesh to match/satisfy the specified mesh size field. The procedures fully account for the parametric geometry of curved domains and maintain the semi-structured nature of the boundary layer elements. The effectiveness of the procedures is demonstrated on three viscous flow examples that include the ONERA M6 wing, a heat transfer manifold, and a scramjet engine.

Sahni, Onkar↗
DOE OSTI2013

Parallel Radioisotope Collection and Analysis in Response to the Fukushima Release

Two independent air samplers were operated at Pacific Northwest National Laboratory in parallel during the collection of samples from the Fukushima reactor releases. One system is an automated aerosol collection and analysis unit, while the other was a manual sampler of higher daily air volume. The samples collected each day showed excellent correlation in radionuclide activity, although some variations were seen. These variations illustrate the reproducibility of the air sample radionuclide measurements made by the Comprehensive Nuclear-Test-Ban Treaty International Monitoring System (IMS) and show a simple way to acquire useful parallel samples for scientific purposes. In particular, a party wishing to have a “copy” of a sample acquired by the verification regime of the treaty could employ this method and have results similar to the IMS station at low cost and even higher sensitivity.

Atmospheric Aerosol Sampling↗
DOE OSTI2026

Commuting embeddings for parallel strategies in non-local games

Non-local games provide a versatile framework for probing quantum correlations and for benchmarking the power of entanglement. In finite dimensions, the standard method for playing several games in parallel requires a tensor product of the local Hilbert spaces, which scales additively in the number of qubits. In this work, we show that this additive cost can be reduced by exploiting algebraic embeddings. We introduce two forms of compressions. First, when a referee selects one game from a finite collection of games at random, the game quantum strategy can be implemented using a maximally entangled state of dimension equal to the largest individual game, thereby eliminating the need for repeated state preparations. Second, we establish conditions under which several games can be played simultaneously in parallel on fewer qubits than the tensor product baseline. These conditions are expressed in terms of commuting embeddings of the game algebras. Moreover, we provide a constructive framework for building such embeddings. Using tools from Lie theory, we show that aligning the various game algebras into a common Cartan decomposition enables such a qubit reduction. Beyond the theoretical contribution, our framework casts NLGs as algebraic primitives for distributed and resource-constrained quantum computations and suggested NLGs as a comparable device-independent dimension witness.

Commuting embeddings↗
DOE OSTI2016

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte Carlo ideas. The efficiency of standard SAA algorithm crucially depends on its selfadjusting mechanism which presents stability issues in high dimensional or rugged optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that significantly improves the stability of the self-adjusting mechanism and the search for the global optimum in the sampling space, as well as it inherits SAA desired convergence properties when a square-root cooling schedule is used. It can be implemented in parallel computing environments in order to mitigate the computational overhead. As a result, PISAA can address complex optimisation problems that it would be difficult for SAA to satisfactory address. We demonstrate the good performance of the proposed algorithm on challenging applications including Bayesian network learning and protein folding. Our numerical comparisons suggest that PISAA outperforms the simulated annealing, stochastic approximation annealing, and annealing evolutionary stochastic approximation Monte Carlo.

Local trap↗
DOE OSTI2017

Parallel Spectral Acquisition with Orthogonal ICR Cells

FT-based high performance mass analyzers yield increased resolving power and mass measurement accuracy, yet require increased duration of signal acquisition that can limit many applications. The implementation of stronger magnetic fields, multiple detection electrodes for harmonic signal detection, and an array of multiple mass analyzers arranged along the magnetic field axis have been used to decrease required acquisition time. The results presented here show that multiple ion cyclotron resonance (ICR) mass analyzers can also be implemented orthogonal to the central magnetic field axis. The orthogonal ICR cell system presented here consisting of two cells (master and slave cells) was constructed with printed circuit boards and installed within a single superconducting magnet and vacuum system. A master cell was positioned, as is normally done with ICR cells, on the central magnetic field axis and a slave cell was located off this central axis, but directly adjacent and alongside the master cell. To achieve ion transfer between cells, ions that were initially trapped in the master cell were drifted across the magnetic field into the slave cell with application of a small DC field applied perpendicularly to the magnetic field axis. A subsequent population of ions was injected and accumulated in the master cell. Simultaneous excitation of cyclotron motion of ions in both cells was carried out; ICR signals from each cell were independently amplified and recorded in parallel. Presented here are the initial results of successful parallel spectral acquisition with this orthogonal dual ICR cell array. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

A new method approximating offset curve by Bézier curve using parallel derivative curves

In this paper, we present a new method for the offset approximation of an n-th degree Bézier curve by an n+1-th degree Bézier curve using parallel derivative curves. Our method is based on the Gauss map approximation of the n-th degree Bézier curve by the n+1-th degree Bézier curve whose derivative curve is parallel to that of the n-th degree Bézier curve. Thus, the convolution of the Gauss map approximation curve and n-th Bézier curve is a polynomial of degree n+1, which is our offset approximation of the n-th degree Bézier curve. Our approximation method has two advantages. One is that the offset approximation method yields a non-rational (polynomial) curve which is obtained by the simple sum of two Bézier curves. The other is that the offset approximation curve satisfying the endpoint interpolation of the given offset curve automatically has the same tangent lines as the offset curve at both endpoints. We illustrate our offset approximation method with some numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Parallelized hybrid optimization methods for nonsmooth problems using NOMAD and linesearch

Two parallelized hybrid methods are presented for single-function optimization problems with side constraints. The optimization problems are difficult not only due to possible existence of local minima and nonsmoothness of functions, but also due to the fact that objective function and constraint values for a solution vector can only be obtained by querying a black box whose execution requires considerable computational effort. Examples are optimization problems in Engineering where objective function and constraint values are computed via complex simulation programs, and where local minima exist and smoothness of functions is not assured. The hybrid methods consist of the well-known method NOMAD and two new methods called DENCON and DENPAR that are based on the linesearch scheme CS-DFN. The hybrid methods compute for each query a set of solution vectors that are evaluated in parallel. The hybrid methods have been tested on a set of difficult optimization problems produced by a certain seeding scheme for multiobjective optimization. We compare computational results with solution by NOMAD, DENCON, and DENPAR as stand-alone methods. It turns out that among the stand-alone methods, NOMAD is significantly better than DENCON and DENPAR. However, the hybrid methods are definitely better than NOMAD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Extending TOUGH + HYDRATE with a parallel particle transport simulator: numerical investigation of sand production during gas production from hydrate deposits

A new parallel code for simulating particle transport in porous media is integrated with the TOUGH + HYDRATE simulator to investigate sand production associated with gas production from unconsolidated gas hydrate-bearing sediments (HBS). Here, the parallel coupled simulator is named THMPT and uses the integral finite difference method to describe the Darcian and non-Darcian flow of fluids and heat transport, the finite element method to describe the associated geomechanical changes, and the discrete element method to track the trajectory of individual sand particles within the HBS. The THMPT simulator is written in Fortran, incorporates multiple optimized algorithms, and can comprehensively address the coupled flow, thermal, chemical, geomechanical, and particle transport processes that characterize the system behaviors during gas production from HBS. The simulator can capture all processes involved in sand particle transport in porous media, including sand detachment, collision, clogging (i.e., bridging), and migration. A benchmark case study of sand production in the course of depressurization-induced gas production from a representative HBS reveals various distinct microscopic particle migration mechanisms and the adverse impact of sand particle detachment, transport, and clogging. The numerical investigation also examines the effect of bottomhole pressure on mitigating sand production. The simulation results indicate that sand clogging near the wellbore significantly reduces permeability, decreasing gas production by at least 50%. Lastly, the efficiency of gravel packing in mitigating sand production is numerically evaluated, revealing that the structure of the porous media appears to profoundly influence the macroscopic motion behavior of sand particles and sand clogging characteristics.

discrete element method↗
DOE OSTI2021

Asynchronous distributed-memory task-parallel algorithm for compressible flows on unstructured 3D Eulerian grids

Here, we discuss the implementation of a finite element method, used to numerically solve the Euler equations of compressible flows, using an asynchronous runtime system (RTS). The algorithm is implemented for distributed-memory machines, using stationary unstructured 3D meshes, combining data-, and task-parallelism on top of the Charm++ RTS. Charm++’s execution model is asynchronous by default, allowing arbitrary overlap of computation and communication. Task-parallelism allows scheduling parts of an algorithm independently of, or dependent on, each other. Built-in automatic load balancing enables continuous redistribution of computational load by migration of work units based on real-time CPU load measurement. The RTS also features automatic checkpointing, fault tolerance, resilience against hardware failure, and supports power-, and energy-aware computation. We demonstrate scalability up to 25 x 10 9 cells at $\mathscr{O}$10 4 compute cores and the benefits of automatic load balancing for irregular workloads. The full source code with documentation is available at https://quinoacomputing.org.

42 ENGINEERING↗
DOE OSTI2013

The parallel reaction monitoring method contributes to a highly sensitive polyubiquitin chain quantification

Highlights: •The parallel reaction monitoring method was applied to ubiquitin quantification. •The ubiquitin PRM method is highly sensitive even in biological samples. •Using the method, we revealed that Ufd4 assembles the K29-linked ubiquitin chain. -- Abstract: Ubiquitylation is an essential posttranslational protein modification that is implicated in a diverse array of cellular functions. Although cells contain eight structurally distinct types of polyubiquitin chains, detailed function of several chain types including K29-linked chains has remained largely unclear. Current mass spectrometry (MS)-based quantification methods are highly inefficient for low abundant atypical chains, such as K29- and M1-linked chains, in complex mixtures that typically contain highly abundant proteins. In this study, we applied parallel reaction monitoring (PRM), a quantitative, high-resolution MS method, to quantify ubiquitin chains. The ubiquitin PRM method allows us to quantify 100 attomole amounts of all possible ubiquitin chains in cell extracts. Furthermore, we quantified ubiquitylation levels of ubiquitin-proline-β-galactosidase (Ub-P-βgal), a historically known model substrate of the ubiquitin fusion degradation (UFD) pathway. In wild-type cells, Ub-P-βgal is modified with ubiquitin chains consisting of 21% K29- and 78% K48-linked chains. In contrast, K29-linked chains are not detected in UFD4 knockout cells, suggesting that Ufd4 assembles the K29-linked ubiquitin chain(s) on Ub-P-βgal in vivo. Thus, the ubiquitin PRM is a novel, useful, quantitative method for analyzing the highly complicated ubiquitin system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Structural basis for cargo binding and autoinhibition of Bicaudal-D1 by a parallel coiled-coil with homotypic registry

Bicaudal-D1 (BICD1) is an α-helical coiled-coil protein mediating the attachment of specific cargo to cytoplasmic dynein. It plays an essential role in minus end-directed intracellular transport along microtubules. The third C-terminal coiled-coil region of BICD1 (BICD1 CC3) has an important role in cargo sorting, including intracellular vesicles associating with the small GTPase Rab6 and the nuclear pore complex Ran binding protein 2 (RanBP2), and inhibiting the association with cytoplasmic dynein by binding to the first N-terminal coiled-coil region (CC1). The crystal structure of BICD1 CC3 revealed a parallel homodimeric coiled-coil with asymmetry and complementary knobs-into-holes interactions, differing from Drosophila BicD CC3. Furthermore, our binding study indicated that BICD1 CC3 possesses a binding surface for two distinct cargos, Rab6 and RanBP2, and that the CC1-binding site overlaps with the Rab6-binding site. These findings suggest a molecular basis for cargo recognition and autoinhibition of BICD proteins during dynein-dependent intracellular retrograde transport. - Highlights: • BICD1 CC3 is a parallel homodimeric coiled-coil with axial asymmetry. • The coiled-coil packing of BICD1 CC3 is adapted to the equivalent heptad position. • BICD1 CC3 has distinct binding sites for two classes of cargo, Rab6 and RanBP2. • The CC1-binding site of BICD1 CC3 overlaps with the Rab6-binding site.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A fast parallel 3D Poisson solver with longitudinal periodic and transverse open boundary conditions for space-charge simulations

A three-dimensional (3D) Poisson solver with longitudinal periodic and transverse open boundary conditions can have important applications in beam physics of particle accelerators. Here, we present a fast efficient method to solve the Poisson equation using a spectral finite-difference method. This method uses a computational domain that contains the charged particle beam only and has a computational complexity of $O(N_u (logN_{mode}))$, where $N_u$ is the total number of unknowns and $N_{mode}$ is the maximum number of longitudinal or azimuthal modes. This saves both the computational time and the memory usage of using an artificial boundary condition in a large extended computational domain. The new 3D Poisson solver is parallelized using a message passing interface (MPI) on multi-processor computers and shows a reasonable parallel performance up to hundreds of processor cores.

97 MATHEMATICS AND COMPUTING↗