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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 1,063 records · Page 59

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NASA NTRS2012

Real-Time Adaptive Lossless Hyperspectral Image Compression using CCSDS on Parallel GPGPU and Multicore Processor Systems

The proposed CCSDS (Consultative Committee for Space Data Systems) Lossless Hyperspectral Image Compression Algorithm was designed to facilitate a fast hardware implementation. This paper analyses that algorithm with regard to available parallelism and describes fast parallel implementations in software for GPGPU and Multicore CPU architectures. We show that careful software implementation, using hardware acceleration in the form of GPGPUs or even just multicore processors, can exceed the performance of existing hardware and software implementations by up to 11x and break the real-time barrier for the first time for a typical test application.

realtime↗

NASA NTRS2015

Stage-by-Stage and Parallel Flow Path Compressor Modeling for a Variable Cycle Engine

Parallel Flow Path modeling↗

NASA NTRS2015

Stage-by-Stage and Parallel Flow Path Compressor Modeling for a Variable Cycle Engine, NASA Advanced Air Vehicles Program - Commercial Supersonic Technology Project - AeroServoElasticity

This paper covers the development of stage-by-stage and parallel flow path compressor modeling approaches for a Variable Cycle Engine. The stage-by-stage compressor modeling approach is an extension of a technique for lumped volume dynamics and performance characteristic modeling. It was developed to improve the accuracy of axial compressor dynamics over lumped volume dynamics modeling. The stage-by-stage compressor model presented here is formulated into a parallel flow path model that includes both axial and rotational dynamics. This is done to enable the study of compressor and propulsion system dynamic performance under flow distortion conditions. The approaches utilized here are generic and should be applicable for the modeling of any axial flow compressor design accurate time domain simulations. The objective of this work is as follows. Given the parameters describing the conditions of atmospheric disturbances, and utilizing the derived formulations, directly compute the transfer function poles and zeros describing these disturbances for acoustic velocity, temperature, pressure, and density. Time domain simulations of representative atmospheric turbulence can then be developed by utilizing these computed transfer functions together with the disturbance frequencies of interest.

Compressor Modeling Modeling↗

DOE OSTI2016

Dynamic Resource Management for Parallel Tasks in an Oversubscribed Energy-Constrained Heterogeneous Environment

Abstract: The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of such a system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 hours). To maximize system performance when scheduling dynamically arriving parallel tasks onto a high performance computing (HPC) system that is oversubscribed and energy-constrained, we have designed, analyzed, and compared different heuristic techniques. Four utility-aware heuristics (i.e., Max Utility, Max Utility-per-Time, Max Utility-per-Resource, and Max Utility-per-Energy), three FCFS-based heuristics (Conservative Backfilling, EASY Backfilling, and FCFS with Multiple Queues), and a Random heuristic were examined in this study. A technique that is often used with the FCFS-based heuristics is the concept of a permanent reservation. We compare the performance of permanent reservations with temporary place-holders to demonstrate the advantages that place-holders can provide. We also present a novel energy filtering technique that constrains the maximum energy-per-resource used by each task. We conducted a simulation study to evaluate the performance of these heuristics and techniques in an energy-constrained oversubscribed HPC environment. With place-holders, energy filtering, and dropping tasks with low potential utility, our utility-aware heuristics are able to significantly outperform the existing FCFS-based techniques.

Energy-Constrained↗

DOE OSTI2017

Parallel Asynchronous Distributed-Memory Maximal Independent Set Algorithm with Work Ordering

The maximal independent set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of graph data suggests the use of distributed memory hardware as a cost-effective approach to providing necessary compute and memory resources. Existing distributed memory parallel MIS algorithms rely on synchronous communication and use techniques such as subgraph computations. In this paper, we present an asynchronous distributed-memory parallel graph algorithm that relies on a virtual directed acyclic graph (DAG) that is created during the algorithm execution. We introduce two additional algorithms that save computations by ordering generated work. The first algorithm applies ordering globally to reduce computations, and the second algorithm applies ordering locally at the level of threads to minimize the synchronization overhead. We use two different implementations of Luby’s algorithm variants as baseline to compare the performance of the presented algorithms: (1) vertex-centric Luby A and Luby B implementations, and (2) the CombBLAS linear-algebra Luby A implementation. Results show that proposed algorithms outperform both implementations of Luby algorithms, especially in distributed execution. Furthermore, we show that for low-diameter graphs the algorithm that applies global ordering scales better than other algorithms and for high diameter graphs the original asynchronous algorithm and thread-level ordering algorithm show better performance.

maximal independent set (MIS), distributed memory ↗

DOE OSTI2023

An Open-Source Parallel EMT Simulation Framework: Preprint

As the integration level of inverter-based resources (IBR) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗

DOE OSTI2017

Parallel electron heating in the magnetospheric inflow region: Electron Heating in Reconnection

We analyzed 20 reconnection events observed by the Magnetosphere MultiScale mission, finding that in a subregion of the magnetospheric inflow, the electron temperature parallel to the magnetic field (T e|| ) decreases toward the separatrix with increasing densities. Such Te|| variation is not consistent with the heating through the parallel potential mechanism for magnetospheric electrons. Associated with the change in T e|| , low-energy gyrotropic magnetosheath-like electrons are observed. These electrons may come from the magnetosheath, penetrate to the magnetospheric side possibly due to waves in the lower hybrid frequency range, and do not experience as much energization as those from deeper in the magnetosphere. In addition, as T e|| decreases, the high-energy field-aligned phase-space densities decrease or fluctuate significantly over time. Wave-particle interaction might produce energy conversion and cause the high-energy phase-space density variations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Breaking Parallel Orientation of Rods via a Dendritic Architecture toward Diverse Supramolecular Structures

Abstract Self‐assembled nanostructures of rod‐like molecules are commonly limited to nematic or layered smectic structures dominated by the parallel arrangement of the rod‐like components. Distinct self‐assembly behavior of four categories of dendritic rods constructed by placing a tri(hydroxy) group at the apex of dendritic oligo‐fluorenes is observed. Designed hydrogen bonding and dendritic architecture break the parallel arrangement of the rods, resulting in molecules with specific (fan‐like or cone‐like) shapes. While the fan‐shaped molecules tend to form hexagonal packing cylindrical phases, the cone‐shaped molecules could form spherical motifs to pack into various ordered structures, including the Frank–Kasper A15 phase and dodecagonal quasicrystal. This study provides a model system to engineer diverse supramolecular structures by rod‐like molecules and sheds new light into the mechanisms of the formation of unconventional spherical packing structures in soft matter.

Zhang, Ruimeng↗

DOE OSTI2019

Breaking Parallel Orientation of Rods via a Dendritic Architecture toward Diverse Supramolecular Structures

Zhang, Ruimeng↗

DOE OSTI2015

A switch from parallel to antiparallel strand orientation in a coiled-coil X-ray structure via two core hydrophobic mutations

The coiled-coil is one of the most ubiquitous and well studied protein structural motifs. Significant effort has been devoted to dissecting subtle variations of the typical heptad repeat sequence pattern that can designate larger topological features such as relative α-helical orientation and oligomer size. Here in this paper we report the X-ray structure of a model coiled-coil peptide, HA2-Del-L2seM, which forms an unanticipated core antiparallel dimer with potential sites for discrete higher-order multimerization (trimer or tetramer). In the X-ray structure, a third, partially-ordered α-helix is weakly associated with the antiparallel dimer and analytical ultracentrifugation experiments indicate the peptide forms a well-defined tetramer in solution. The HA2-Del-L2seM sequence is closely related to a parent model peptide, HA2-Del, which we previously reported adopts a parallel trimer; HA2-Del-L2seM differs by only hydrophobic leucine to selenomethione mutations and thus this subtle difference is sufficient to switch both relative α-helical topology and number of α-helices participating in the coiled-coil. Comparison of the X-ray structures of HA2-Del-L2seM (reported here) with the HA2-Del parent (reported previously) reveals novel interactions involving the selenomethionine residues that promote antiparallel coiled-coil configuration and preclude parallel trimer formation. Finally, these novel atomic insights are instructive for understanding subtle features that can affect coiled-coil topology and provide additional information for design of antiparallel coiled-coils.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Genetic algorithm based task reordering to improve the performance of batch scheduled massively parallel scientific applications

The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on the performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.

97 MATHEMATICS AND COMPUTING↗

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 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 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↗