SEARCH · Engineering Papers

Results for “parallel performance”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38

All sources NASA NTRS DOE OSTI

NASA NTRS1986

Isolated and interacting round parallel heated jets

An experimental study of the flowfield of heated and unheated single and dual jet configurations was performed. This study of two parallel jets is unique since most previous aerodynamic structure experiments were limited to single round and two-dimensional jets. The present closely spaced dual jet geometry was motivated by the potential jet noise reduction available from this configuration. This geometry has shown promise as a method for redirecting jet noise away from ground based observers in side by side or over/under turbofan engine mountings on aircraft (Simonich et al., 1984). Since the effectiveness of this noise reduction technique is based on the existence of two independent jets, an understanding of the aerodynamics of the merging process is essential to establishing the acoustic benefits. The experimental program was structured so that Mach number, jet exit temperature, and spacing to diameter ratios could be independently varied to isolate each effect.

Simonich, J. C.↗

DOE OSTI2022

Synthetic Nuances to Maximize n-Type Organic Electrochemical Transistor and Thermoelectric Performance in Fused Lactam Polymers

A series of fully fused n-type mixed conduction lactam polymers p(g 7 NC n N), systematically increasing the alkyl side chain content, are synthesized via an inexpensive, nontoxic, precious-metal-free aldol polycondensation. Employing these polymers as channel materials in organic electrochemical transistors (OECTs) affords state-of-the-art n-type performance with p(g 7 NC 10 N) recording an OECT electron mobility of 1.20 x 10 -2 cm 2 V -1 s -1 and a μC* figure of merit of 1.83 F cm -1 V -1 s -1. In parallel to high OECT performance, upon solution doping with (4-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)phenyl)dimethylamine (N-DMBI), the highest thermoelectric performance is observed for p(g 7 NC 4 N), with a maximum electrical conductivity of 7.67 S cm -1 and a power factor of 10.4 μWm -1 K -2 . These results are among the highest reported for n-type polymers. Importantly, while this series of fused polylactam organic mixed ionic-electronic conductors (OMIECs) highlights that synthetic molecular design strategies to bolster OECT performance can be translated to also achieve high organic thermoelectric (OTE) performance, a nuanced synthetic approach must be used to optimize performance. Herein, we outline the performance metrics and provide new insights into the molecular design guidelines for the next generation of high-performance n-type materials for mixed conduction applications, presenting for the first time the results of a single polymer series within both OECT and OTE applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1994

Some Problems and Solutions in Transferring Ecosystem Simulation Codes to Supercomputers

Many computer codes for the simulation of ecological systems have been developed in the last twenty-five years. This development took place initially on main-frame computers, then mini-computers, and more recently, on micro-computers and workstations. Recent recognition of ecosystem science as a High Performance Computing and Communications Program Grand Challenge area emphasizes supercomputers (both parallel and distributed systems) as the next set of tools for ecological simulation. Transferring ecosystem simulation codes to such systems is not a matter of simply compiling and executing existing code on the supercomputer since there are significant differences in the system architectures of sequential, scalar computers and parallel and/or vector supercomputers. To more appropriately match the application to the architecture (necessary to achieve reasonable performance), the parallelism (if it exists) of the original application must be exploited. We discuss our work in transferring a general grassland simulation model (developed on a VAX in the FORTRAN computer programming language) to a Cray Y-MP. We show the Cray shared-memory vector-architecture, and discuss our rationale for selecting the Cray. We describe porting the model to the Cray and executing and verifying a baseline version, and we discuss the changes we made to exploit the parallelism in the application and to improve code execution. As a result, the Cray executed the model 30 times faster than the VAX 11/785 and 10 times faster than a Sun 4 workstation. We achieved an additional speed-up of approximately 30 percent over the original Cray run by using the compiler's vectorizing capabilities and the machine's ability to put subroutines and functions "in-line" in the code. With the modifications, the code still runs at only about 5% of the Cray's peak speed because it makes ineffective use of the vector processing capabilities of the Cray. We conclude with a discussion and future plans.

Skiles, J. W.↗

DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗

NASA NTRS1990

Solving unstructured grid problems on massively parallel computers

A highly parallel graph mapping technique that enables one to efficiently solve unstructured grid problems on massively parallel computers is presented. Many implicit and explicit methods for solving discretized partial differential equations require each point in the discretization to exchange data with its neighboring points every time step or iteration. The cost of this communication can negate the high performance promised by massively parallel computing. To eliminate this bottleneck, the graph of the irregular problem is mapped into the graph representing the interconnection topology of the computer such that the sum of the distances that the messages travel is minimized. It is shown that using the heuristic mapping algorithm significantly reduces the communication time compared to a naive assignment of processes to processors.

Hammond, Steven W.↗

NASA NTRS2015

Between a Map and a Data Rod

A Digital Divide has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or maps) and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported data rods project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectivesconstraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly (virtual) data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a years worth of time series for hourly data (9,000 time steps) in 90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

machine learning↗

DOE OSTI2020

Massively parallel quantum chemical density matrix renormalization group method

Here, we present, to the best of our knowledge, the first attempt to exploit the supercomputer platform for quantum chemical density matrix renormalization group (QCDMRG) calculations. We have developed the parallel scheme based on the in-house MPI global memory library, which combines operator and symmetry sector parallelisms, and tested its performance on three different molecules, all typical candidates for QC-DMRG calculations. In case of the largest calculation, which is the nitrogenase FeMo cofactor cluster with the active space comprising 113 electrons in 76 orbitals and bond dimension equal to 6000, our parallel approach scales up to approximately 2000 CPU cores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗

DOE OSTI2026

Elevating SolTrace's Capabilities for the Next Generation of Concentrating Solar Analysis

SolTrace is an open-source Monte Carlo ray tracing software developed at NREL. SolTrace can characterize concentrating solar thermal (CST) collector optical performance and is CST technology agnostic. Shown in Fig. 1, SolTrace is a foundational tool in NREL's CST system and component modeling suite. SolTrace's generic surface elements can flexibly model novel collector and receiver designs to predict spatial and temporal flux distributions - critical to understand for CST component design, performance prediction, and system integration. Since its initial development, SolTrace has over 1,650 references on Google Scholar, over 9,800 downloads since 2017, and has served the CST research and development community as a benchmark of 3rd party verification. SolTrace provides users with many options for defining surface shape and boundaries. However, SolTrace provides limited documentation which can result in a steep learning curve for new users. Additionally, SolTrace lacks the computational performance required to evaluate optical performance of a CST system over the course of a year and/or iteratively over design parameters in a timely manner. To address this, we are working towards a new release of SolTrace that enables increased computational throughput by implementing ray tracing acceleration structures and enabling GPU parallelization. Additionally, we are working to improve SolTrace's usability, accessibility, and maintainability by (1) automating solar position time-dependent simulation processes, (2) creating general CST collector templates of grouped elements, (3) updating the user interface to better visualize model inputs and outputs, and (4) creating a user support network through forums, "how to" videos, and documentation.

14 SOLAR ENERGY↗

NASA NTRS1986

Increasing processor utilization during parallel computation rundown

Some parallel processing environments provide for asynchronous execution and completion of general purpose parallel computations from a single computational phase. When all the computations from such a phase are complete, a new parallel computational phase is begun. Depending upon the granularity of the parallel computations to be performed, there may be a shortage of available work as a particular computational phase draws to a close (computational rundown). This can result in the waste of computing resources and the delay of the overall problem. In many practical instances, strict sequential ordering of phases of parallel computation is not totally required. In such cases, the beginning of one phase can be correctly computed before the end of a previous phase is completed. This allows additional work to be generated somewhat earlier to keep computing resources busy during each computational rundown. The conditions under which this can occur are identified and the frequency of occurrence of such overlapping in an actual parallel Navier-Stokes code is reported. A language construct is suggested and possible control strategies for the management of such computational phase overlapping are discussed.

Jones, W. H.↗

NASA NTRS1986

Increasing processor utilization during parallel computation rundown

Jones, William H.↗

DOE OSTI2022

GronOR: Scalable and Accelerated Nonorthogonal Configuration Interaction for Molecular Fragment Wave Functions

GronOR is a program package for nonorthogonal configuration interaction calculations. Electronic wave functions are constructed in terms of antisymmetrized products of multiconfiguration molecular fragment wave functions. The computational complexity of the nonorthogonal methodologies implemented in GronOR applied to large molecular assemblies requires a design that takes full advantage of massively parallel supercomputer architectures and accelerator technologies. This work describes the implementation strategy and resulting performance characteristics. In addition to parallelization and acceleration, the software development strategy includes aspects of fault resiliency and heterogeneous computing. The program was designed for large-scale supercomputers but also runs effectively on small clusters and workstations for small molecular systems. GronOR is available as open source to the scientific community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Enabling Low-Overhead HT-HPC Workflows at Extreme Scale using GNU Parallel

GNU Parallel is a versatile and powerful tool for process parallelization widely used in scientific computing. This paper demonstrates its effective application in high-performance computing (HPC) environments, particularly focusing on its scalability and efficiency in executing large-scale high-throughput high-performance computing (HT-HPC) workflows. Through real-world examples, we highlight GNU Parallel’s performance across various HPC workloads, including GPU computing, container-based workloads, and node-local NVMe storage. Our results on two leading supercomputers, OLCF’s Frontier and NERSC’s Perlmutter, showcase GNU Parallel’s rapid process dispatching ability and its capacity to maintain low overhead even at extreme scales. We explore GNU Parallel’s application in massive parallel file transfers using a scheduled Data Transfer Node (DTN) cluster, emphasizing its broad utility in diverse scientific workflows. Beyond its direct application as a viable workflow manager, GNU Parallel can be employed in conjunction with other workflow systems as a "last-mile" parallelizing driver and as a quick prototyping tool to design and extract parallel profiles from application executions. We then argue that the potential for GNU Parallel to transform workflow management at extreme scales is substantial, paving the way for more efficient and effective scientific discoveries.

Maheshwari, Ketan↗

NASA NTRS1994

Steady and unsteady three-dimensional transonic flow computations by integral equation method

This is the final technical report of the research performed under the grant: NAG1-1170, from the National Aeronautics and Space Administration. The report consists of three parts. The first part presents the work on unsteady flows around a zero-thickness wing. The second part presents the work on steady flows around non-zero thickness wings. The third part presents the massively parallel processing implementation and performance analysis of integral equation computations. At the end of the report, publications resulting from this grant are listed and attached.

Hu, Hong↗

NASA NTRS1993

RIACS/USRA

The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on 6 June 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under contract with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Parallel Computing, Advanced Methods for Scientific Computing, High Performance Networks and Technology, and Learning Systems. Parallel compiler techniques, adaptive numerical methods for flows in complicated geometries, and optimization were identified as important problems to investigate for ARC's involvement in the Computational Grand Challenges of the next decade.

Oliger, Joseph↗

NASA NTRS2017

Position Paper - pFLogger: The Parallel Fortran Logging framework for HPC Applications

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or logger) similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Fortran↗

NASA NTRS2017

POSITION PAPER - pFLogger: The Parallel Fortran Logging Framework for HPC Applications

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or 'logger') similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger - a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Clune, Thomas L.↗

DOE OSTI2022

Mass transport limitations in polymer electrolyte water electrolyzers using spatially-resolved current measurement

Here this work utilizes spatially-resolved current measurements to provide insight into mass transport limitations in electrolyzers that are not observable from traditional polarization measurement. In this study, two types of flow-fields (parallel and triple-serpentine) and two types of diffusion media (patterned porous thin titanium foil LGDLs and Ir-coated titanium felt PTLs) were examined. A non-uniform current distribution dominated by mass transport limitations was observed to be instigated by the restriction of liquid water transport to catalyst sites. Additionally, conditions are revealed which yield similar polarization performance but dissimilar current distributions. In such cases, the transport limitations for different architectures and porous media affect polarization in different regions of the active area. Furthermore, the triple-serpentine flow-field used in this study performs better than the parallel flow-field under mass transport limited operating conditions. This indicates that the parallel flow-field used in this study is more susceptible to starvation than the triple-serpentine flow-field for the electrolyzer studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗