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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

High Performance Parallel Computational Nanotechnology

At a recent press conference, NASA Administrator Dan Goldin encouraged NASA Ames Research Center to take a lead role in promoting research and development of advanced, high-performance computer technology, including nanotechnology. Manufacturers of leading-edge microprocessors currently perform large-scale simulations in the design and verification of semiconductor devices and microprocessors. Recently, the need for this intensive simulation and modeling analysis has greatly increased, due in part to the ever-increasing complexity of these devices, as well as the lessons of experiences such as the Pentium fiasco. Simulation, modeling, testing, and validation will be even more important for designing molecular computers because of the complex specification of millions of atoms, thousands of assembly steps, as well as the simulation and modeling needed to ensure reliable, robust and efficient fabrication of the molecular devices. The software for this capacity does not exist today, but it can be extrapolated from the software currently used in molecular modeling for other applications: semi-empirical methods, ab initio methods, self-consistent field methods, Hartree-Fock methods, molecular mechanics; and simulation methods for diamondoid structures. In as much as it seems clear that the application of such methods in nanotechnology will require powerful, highly powerful systems, this talk will discuss techniques and issues for performing these types of computations on parallel systems. We will describe system design issues (memory, I/O, mass storage, operating system requirements, special user interface issues, interconnects, bandwidths, and programming languages) involved in parallel methods for scalable classical, semiclassical, quantum, molecular mechanics, and continuum models; molecular nanotechnology computer-aided designs (NanoCAD) techniques; visualization using virtual reality techniques of structural models and assembly sequences; software required to control mini robotic manipulators for positional control; scalable numerical algorithms for reliability, verifications and testability. There appears no fundamental obstacle to simulating molecular compilers and molecular computers on high performance parallel computers, just as the Boeing 777 was simulated on a computer before manufacturing it.

Saini, Subhash↗
DOE OSTI2016

High fidelity simulation and analysis of liquid jet atomization in a gaseous crossflow at intermediate Weber numbers

Recent advances in numerical methods coupled with the substantial enhancements in computing power and the advent of high performance computing have presented first principle, high fidelity simulation as a viable tool in the prediction and analysis of spray atomization processes. The credibility and potential impact of such simulations, however, has been hampered by the relative absence of detailed validation against experimental evidence. The numerical stability and accuracy challenges arising from the need to simulate the high liquid-gas density ratio across the sharp interfaces encountered in these flows are key reasons for this. In this work we challenge this status quo by presenting a numerical model able to deal with these challenges, employing it in simulations of liquid jet in crossflow atomization and performing extensive validation of its results against a carefully executed experiment with detailed measurements in the atomization region. We then proceed to the detailed analysis of the flow physics. The computational model employs the coupled level set and volume of fluid approach to directly capture the spatiotemporal evolution of the liquid-gas interface and the sharp-interface ghost fluid method to stably handle high liquid-air density ratio. Adaptive mesh refinement and Lagrangian droplet models are shown to be viable options for computational cost reduction. Moreover, high performance computing is leveraged to manage the computational cost. The experiment selected for validation eliminates the impact of inlet liquid and gas turbulence and focuses on the impact of the crossflow aerodynamic forces on the atomization physics. Validation is demonstrated by comparing column surface wavelengths, deformation, breakup locations, column trajectories and droplet sizes, velocities, and mass rates for a range of intermediate Weber numbers. Analysis of the physics is performed in terms of the instability and breakup characteristics and the features of downstream flow recirculation, and vortex shedding. Formation of “Λ” shape windward column waves is observed and explained by the combined upward and lateral surface motion. The existence of Rayleigh-Taylor instability as the primary mechanism for the windward column waves is verified for this case by comparing wavelengths from the simulations to those predicted by linear stability analyses. Physical arguments are employed to postulate that the type of instability manifested may be related to conditions such as the gas Weber number and the inlet turbulence level. The decreased column wavelength with increasing Weber number is found to cause enhanced surface stripping and early depletion of liquid core at higher Weber number. A peculiar “three-streak-two-membrane” liquid structure is identified at the lowest Weber number and explained as the consequence of the symmetric recirculation zones behind the jet column. It is found that the vortical flow downstream of the liquid column resembles a von Karman vortex street and that the coupling between the gas flow and droplet transport is weak for the conditions explored.

42 ENGINEERING↗
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
DOE OSTI2012

Programs for Increasing the Engagement of Underrepresented Ethnic Groups and People with Disabilities in HPC. Final assessment report

Given the significant impact of computing on society, it is important that all cultures, especially underrepresented cultures, are fully engaged in the field of computing to ensure that everyone benefits from the advances in computing. This proposal is focused on the field of high performance computing. The lack of cultural diversity in computing, in particular high performance computing, is especially evident with respect to the following ethnic groups – African Americans, Hispanics, and Native Americans – as well as People with Disabilities. The goal of this proposal is to organize and coordinate a National Laboratory Career Development Workshop focused on underrepresented cultures (ethnic cultures and disability cultures) in high performance computing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing. The PRIMA Project

The growing number of cores provided by today’s high-­end computing systems present substantial challenges to application developers in their pursuit of parallel efficiency. To find the most effective optimization strategy, application developers need insight into the runtime behavior of their code. The University of Oregon (UO) and the Juelich Supercomputing Centre of Forschungszentrum Juelich (FZJ) develop the performance analysis tools TAU and Scalasca, respectively, which allow high-­performance computing (HPC) users to collect and analyze relevant performance data – even at very large scales. TAU and Scalasca are considered among the most advanced parallel performance systems available, and are used extensively across HPC centers in the U.S., Germany, and around the world. The TAU and Scalasca groups share a heritage of parallel performance tool research and partnership throughout the past fifteen years. Indeed, the close interactions of the two groups resulted in a cross-­fertilization of tool ideas and technologies that pushed TAU and Scalasca to what they are today. It also produced two performance systems with an increasing degree of functional overlap. While each tool has its specific analysis focus, the tools were implementing measurement infrastructures that were substantially similar. Because each tool provides complementary performance analysis, sharing of measurement results is valuable to provide the user with more facets to understand performance behavior. However, each measurement system was producing performance data in different formats, requiring data interoperability tools to be created. A common measurement and instrumentation system was needed to more closely integrate TAU and Scalasca and to avoid the duplication of development and maintenance effort. The PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis) project was proposed over three years ago as a joint international effort between UO and FZJ to accomplish these objectives: (1) refactor TAU and Scalasca performance system components for core code sharing and (2) integrate TAU and Scalasca functionality through data interfaces, formats, and utilities. As presented in this report, the project has completed these goals. In addition to shared technical advances, the groups have worked to engage with users through application performance engineering and tools training. In this regard, the project benefits from the close interactions the teams have with national laboratories in the United States and Germany. We have also sought to enhance our interactions through joint tutorials and outreach. UO has become a member of the Virtual Institute of High-­Productivity Supercomputing (VI-­HPS) established by the Helmholtz Association of German Research Centres as a center of excellence, focusing on HPC tools for diagnosing programming errors and optimizing performance. UO and FZJ have conducted several VI-­HPS training activities together within the past three years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Special Issue on Data-Intensive Scalable Computing Systems

Traditional high performance computing (HPC) systems were designed from a compute-centric perspective, with an emphasis on high floating point performance. As scientific and analytics applications become more data intensive, there is a need to rethink HPC system architectures, programming models, runtime systems, and tools with a focus on scalable data intensive computing. Industry approaches supporting data intensive applications have been highly successful, leading many in the HPC community to search for ways to adapt them to HPC hardware and software contexts. Conversely, industry looks to the HPC community for their expertise in designing, deploying, and using extremely large and high performance computing systems. This special issue contains seven articles, each of which is an extended version of a paper presented in the 2015 International Workshop on Data Intensive Scalable Computing Systems (DISCS-2015) held in Austin, Texas on November 15, 2015. The workshop program included papers that ranged from programming models to systems software to storage, each with a strong focus on data intensive computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Smart Sampling and HPC-based Probabilistic Look-ahead Contingency Analysis Implementation and its Evaluation with Real-world Data

This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested with real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.

high performance computing↗
DOE OSTI2013

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we will instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.

James, Conrad D.↗
DOE OSTI2015

Optimization and large scale computation of an entropy-based moment closure

We present computational advances and results in the implementation of an entropy-based moment closure, M N , in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as P N , but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. Lastly, these results show, in particular, load balancing issues in scaling the M N algorithm that do not appear for the P N algorithm. We also observe that in weak scaling tests, the ratio in time to solution of M N to P N decreases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High Performance Approximate Computing

This code repository contains the implementation of the "High-Performance Approximate Computing" (HPAC) toolkit. The toolkit allows you to approximate your own C/C++. The developer uses "pragma's" to annotate code regions as approximate. The compiler extensions lower these pragmas to either compiletime approximate techniques or runtime approximation techniques. At execution time, the implemented runtime system decides which annotated regions it should approximate. HPAC also provides a set of script utilities. The utilities perform a grid search within approximation parameters and performance. The user can analyze the raw data to identify optimal approximation techniques for the application.

Parasyris, Konstantinos↗
NASA NTRS2000

SUPREM-DSMC: A New Scalable, Parallel, Reacting, Multidimensional Direct Simulation Monte Carlo Flow Code

An AFRL/NRL team has recently been selected to develop a scalable, parallel, reacting, multidimensional (SUPREM) Direct Simulation Monte Carlo (DSMC) code for the DoD user community under the High Performance Computing Modernization Office (HPCMO) Common High Performance Computing Software Support Initiative (CHSSI). This paper will introduce the JANNAF Exhaust Plume community to this three-year development effort and present the overall goals, schedule, and current status of this new code.

Campbell, David↗
NASA NTRS2000

Efficient Use of Distributed Systems for Scientific Applications

Distributed computing has been regarded as the future of high performance computing. Nationwide high speed networks such as vBNS are becoming widely available to interconnect high-speed computers, virtual environments, scientific instruments and large data sets. One of the major issues to be addressed with distributed systems is the development of computational tools that facilitate the efficient execution of parallel applications on such systems. These tools must exploit the heterogeneous resources (networks and compute nodes) in distributed systems. This paper presents a tool, called PART, which addresses this issue for mesh partitioning. PART takes advantage of the following heterogeneous system features: (1) processor speed; (2) number of processors; (3) local network performance; and (4) wide area network performance. Further, different finite element applications under consideration may have different computational complexities, different communication patterns, and different element types, which also must be taken into consideration when partitioning. PART uses parallel simulated annealing to partition the domain, taking into consideration network and processor heterogeneity. The results of using PART for an explicit finite element application executing on two IBM SPs (located at Argonne National Laboratory and the San Diego Supercomputer Center) indicate an increase in efficiency by up to 36% as compared to METIS, a widely used mesh partitioning tool. The input to METIS was modified to take into consideration heterogeneous processor performance; METIS does not take into consideration heterogeneous networks. The execution times for these applications were reduced by up to 30% as compared to METIS. These results are given in Figure 1 for four irregular meshes with number of elements ranging from 30,269 elements for the Barth5 mesh to 11,451 elements for the Barth4 mesh. Future work with PART entails using the tool with an integrated application requiring distributed systems. In particular this application, illustrated in the document entails an integration of finite element and fluid dynamic simulations to address the cooling of turbine blades of a gas turbine engine design. It is not uncommon to encounter high-temperature, film-cooled turbine airfoils with 1,000,000s of degrees of freedom. This results because of the complexity of the various components of the airfoils, requiring fine-grain meshing for accuracy. Additional information is contained in the original.

Taylor, Valerie↗
DOE OSTI2016

OpenACC to FPGA: A Framework for Directive-based High-Performance Reconfigurable Computing

This paper presents a directive-based, high-level programming framework for high-performance reconfigurable computing. It takes a standard, portable OpenACC C program as input and generates a hardware configuration file for execution on FPGAs. We implemented this prototype system using our open-source OpenARC compiler; it performs source-to-source translation and optimization of the input OpenACC program into an OpenCL code, which is further compiled into a FPGA program by the backend Altera Offline OpenCL compiler. Internally, the design of OpenARC uses a high- level intermediate representation that separates concerns of program representation from underlying architectures, which facilitates portability of OpenARC. In fact, this design allowed us to create the OpenACC-to-FPGA translation framework with minimal extensions to our existing system. In addition, we show that our proposed FPGA-specific compiler optimizations and novel OpenACC pragma extensions assist the compiler in generating more efficient FPGA hardware configuration files. Our empirical evaluation on an Altera Stratix V FPGA with eight OpenACC benchmarks demonstrate the benefits of our strategy. To demonstrate the portability of OpenARC, we show results for the same benchmarks executing on other heterogeneous platforms, including NVIDIA GPUs, AMD GPUs, and Intel Xeon Phis. This initial evidence helps support the goal of using a directive-based, high-level programming strategy for performance portability across heterogeneous HPC architectures.

Lee, Seyong↗
DOE OSTI2026

Oak Ridge Computing Academy: An HPC cluster deployment and management pilot

The High Performance Computing Technologies (HPCT) course is a hands-on High Performance Computing (HPC) cluster deployment and management training program offered as part of the International School for Advanced Studies (SISSA) and the International Center for Theoretical Physics (ICTP) Master in High Performance Computing (MHPC) specialization. Here, this training program introduces students to key concepts in cluster configuration. which include networking, software stack provisioning, job scheduling, and monitoring. The publicly available course materials feature several examples and underlying methods that are broadly applicable to cluster deployment and management. This paper discusses the design of a new workforce development program at the Oak Ridge National Laboratory that is based on HPCT, the Oak Ridge Computing Academy (ORCA). The ORCA pilot program was hosted by the Oak Ridge Leadership Computing Facility (OLCF) in Summer 2025. As a part of this discussion, HPCT and ORCA course contents and infrastructure are outlined, ORCA participant experiences are detailed, and potential opportunities for improvement are discussed.

Education↗
DOE OSTI2023

A Sparse Distributed Gigascale Resolution Material Point Method

In this paper, we present a four-layer distributed simulation system and its adaptation to the Material Point Method (MPM). The system is built upon a performance portable C++ programming model targeting major High-Performance-Computing (HPC) platforms. A key ingredient of our system is a hierarchical block-tile-cell sparse grid data structure that is distributable to an arbitrary number of Message Passing Interface (MPI) ranks. We additionally propose strategies for efficient dynamic load balance optimization to maximize the efficiency of MPI tasks. Our simulation pipeline can easily switch among backend programming models, including OpenMP and CUDA, and can be effortlessly dispatched onto supercomputers and the cloud. Finally, we construct benchmark experiments and ablation studies on supercomputers and consumer workstations in a local network to evaluate the scalability and load balancing criteria. We demonstrate massively parallel, highly scalable, and gigascale resolution MPM simulations of up to 1.01 billion particles for less than 323.25 seconds per frame with 8 OpenSSH-connected workstations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Framework for Error-Bounded Approximate Computing, with an Application to Dot Products

Approximate computing techniques, which trade off the computation accuracy of an algorithm for better performance and energy efficiency, have been successful in reducing computation and power costs in several domains. However, error sensitive applications in high-performance computing are unable to benefit from existing approximate computing strategies that are not developed with guaranteed error bounds. While approximate computing techniques can be developed for individual high-performance computing applications by domain specialists, this often requires additional theoretical analysis and potentially extensive software modification. Hence, the development of low-level error-bounded approximate computing strategies that can be introduced into any high-performance computing application without requiring additional analysis or significant software alterations is desirable. In this paper, we provide a contribution in this direction by proposing a general framework for designing error-bounded approximate computing strategies and apply it to the dot product kernel to develop \bf qdot---an error-bounded approximate dot product kernel. Following the introduction of qdot, here we perform a theoretical analysis that yields a deterministic bound on the relative approximation error introduced by qdot. Empirical tests are performed to illustrate the tightness of the derived error bound and to demonstrate the effectiveness of qdot on a synthetic dataset, as well as two scientific benchmarks---the conjugate gradient (CG) and power methods. In some instances, using qdot for the dot products in CG can result in many components being quantized to half precision without increasing the iteration count required for convergence to the same solution as CG using a double precision dot product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Software Systems for High-performance Quantum Computing

Quantum computing promises new opportunities for solving hard computational problems, but harnessing this novelty requires breakthrough concepts in the design, operation, and application of computing systems. We define some of the challenges facing the development of quantum computing systems as well as software-based approaches that can be used to overcome these challenges. Following a brief overview of the state of the art, we present models for the quantum programming and execution models, the development of architectures for hybrid high-performance computing systems, and the realization of software stacks for quantum networking. This leads to a discussion of the role that conventional computing plays in the quantum paradigm and how some of the current challenges for exascale computing overlap with those facing quantum computing.

high-performance computing↗
DOE OSTI2023

Energy-Efficient and Resilient Infrastructure: Simulation, Validation, and Installation

Advanced, high-performance computing at the National Renewable Energy Laboratory (NREL) has enabled access to vast data resources with cutting-edge software techniques to understand, design, plan for, and maintain energy-efficient and resilient infrastructure. We have focused on cities and airports, but the technology we have developed will easily translate to seaports, inland ports, military installations, or other complex and large-scale energy-intensive systems. We can digitally simulate and explore current and future scenarios to make datadriven decisions for optimizing advanced energy systems, transportation and building operations, infrastructure planning and expansion, and battery storage to guide short- and long-term investments, electrification strategies, and integration of new technologies.

Athena↗