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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Parallel Rendering of Large Time-Varying Volume Data

Interactive visualization of large time-varying 3D volume datasets has been and still is a great challenge to the modem computational world. It stretches the limits of the memory capacity, the disk space, the network bandwidth and the CPU speed of a conventional computer. In this SURF project, we propose to develop a parallel volume rendering program on SGI's Prism, a cluster computer equipped with state-of-the-art graphic hardware. The proposed program combines both parallel computing and hardware rendering in order to achieve an interactive rendering rate. We use 3D texture mapping and a hardware shader to implement 3D volume rendering on each workstation. We use SGI's VisServer to enable remote rendering using Prism's graphic hardware. And last, we will integrate this new program with ParVox, a parallel distributed visualization system developed at JPL. At the end of the project, we Will demonstrate remote interactive visualization using this new hardware volume renderer on JPL's Prism System using a time-varying dataset from selected JPL applications.

Garbutt, Alexander E.↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2021

Sierra/SolidMechanics for Target Pulse Analysis

Sierra is a Multiphysics engineering simulation software suite developed and maintained by Sandia National Laboratories. Created for nuclear weapon needs, it is available to national laboratories with a foreign national restriction. The STS Target Systems analysis team is interested in using Sierra to perform explicit dynamics simulations of the tantalum clad tungsten target blocks as a response to sudden temperature changes induced by proton beam pulses. The capability already exists with Abaqus|Explicit, however, there are time and cost limitations due to licensing. The following document serves as a “quick-start” guide to the successful setup, simulation, and post-processing of a target model in Sierra on the OZ3 computing cluster at the Spallation Neutron Source.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs

DBSCAN is a well-known density-based clustering algorithm to discover arbitrary shape clusters. While conceptually simple in serial, the algorithm is challenging to efficiently parallelize on manycore GPU architectures. Common pitfalls, such as asynchronous range query calls, result in high thread execution divergence in many implementations. In this paper, we propose a new framework for GPU-accelerated DBSCAN, and describe two tree-based algorithms within that framework. Both algorithms fuse the search for neighbors with updating cluster information, but differ in their treatment of dense regions of the data. We show that the time taken to compute clusters is at most twice that of determination of the neighbors. We compare the proposed algorithms with existing CPU and GPU implementations, and demonstrate their competitiveness and performance using a fast traversal structure (bounding volume hierarchy) for low dimensional data. We also show that the memory usage can be reduced by processing object neighbors dynamically without storing them.

Prokopenko, Andrey↗
NASA NTRS2005

Performance Comparison of Mainframe, Workstations, Clusters, and Desktop Computers

A performance evaluation of a variety of computers frequently found in a scientific or engineering research environment was conducted using a synthetic and application program benchmarks. From a performance perspective, emerging commodity processors have superior performance relative to legacy mainframe computers. In many cases, the PC clusters exhibited comparable performance with traditional mainframe hardware when 8-12 processors were used. The main advantage of the PC clusters was related to their cost. Regardless of whether the clusters were built from new computers or whether they were created from retired computers their performance to cost ratio was superior to the legacy mainframe computers. Finally, the typical annual maintenance cost of legacy mainframe computers is several times the cost of new equipment such as multiprocessor PC workstations. The savings from eliminating the annual maintenance fee on legacy hardware can result in a yearly increase in total computational capability for an organization.

Farley, Douglas L.↗
NASA NTRSDate not supplied

Medical Resource Set Bulky Item Trade Space Analysis for Spaceflight Medical Risk

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the efforts by NASA’s Human Research Program (HRP) to quantitatively assess the impact of health and performance risk. One of MEDPRAT’s key features is its high degree of computational efficiency. Coupled with the HRP High Performance Compute cluster located at NASA’s Glenn Research Center, MEDPRAT runs millions of simulated missions in a matter of minutes. This degree of computational efficiency provides the novel opportunity to explore the relationship between medical set mass, volume, and medical resource size. Of particular interest for future human spaceflight missions are ‘bulky’ items, medical resources like devices, which occupy a large portion of the small, allocated mass and volume for the medical set leaving less room for other resources. This talk will present results showing the quantitative impact of forced inclusion of several bulky items across a variety of medical kit constraints, and the effect that a potential research investment into reducing the bulky item mass and volume may have on risk.

Lauren Mcintyre↗
DOE OSTI2017

Data Structures for Extreme Scale Computing

As computing problems of national importance grow, the government meets the increased demand by funding the development of ever larger systems. The overarching goal of the work supported in part by this grant is to increase efficiency of programming and performing computations on these large computing systems. In past work, we have demonstrated that some of these computations once thought to require expensive hardware designs and/or complex, special-purpose programming may be executed efficiently on low-cost commodity cluster computing systems using a general-purpose “latency-tolerant” programming framework. One important developed application of the ideas underlying this framework is graph database technology supporting social network pattern matching used by US intelligence agencies to more quickly identify potential terrorist threats. This database application has been spun out by the Pacific Northwest National Laboratory, a Department of Energy Laboratory, into a commercial start-up, Trovares Inc. We explore an alternative application of the same underlying ideas to a well-studied challenge arising in engineering: solving unstructured sparse linear equations. Solving these equations is key to predicting the behavior of large electronic circuits before they are fabricated. Predicting that behavior ahead of fabrication means that designs can optimized and errors corrected ahead of the expense of manufacture.

42 ENGINEERING↗
DOE OSTI2024

Ab initio computations of strongly deformed nuclei near Zr 80

Nuclei around N ≈ Z ≈ 40 are strongly deformed and exhibit coexistence of shapes. These phenomena have challenged nuclear models. Here, we perform ab initio coupled-cluster computations of low-lying collective states and electromagnetic quadrupole transitions of the even-even nuclei 72 Kr, 76,78 Sr, 78,80 Zr, and 84 Mo starting from chiral nucleon-nucleon and three-nucleon forces. Our calculations reproduce the coexistence of oblate and prolate shapes in these nuclei, yield rotational bands and strong electromagnetic transitions, but are not accurate for some observables and nuclei. Finally, these results highlight the advances and challenges of ab initio computations of heavy deformed nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Parallel Fokker–Planck-DSMC algorithm for rarefied gas flow simulation in complex domains at all Knudsen numbers

A major challenge for the conventional Direct Simulation Monte Carlo (DSMC) technique lies in the fact that its computational cost becomes prohibitive in the near continuum regime, where the Knudsen number (Kn)—characterizing the degree of rarefaction—becomes small. In contrast, the Fokker–Planck (FP) based particle Monte Carlo scheme allows for computationally efficient simulations of rarefied gas flows in the low and intermediate Kn regime. The Fokker–Planck collision operator—instead of performing binary collisions employed by the DSMC method—integrates continuous stochastic processes for the phase space evolution in time. This allows for time step and grid cell sizes larger than the respective collisional scales required by DSMC. Dynamically switching between the FP and the DSMC collision operators in each computational cell is the basis of the combined FP-DSMC method, which has been proven successful in simulating flows covering the whole Kn range. Until recently, this algorithm had only been applied to two-dimensional test cases. In this contribution, we present the first general purpose implementation of the combined FP-DSMC method. Utilizing both shared- and distributed-memory parallelization, this implementation provides the capability for simulations involving many particles and complex geometries by exploiting state of the art computer cluster technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
NASA NTRS2003

NASA Tech Briefs, September 2003

Topics include: Oxygen-Partial-Pressure Sensor for Aircraft Oxygen Mask; Three-Dimensional Venturi Sensor for Measuring Extreme Winds; Swarms of Micron-Sized Sensors; Monitoring Volcanoes by Use of Air-Dropped Sensor Packages; Capacitive Sensors for Measuring Masses of Cryogenic Fluids; UHF Microstrip Antenna Array for Synthetic- Aperture Radar; Multimode Broad-Band Patch Antennas; 164-GHz MMIC HEMT Frequency Doubler; GPS Position and Heading Circuitry for Ships; Software for Managing Parametric Studies; Software Aids Visualization of Computed Unsteady Flow; Software for Testing Electroactive Structural Components; Advanced Software for Analysis of High-Speed Rolling-Element Bearings; Web Program for Development of GUIs for Cluster Computers; XML-Based Generator of C++ Code for Integration With GUIs; Oxide Protective Coats for Ir/Re Rocket Combustion Chambers; Simplified Waterproofing of Aerogels; Improved Thermal-Insulation Systems for Low Temperatures; Device for Automated Cutting and Transfer of Plant Shoots; Extension of Liouville Formalism to Postinstability Dynamics; Advances in Thrust-Based Emergency Control of an Airplane; Ultrasonic/Sonic Mechanisms for Drilling and Coring; Exercise Device Would Exert Selectable Constant Resistance; Improved Apparatus for Measuring Distance Between Axles; Six Classes of Diffraction-Based Optoelectronic Instruments; Modernizing Fortran 77 Legacy Codes; Active State Model for Autonomous Systems; Shields for Enhanced Protection Against High-Speed Debris; Scaling of Two-Phase Flows to Partial-Earth Gravity; Neutral-Axis Springs for Thin-Wall Integral Boom Hinges.

Source record↗
DOE OSTI2017

A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark

We investigate the performance and scalability of the randomized CX low-rank matrix factorization and demonstrate its applicability through the analysis of a 1TB mass spectrometry imaging (MSI) dataset, using Apache Spark on an Amazon EC2 cluster, a Cray XC40 system, and an experimental Cray cluster. We implemented this factorization both as a parallelized C implementation with hand-tuned optimizations and in Scala using the Apache Spark high-level cluster computing framework. We obtained consistent performance across the three platforms: using Spark we were able to process the 1TB size dataset in under 30 minutes with 960 cores on all systems, with the fastest times obtained on the experimental Cray cluster. In comparison, the C implementation was 21X faster on the Amazon EC2 system, due to careful cache optimizations, bandwidth-friendly access of matrices and vector computation using SIMD units. We report these results and their implications on the hardware and software issues arising in supporting data-centric workloads in parallel and distributed environments.

Gittens, Alex↗
DOE OSTI2020

Ad Hoc File Systems for High-Performance Computing

Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 “Challenges and Opportunities of User-Level File Systems for HPC” and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Mota Mapper: A Multi-Objective Topology-Aware Mapper Library

Mota is a library that provides several heuristics for mapping tasks to compute ranks (processes) on a networked computer cluster or supercomputer. It was originally designed for task placement of adaptive mesh refinement codes (AMR). It is multi-objective in the sense that it simultaneously balances (potentially multiple types of) computational load on each rank as well as the communication traffic between the boxes. The heuristics used for mapping include algorithms such as greedy list assignment and space-filling curves, as well as algorithms from graph analysis such as adjacency matrix bandwidth reduction, recursive bisection, and greedy graph placement.

Chan, Cy↗
DOE OSTI2022

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.

Poeschel, Franz↗
DOE OSTI2019

Development of mathematical model and simulation tool for the high capacity production of carbon fiber

Weight reduction in the transportation is the most effective way for reducing fuel consumption. Carbon fiber composites have been increasingly used by automotive manufacturers in vehicle nonstructural and structural parts. Larger penetration of carbon fiber-based materials requires very large quantities of fiber at a low price, and of consistently high quality. The advancements in carbon fiber technology that are required to achieve this production scale-up and cost reduction, are very challenging. The most expensive production step is the conversion of textile-grade polyacrylonitrile (PAN) precursor to carbon fiber. The conversion takes place in three main stages: stabilization, low and high temperature carbonization. Most of the research to date has focused on the stabilization of the PAN precursors and oxidized PAN fibers. Comparingly little effort has been dedicated to the investigation of processing conditions during low temperature (LT) carbonization. During this stage of fiber conversion, the largest fiber mass loss occurs, and the fundamental carbon fiber structure and material properties are established. The understanding of the conversion processes in the LT furnace, and the effects of spatial and temporal variations of the LT process parameters on the carbon fiber properties are necessary to identify possible process improvements and cost savings. The objective of this research was to develop computational models for gas flow and energy transport in the gas and fiber phases of the LT furnace, and to couple them with the fiber conversion process models. The model is implemented in the three-dimensional framework that enables analysis of process condition variations in all directions. The formulation is based on modified transport equation to simulate steady state conditions and thus significantly reduce computational effort associated with transient methods. The model is implemented in open source computational fluid dynamics (CFD) code OpenFOAM. The model has been demonstrated on a prototype carbon fiber LT furnace and can be extended to other geometries and process conditions. Extensions to other physical effects, such as accounting evolution of multiple gas species, and fiber tensioning are straightforward. The model uses High Performance Computing (HPC) capabilities of OpenFOAM and can be implemented on multi-processor workstations or mid-size computer clusters common in the industrial setting.

36 MATERIALS SCIENCE↗
NASA NTRS2011

CFD Based Computations of Flexible Helicopter Blades for Stability Analysis

As a collaborative effort among government aerospace research laboratories an advanced version of a widely used computational fluid dynamics code, OVERFLOW, was recently released. This latest version includes additions to model flexible rotating multiple blades. In this paper, the OVERFLOW code is applied to improve the accuracy of airload computations from the linear lifting line theory that uses displacements from beam model. Data transfers required at every revolution are managed through a Unix based script that runs jobs on large super-cluster computers. Results are demonstrated for the 4-bladed UH-60A helicopter. Deviations of computed data from flight data are evaluated. Fourier analysis post-processing that is suitable for aeroelastic stability computations are performed.

Guruswamy, Guru P.↗
DOE OSTI2019

VeloxChem: A Python-driven density-functional theory program for spectroscopy simulations in high-performance computing environments

An open-source program named VeloxChem has been developed for the calculation of electronic real and complex linear response functions at the levels of Hartree–Fock and Kohn–Sham density functional theories. With an object-oriented program structure written in a Python/C++ layered fashion, VeloxChem enables time-efficient prototyping of novel scientific approaches without sacrificing computational efficiency, so that molecular systems involving up to and beyond 500 second-row atoms (or some 10,000 contracted and in part diffuse Gaussian basis functions) can be routinely addressed. In addition, VeloxChem is equipped with a polarizable embedding scheme for the treatment of the classical electrostatic interactions with an environment that in turn is modeled by atomic site charges and polarizabilities. The underlying hybrid message passing interface (MPI)/open multiprocessing (OpenMP) parallelization scheme makes VeloxChem suitable for execution in high-performance computing cluster environments, showing even slightly beyond linear scaling for the Fock matrix construction with use of up to 16,384 central processing unit (CPU) cores. An efficient—with respect to convergence rate and overall computational cost—multifrequency/gradient complex linear response equation solver enables calculations not only of conventional spectra, such as visible/ultraviolet/X-ray electronic absorption and circular dichroism spectra, but also time-resolved linear response signals as due to ultra-short weak laser pulses.

97 MATHEMATICS AND COMPUTING↗