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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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NASA NTRS1992

USRA/RIACS

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 a cooperative agreement 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; Learning Systems; High Performance Networks and Technology; Graphics, Visualization, and Virtual Environments.

Oliger, Joseph↗

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 NTRS1994

Activities of the Research Institute for Advanced Computer Science

Oliger, Joseph↗

NASA NTRS1996

Research Projects, Technical Reports and Publications

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 June 6, 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: Advanced Methods for Scientific Computing High Performance Networks During this report pefiod Professor Antony Jameson of Princeton University, Professor Wei-Pai Tang of the University of Waterloo, Professor Marsha Berger of New York University, Professor Tony Chan of UCLA, Associate Professor David Zingg of University of Toronto, Canada and Assistant Professor Andrew Sohn of New Jersey Institute of Technology have been visiting RIACS. January 1, 1996 through September 30, 1996 RIACS had three staff scientists, four visiting scientists, one post-doctoral scientist, three consultants, two research associates and one research assistant. RIACS held a joint workshop with Code 1 29-30 July 1996. The workshop was held to discuss needs and opportunities in basic research in computer science in and for NASA applications. There were 14 talks given by NASA, industry and university scientists and three open discussion sessions. There were approximately fifty participants. A proceedings is being prepared. It is planned to have similar workshops on an annual basis. RIACS technical reports are usually preprints of manuscripts that have been submitted to research 'ournals or conference proceedings. A list of these reports for the period January i 1, 1996 through September 30, 1996 is in the Reports and Abstracts section of this report.

Oliger, Joseph↗

NASA NTRS2002

A Grid Infrastructure for Supporting Space-based Science Operations

Emerging technologies for computational grid infrastructures have the potential for revolutionizing the way computers are used in all aspects of our lives. Computational grids are currently being implemented to provide a large-scale, dynamic, and secure research and engineering environments based on standards and next-generation reusable software, enabling greater science and engineering productivity through shared resources and distributed computing for less cost than traditional architectures. Combined with the emerging technologies of high-performance networks, grids provide researchers, scientists and engineers the first real opportunity for an effective distributed collaborative environment with access to resources such as computational and storage systems, instruments, and software tools and services for the most computationally challenging applications.

Bradford, Robert N.↗

DOE OSTI2014

Avalaunch

Avalaunch implements a tree-based process launcher. It first bootstraps itself on to a set of compute nodes by launching children processes, which immediately connect back to the parent process to acquire info needed t launch their own children. Once the tree is established, user processes are started by broadcasting commands and application binaries through the tree. All communication flows over high-performance network protocols via spawnnet. The goal is to start MPI jobs having hundreds of thousands of processes within seconds.

Moody, A. T.↗

DOE OSTI2017

Systems and methods for rapid processing and storage of data

Systems and methods of building massively parallel computing systems using low power computing complexes in accordance with embodiments of the invention are disclosed. A massively parallel computing system in accordance with one embodiment of the invention includes at least one Solid State Blade configured to communicate via a high performance network fabric. In addition, each Solid State Blade includes a processor configured to communicate with a plurality of low power computing complexes interconnected by a router, and each low power computing complex includes at least one general processing core, an accelerator, an I/O interface, and cache memory and is configured to communicate with non-volatile solid state memory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

End-System Aware Peak Link Utilization Transport for Big Data Transfer over Dedicated Connections

High-performance networks provisioning 10/40-Gbps dedicated channels have been rapidly developed andincreasingly deployed to support big data transfers on the order of terabytes or petabytes in large-scale network-intensiveapplications. However, end users have not seen corresponding increase in application throughput mainly because traditional end-to-end transport methods are not optimized for such connections. New congestion or flow control mechanisms are desirable to meet the challenges brought to transport protocol design by dedicated connections. The advent and proliferation of multi-core processors make it now possible to improve application throughput by providing multiple processing and network resources to a single data transfer. Based on the existing PLUT method, we proposed a new dynamic buffer management scheme and an automatic parallelism tuning mechanism to achieve the maximal bottleneck rate. The performance superiority of PLUT is justified by the experimental results collected over a local 10Gbps dedicated connection.

Rao, Nageswara↗

DOE OSTI2019

Systems and methods for rapid processing and storage of data

Stalzer, Mark A.↗

DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗

DOE OSTI2021

Systems and methods for rapid processing and storage of data

Stalzer, Mark A.↗

DOE OSTI2019

O3BNN: An Out-Of-Order Architecture for High-Performance Binarized Neural Network Inference with Fine-Grained Pruning

In this work, we demonstrate that the highly-condensed BNN model can be shrunk significantly further by dynamically pruning irregular redundant edges. Based on two new observations on BNN-specific properties, our out-of-order (OoO) architecture – O3BNN, can curtail remaining edge evaluation in cases where the binary output of a neuron can be determined early. Similar to Instruction-Level-Parallelism (ILP), these fine-grained, irregular, runtime pruning opportunities are traditionally presumed to be di cult to exploit. We evaluate our design on an FPGA platform using three well-known networks, including VggNet-16, AlexNet for ImageNet, and a VGG-like network for Cifar-10. Results show that our out-of-order approach can prune 27%, 16%, and 42% of the operations for the three networks respectively, without any accuracy loss, leading to at least 1.7×, 1.5×, and 2.1× speedups over state-of-the-art implementations on FPGA/GPU/CPU BNN implementations. Our approach is inference runtime pruning, so no retrain or fine-tuning is needed. We demonstrate our design on an FPGA platform. However, this is only for showcasing the method; the approach does not rely on any FPGA-specific features and can thus be adopted by other devices as well.

Geng, Tong↗

DOE OSTI2020

Fibril Network Strategy Enables High‐Performance Semitransparent Organic Solar Cells

Abstract The development of semitransparent organic solar cells (ST‐OSCs) represents a significant step toward the commercialization of OSCs. However, the trade‐off between power conversion efficiency (PCE) and average visible transmittance (AVT) restricts further improvements of ST‐OSCs. Herein, it is demonstrated that a fibril network strategy can enable ST‐OSCs with a high PCE and AVT simultaneously. A wide‐bandgap polymer PBT1‐C‐2Cl that can self‐assemble into a fibril nanostructure is used as the donor and a near‐infrared small molecule Y6 is adopted as the acceptor. It is found that a tiny amount of PBT1‐C‐2Cl in the blend can form a high speed pathway for hole transport due to the well distributed fibril nanostructure, which increases the transmittance in the visible region. Meanwhile, the acceptor Y6 guarantees sufficient light absorption. Using this strategy, the optimized ST‐OSCs yield a high PCE of 9.1% with an AVT of over 40% and significant light utilization efficiency of 3.65% at donor/acceptor ratio of 0.25:1. This work demonstrates a simple and effective approach to realizing high PCE and AVT of ST‐OSCs simultaneously.

Xie, Yuanpeng↗

DOE OSTI2019

3D Interpenetrating Network for High-Performance Nonfullerene Acceptors via Asymmetric Chlorine Substitution

We report two chlorine-substituted isomers, ITIC-2Cl-β and α-ITIC-2Cl, were synthesized for potential use as nonfullerene acceptors. The two molecules differ in the position of chlorine atoms, leading to symmetric (ITIC-2Cl-β) and asymmetric (α-ITIC-2Cl) molecular configuration. In single crystals, the two molecules exhibit a completely different arrangement and stacking as derived from X-ray diffraction analysis. Whereas ITIC-2Cl-β has a linear packing structure, a-ITIC-2Cl forms a 3D interpenetrating network structure with shorter π-π distances and better molecular planarity. Therefore, a high power conversion efficiency of >12% is obtained by α-ITIC-2Cl-based devices. It is similar to ~10% higher than that of ITIC-2Cl-β-based devices due to the chlorine substituent effect. Thus the fine-tuning of the Cl-substituted position seems to be a promising strategy to construct a 3D interpenetrating charge transportation network and achieve higher performance organic solar cells (OSCs).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Functionalized Porous Polymer Networks as High-Performance PFAS Adsorbents

Toxic per- and polyfluoroalkyl substances (PFAS) are now found in nearly every water source on the planet. Exposure to these molecules can have negative health consequences, but the low concentration of PFAS relative to other solutes in water makes their removal challenging. Adsorbents offer a promising treatment route, but often exhibit low selectivities and removal capacities, as well as slow kinetics. The performance in these metrics can be improved by chemically optimizing PFAS binding sites and maximizing PFAS-adsorbent interactions. To explore how to achieve this, a porous polymer network solid (PPN-6, also known as PAF-1) was postsynthetically modified with various chemical moieties capable of leveraging unique combinations of electrostatic, hydrogen-bonding, hydrophobic, and fluorophilic interactions with PFAS molecules. Batch adsorption experiments and computational studies revealed that electrostatic and hydrogen-bonding interactions drive short-chain PFAS adsorption, while hydrophobic and fluorophilic interactions improve long-chain PFAS adsorption. In complex water matrices, a combination of electrostatic and fluorophilic interactions led to the greatest total PFAS removal. The best-performing material, functionalized with a fluorinated alkylammonium (PPN-6-FNDMB), selectively adsorbs PFAS with high capacity (up to 4.0 mmol/g) and rapid kinetics (equilibrium reached in <30 s). Furthermore, PPN-6-FNDMB outperforms several commercial adsorbents, achieving near-complete removal of 21 different PFAS from a groundwater sample collected at a US Air Force base. The PFAS could subsequently be desorbed from PPN-6-FNDMB, concentrating them by a factor of over 50 times. The recycled PPN-6-FNDMB could then be reused with minimal losses in long-chain PFAS adsorption capacity over four cycles.

Pezoulas, Ethan R↗

DOE OSTI2016

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Tunable Crosslinked Ether Polymer Network Electrolytes for High‐Performance All‐Solid‐State Sodium Batteries

All-solid-state batteries (ASSBs) are critical for achieving high energy density and enhanced safety. Solid polymer electrolytes (SPEs) offer key advantages over other electrolytes, including improved safety, flexibility, and interfacial contact. Among the SPEs, ether-based polymers are widely studied due to their ease of processing and high ionic conductivity (σi) in the amorphous state. In this work, the introduction of poly(ethylene glycol) methyl ether methacrylate (PEGMEMA) into an SPE matrix composed of poly(ethylene glycol) diacrylate (PEGDA), poly(ethylene glycol) (PEG2k), and sodium bis(fluorosulfonyl)imide (NaFSI) salt is investigated to facilitate the formation of amorphous, high σ i SPEs through end-group engineering and polymer ratio optimization. PEGMEMA enhances structural integrity via crosslinking with PEGDA through its methacrylate group, while its methyl end group aids ion conduction. A 2:1:7 ratio of PEGDA:PEGMEMA:PEG2k exhibits a σi of 1.16 x 10 -4 S cm -1 and oxidative stability up to 4.4 V at 60 °C. A solid-state cell incorporating this SPE, a Na 2/3 Ni 1/3 Mn 2/3 O 2 (NM12) cathode, and a sodium-metal anode demonstrates excellent cycling stability, retaining over 80 % of its initial capacity for 150 cycles at 60 °C. The findings highlight the potential of end-group engineering in improving the electrochemical performance of SPEs.

25 ENERGY STORAGE↗

DOE OSTI2016

Optical Networks for High-Performance Computing: Promises and Perils.

Abstract not provided.

Rodrigues, Arun F.↗