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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Systematic Study to Determine 5G Baseline Performance for Scientific Computing

The fifth-generation (5G) cellular networks envisions achieving higher data rates, improved connectivity, reduced latency, and better quality of service (QoS) than the fourthgeneration (4G) cellular networks. Such improved performance can be utilized to address the challenges in applications such as electricity generation in power systems. The traditional power grids responsible for electricity generation suffer from drawbacks such as life-threatening blackout crises, and energy storage proliferation as they are not robust to extreme climatic conditions. A recent study proposed the idea of extending the capabilities of advanced wireless technologies such as the current 5G to develop a robust, energy-efficient, and secure smart grids. However there are two main challenges associated with the integration of power systems and wireless technologies. First, it is imperative to understand the architecture and the enabling technologies of 5G to ensure that the performance requirements of the smart grids are met. Second, an end-to-end testbed is required to determine if the performance requirements are met by estimating the 5G characteristics such as latency, and throughput. Our proposed alleviates the aforementioned concerns in the following manner. To begin with, a systematic study of the 5G architecture including both the StandAlone (SA) and Non-Standalone (NSA) operations is presented. Furthermore, a detailed survey of the possible 5G enabling technologies is elicited. In addition to these, an end-toend testbed that can estimate the 5G characteristics is explained in detail with appropriate preliminary results.

5G, 5G Communication↗
DOE OSTI2015

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context to price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Heterogeneity-Aware Task Scheduler for Spark

Big data processing systems such as Spark are employed in an increasing number of diverse applications-such as machine learning, graph computation, and scientific computing-each with dynamic and different resource needs. These applications increasingly run on heterogeneous hardware, e.g., with out-of-core accelerators. However, big data platforms do not factor in the multi-dimensional heterogeneity of applications and hardware. This leads to a fundamental mismatch between the application and hardware characteristics, and the resource scheduling adopted in big data platforms. For example, Hadoop and Spark consider only data locality when assigning tasks to nodes, and typically disregard the hardware capabilities and suitability to specific application requirements. In this paper, we present RUPAM, a heterogeneity-aware task scheduling system for big data platforms, which considers both task-level resource characteristics and underlying hardware characteristics, as well as preserves data locality. RUPAM adopts a simple yet effective heuristic to decide the dominant scheduling factor (e.g., CPU, memory, or I/O), given a task in a particular stage. Our experiments show that RUPAM is able to improve the performance of representative applications by up to 62.3% compared to the standard Spark scheduler.

Computer Science↗
DOE OSTI2022

Neuromorphic Computing for Scientific Applications

Neuromorphic computing technology continues to make strides in the development of new algorithms, devices, and materials. In addition, applications have begun to emerge where neuromorphic computing shows promising results. However, numerous barriers to further development and application remain. In this work, we identify several science areas where neuromorphic computing can either make an immediate impact (within 1 to 3 years) or the societal impact would be extremely high if the technological barriers can be addressed. We identify both opportunities and hurdles to the development of neuromorphic computing technology for these areas. Finally, we discuss future directions that need to be addressed to expand both the development and application of neuromorphic computing.

Patton, Robert↗
NASA NTRS2013

Educational NASA Computational and Scientific Studies (enCOMPASS)

Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.

Memarsadeghi, Nargess↗
DOE OSTI2018

Neuromorphic computing for temporal scientific data classification

In this work, we apply a spiking neural network model and an associated memristive neuromorphic implementation to an application in classifying temporal scientific data. We demonstrate that the spiking neural network model achieves comparable results to a previously reported convolutional neural network model, with significantly fewer neurons and synapses required.

Schuman, Catherine↗
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↗