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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 163 records · Page 9
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↗
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↗
DOE OSTI2017

Toward Exascale Earthquake Ground Motion Simulations for Near-Fault Engineering Analysis

Modernizing SW4 for massively parallel time-domain simulations of earthquake ground motions in 3D earth models increases resolution and provides ground motion estimates for critical infrastructure risk evaluations. Simulations of ground motions from large (M ≥ 7.0) earthquakes require domains on the order of 100 to500 km and spatial granularity on the order of 1 to5 m resulting in hundreds of billions of grid points. Surface-focused structured mesh refinement (SMR) allows for more constant grid point per wavelength scaling in typical Earth models, where wavespeeds increase with depth. In fact, MR allows for simulations to double the frequency content relative to a fixed grid calculation on a given resource. The authors report improvements to the SW4 algorithm developed while porting the code to the Cori Phase 2 (Intel Xeon Phi) systems at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. As a result, investigations of the performance of the innermost loop of the calculations found that reorganizing the order of operations can improve performance for massive problems.

58 GEOSCIENCES↗
DOE OSTI2019

Computational Reproducibility of Scientific Workflows at Extreme Scales

We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.

provenance, Reproducibility, Climate & Earth Syste↗