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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Two-dimensional materials for bio-realistic neuronal computing networks

Two-dimensional (2D) van der Waals materials have found broad utility in a diverse range of applications including electronics, optoelectronics, renewable energy, and quantum information technologies. Meanwhile, exponentially growing digital data coupled with the ubiquity of artificial intelligence algorithms have generated significant interest in edge neuromorphic computing as an alternative to centralized cloud computing. The drive to incorporate neuroscience principles into computing hardware is motivated by the low power consumption, parallel processing, and reconfigurability of the human brain. The diverse library of 2D materials with atomic-level thicknesses, exceptional electrostatic tunability, and integration versatility is particularly well-suited for realizing bio-realistic synaptic and neuronal functionality. Here, we summarize past and present work in this field and outline the frontier challenges that have not yet been overcome. Here we also delineate potential solutions and suggest that the neuroscience principles of criticality and synchrony have the potential to inspire breakthrough applications of 2D materials in neuronal computing networks.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Porting E3SM from LANL’s Open to Classified Computing Networks

This project used turquoise computing time to support a LANL TED project focused on porting E3SM to classified computing resources. Computing time was used to test and confirm a complete and working simulation and analysis workflow prior to porting to yellow and SCI-level computing platforms. The code port and all planned simulations and analysis were successful. E3SM analysis from simulations on classified platforms was published to C2S and will be used to demonstrate and advertise LANL’s classified Earth system modeling and analysis capabilities to current and new customers in the intelligence community. This project also significantly improved library and E3SM analysis support on turquoise, yellow, and classified computing platforms at LANL, which will result in benefits beyond the work discussed here.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Computer network defense system

A method and apparatus for protecting virtual machines. A computer system creates a copy of a group of the virtual machines in an operating network in a deception network to form a group of cloned virtual machines in the deception network when the group of the virtual machines is accessed by an adversary. The computer system creates an emulation of components from the operating network in the deception network. The components are accessible by the group of the cloned virtual machines as if the group of the cloned virtual machines was in the operating network. The computer system moves network connections for the group of the virtual machines in the operating network used by the adversary from the group of the virtual machines in the operating network to the group of the cloned virtual machines, enabling protecting the group of the virtual machines from actions performed by the adversary.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Reliability Lessons Learned From GPU Experience With The Titan Supercomputer at Oak Ridge Leadership Computing Facility

The high computational capability of graphics processing units (GPUs) is enabling and driving the scientific discovery process at large-scale. The world’s second fastest supercomputer for open science, Titan, has more than 18,000 GPUs that computational scientists use to perform scientific simu- lations and data analysis. Understanding of GPU reliability characteristics, however, is still in its nascent stage since GPUs have only recently been deployed at large-scale. This paper presents a detailed study of GPU errors and their impact on system operations and applications, describing experiences with the 18,688 GPUs on the Titan supercom- puter as well as lessons learned in the process of efficient operation of GPUs at scale. These experiences are helpful to HPC sites which already have large-scale GPU clusters or plan to deploy GPUs in the future.

Tiwari, Devesh↗
DOE OSTI2012

Phoebus: Network Middleware for Next-Generation Network Computing

The Phoebus project investigated algorithms, protocols, and middleware infrastructure to improve end-to-end performance in high speed, dynamic networks. The Phoebus system essentially serves as an adaptation point for networks with disparate capabilities or provisioning. This adaptation can take a variety of forms including acting as a provisioning agent across multiple signaling domains, providing transport protocol adaptation points, and mapping between distributed resource reservation paradigms and the optical network control plane. We have successfully developed the system and demonstrated benefits. The Phoebus system was deployed in Internet2 and in ESnet, as well as in GEANT2, RNP in Brazil and over international links to Korea and Japan. Phoebus is a system that implements a new protocol and associated forwarding infrastructure for improving throughput in high-speed dynamic networks. It was developed to serve the needs of large DOE applications on high-performance networks. The idea underlying the Phoebus model is to embed Phoebus Gateways (PGs) in the network as on-ramps to dynamic circuit networks. The gateways act as protocol translators that allow legacy applications to use dedicated paths with high performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Best Practices and Lessons Learned from Deploying and Operating Large-Scale Data-Centric Parallel File Systems, In: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

The Oak Ridge Leadership Computing Facility (OLCF) has deployed multiple large-scale parallel file systems (PFS) to support its operations. During this process, OLCF acquired significant expertise in large-scale storage system design, file system software development, technology evaluation, benchmarking, procurement, deployment, and operational practices. Based on the lessons learned from each new PFS deployment, OLCF improved its operating procedures, and strategies. This paper provides an account of our experience and lessons learned in acquiring, deploying, and operating large-scale parallel file systems. We believe that these lessons will be useful to the wider HPC community.

Computer Science↗
DOE OSTI2013

Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.

Computer Science↗
DOE OSTI2023

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Exploration of multifidelity UQ sampling strategies for computer network applications.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Here, two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Feng shui of supercomputer memory: positional effects in DRAM and SRAM faults. In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Several recent publications confirm that faults are common in high-performance computing systems. Therefore, further attention to the faults experienced by such computing systems is warranted. In this paper, we present a study of DRAM and SRAM faults in large high-performance computing systems. Our goal is to understand the factors that influence faults in production settings. We examine the impact of aging on DRAM, finding a marked shift from permanent to transient faults in the first two years of DRAM lifetime. We examine the impact of DRAM vendor, finding that fault rates vary by more than 4x among vendors. We examine the physical location of faults in a DRAM device and in a data center; contrary to prior studies, we find no correlations with either. Finally, we study the impact of altitude and rack placement on SRAM faults, finding that, as expected, altitude has a substantial impact on SRAM faults, and that top of rack placement correlates with 20% higher fault rate.

Sridharan, Vilas↗
DOE OSTI2013

20 petaflops simulation of proteins suspensions in crowding conditions, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

We present performance results for the simulation of proteins suspensions in crowding conditions obtained with MUPHY, a computational platform for multi-scale simulations of real-life biofluidic problems. Previous versions of MUPHY have been used in the past for the simulation of blood flow through the human coronary arteries and DNA translocation across nanopores. The simulation exhibits excellent scalability up to 18, 000 K20X Nvidia GPUs and achieves almost 20 Petaflops of aggregate sustained performance with a peak performance of 27.5 Petaflops for the most intensive computing component. Those figures demonstrate once again the flexibility of MUPHY in simulating biofluidic phenomena, exploiting at their best the features of the architecture in use. Preliminary results were obtained in the present case on a completely different platform, the IBM Blue Gene/Q. The combination of novel mathematical models, computational algorithms, hardware technology, code tuning and parallelization techniques required to achieve these results are presented.

Computer Science↗
DOE OSTI2012

A High Performance Computing Network and System Simulator for the Power Grid: NGNS^2

Designing and planing next generation power grid sys- tems composed of large power distribution networks, monitoring and control networks, autonomous generators and consumers of power requires advanced simulation infrastructures. The objective is to predict and analyze in time the behavior of networks of systems for unexpected events such as loss of connectivity, malicious attacks and power loss scenarios. This ultimately allows one to answer questions such as: “What could happen to the power grid if ...”. We want to be able to answer as many questions as possible in the shortest possible time for the largest possible systems. In this paper we present a new High Performance Computing (HPC) oriented simulation infrastructure named Next Generation Network and System Simulator (NGNS2 ). NGNS2 allows for the distribution of a single simulation among multiple computing elements by using MPI and OpenMP threads. NGNS2 provides extensive configuration, fault tolerant and load balancing capabilities needed to simulate large and dynamic systems for long periods of time. We show the preliminary results of the simulator running approximately two million simulated entities both on a 64-node commodity Infiniband cluster and a 48-core SMP workstation.

Simulation, power grid, netowrks↗