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At least 235 records · Page 13

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NASA NTRS2008

Transformation of OODT CAS to Perform Larger Tasks

A computer program denoted OODT CAS has been transformed to enable performance of larger tasks that involve greatly increased data volumes and increasingly intensive processing of data on heterogeneous, geographically dispersed computers. Prior to the transformation, OODT CAS (also alternatively denoted, simply, 'CAS') [wherein 'OODT' signifies 'Object-Oriented Data Technology' and 'CAS' signifies 'Catalog and Archive Service'] was a proven software component used to manage scientific data from spaceflight missions. In the transformation, CAS was split into two separate components representing its canonical capabilities: file management and workflow management. In addition, CAS was augmented by addition of a resource-management component. This third component enables CAS to manage heterogeneous computing by use of diverse resources, including high-performance clusters of computers, commodity computing hardware, and grid computing infrastructures. CAS is now more easily maintainable, evolvable, and reusable. These components can be used separately or, taking advantage of synergies, can be used together. Other elements of the transformation included addition of a separate Web presentation layer that supports distribution of data products via Really Simple Syndication (RSS) feeds, and provision for full Resource Description Framework (RDF) exports of metadata.

Mattmann, Chris↗

NASA NTRS2010

A New Measurement of the Bulk Flow of X-Ray Luminous Clusters of Galaxies

We present new measurements of the large-scale bulk flows of galaxy clusters based on five-year WMAP data and a significantly expanded X-ray cluster catalog. Our method probes the flow via measurements of the kinematic Sunyaev-Zel'dovich (SZ) effect produced by the hot gas in moving clusters. It computes the dipole in the cosmic microwave background data at cluster pixels, which preserves the SZ component while integrating down other contributions. Our improved catalog of over 1000 clusters enables us to further investigate possible systematic effects and, thanks to a higher median cluster redshift, allows us to measure the bulk flow to larger scales. We present a corrected error treatment and demonstrate that the more X-ray luminous clusters, while fewer in number, have much larger optical depth, resulting in a higher dipole and thus a more accurate flow measurement. This results in the observed correlation of the dipole derived at the aperture of zero monopole with the monopole measured over the cluster central regions. This correlation is expected if the dipole is produced by the SZ effect and cannot be caused by unidentified systematics (or primary cosmic microwave background anisotropies). We measure that the flow is consistent with approximately constant velocity out to at least [similar, equals]800 Mpc. The significance of the measured signal peaks around 500 h -1 70 Mpc, most likely because the contribution from more distant clusters becomes progressively more diluted by the WMAP beam. However, at present, we cannot rule out that these more distant clusters simply contribute less to the overall motion.

Kashlinsky, A.↗

DOE OSTI2021

An Integrated High-performance Computing and Digital Real-time Simulation Testbed to Benchmark Closed-loop Load Shedding Algorithms in Power Systems

An integrated testbed using digital real-time simulator (DRTS) and a high-performance computing (HPC) cluster is presented here to compare speed and performance of computational schemes to mitigate time-critical issues in electric power systems. The first approach in this testbed validation is taken by running a set of closed-loop load shedding algorithms to compare and contrast two paradigms of arresting cascading failure propagation. Two algorithms involve solving DC and AC power flow model-based optimization problems to compute load shedding at different buses, while a model-based stochastic search using parallel computing provides a viable alternative. The algorithms are implemented in the DRTS-HPC testbed for the IEEE 14-bus benchmark transmission system. As a proof of the concept, simulation results are presented for implementation of closed-loop load-shedding algorithms for cascading failures in the DRTS-HPC testbed

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansätze for Hybrid Variational Quantum Computing

One of the commonly used chemical-inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansatze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansatze exhibits unfavorable scaling with respect to the system size, hindering its practical use on near-term quantum devices. Efforts have been taken to propose some variants of UCC ansatze with better scaling. In this paper we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansatze employing spin-adapted formulation, small amplitude filtration, and entropy-based orbital selection approaches. Numerical results of using our approach on some small molecules have exhibited a significant cost reduction in the number of parameters to be optimized and in the time to convergence compared with conventional UCCSD-VQE simulations. Further, we also discuss the potential application of some machine learning techniques in further exploring the parameter redundancy, providing a possible direction for future studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing

The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures. The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors. We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications. The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1996

Performance Evaluation in Network-Based Parallel Computing

Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.

Dezhgosha, Kamyar↗

DOE OSTI2020

Deconvolute individual genomes from metagenome sequences through short read clustering

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗

DOE OSTI2020

The Installation of Direct Water-Cooling Systems to Reduce Cooling Energy Requirements for High-Performance Computing Centers

A large cluster of High-Performance Computing (HPC) equipment at the Lawrence Livermore National Laboratory in California was retrofitted with an Asetek cooling system. The Asetek system is a hybrid scheme with water-cooled cold plates on high-heat-producing components in the information technology (IT) equipment, and with the remainder of the heat being removed by conventional air-cooling systems. In order to determine energy savings of the Asetek system, data were gathered and analyzed two ways: using top-down statistical models, and bottom-up engineering models. The cluster, “Cabernet”, rejected its heat into a facilities cooling water loop which in turn rejected the heat into the same chilled water system serving the computer-room air handlers (CRAHs) that provided the air-based cooling for the room. Because the “before” and “after” cases both reject their heat into the chilled water system, the only savings is due to reduction in CRAH fan power. The top-down analysis showed a 4% overall energy savings for the data center (power usage effectiveness (PUE) —the ratio of total data center energy to IT energy— dropped from 1.60 to 1.53, lower is better); the bottom-up analysis showed a 3% overall energy savings (PUE from 1.70 to 1.66) and an 11% savings for the Cabernet system by itself (partial PUE of 1.51). Greater savings, on the order of 15-20%, would be possible if the chilled water system was not used for rejecting the heat from the Asetek system. About 37% of the heat from the Cab system was rejected to the cooling water, lower than at other installations.

Earni, Shankar↗

DOE OSTI2022

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ~40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ~3.7 billion candidate molecules.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2000

Message Passing vs. Shared Address Space on a Cluster of SMPs

The convergence of scalable computer architectures using clusters of PCs (or PC-SMPs) with commodity networking has become an attractive platform for high end scientific computing. Currently, message-passing and shared address space (SAS) are the two leading programming paradigms for these systems. Message-passing has been standardized with MPI, and is the most common and mature programming approach. However message-passing code development can be extremely difficult, especially for irregular structured computations. SAS offers substantial ease of programming, but may suffer from performance limitations due to poor spatial locality, and high protocol overhead. In this paper, we compare the performance of and programming effort, required for six applications under both programming models on a 32 CPU PC-SMP cluster. Our application suite consists of codes that typically do not exhibit high efficiency under shared memory programming. due to their high communication to computation ratios and complex communication patterns. Results indicate that SAS can achieve about half the parallel efficiency of MPI for most of our applications: however, on certain classes of problems SAS performance is competitive with MPI. We also present new algorithms for improving the PC cluster performance of MPI collective operations.

Shan, Hongzhang↗

DOE OSTI2022

Electron Binding Energy Spectra of Al n Pt – Clusters—A Combined Experimental and Computational Study

Results of size-selected electron photo-detachment experiments and density functional theory calculations on anionic Al n Pt – , n = 1–7, clusters are presented and analyzed. The measured and calculated spectra of electron binding energies are, overall, in excellent accord with each other. The analysis reveals the general importance of accounting for the multiplicity of structural forms of a given-size cluster that can contribute to its measured spectrum, especially when the clusters are fluxional and/or the conditions of the experiment allow for structural transitions. Here, we show that for the systems studied here, the size-specific peculiarities of the measured spectra can be understood in terms of the combined contributions of corresponding different accessible stable equilibrium conformations, bona-fide transition-state configurations, and electronic-crossing structures that may play the role of effective barriers in electronically nonadiabatic dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Observation of Conformational Simplification upon N-methylation on Amino Acid Iodide Clusters

This work reports a surprising observation that methylation of glycine-iodide cluster leads to less conformations and spectroscopic simplicity. We used cryogenic “iodide-tagging” negative ion photoelectron spectroscopy (NIPES) to probe specific binding sites of three N-methylated glycine derivatives, i.e., N-methylglycine (sarcosine, Sar), N,N-dimethylglycine (Dmg), and N,N,N-trimethylglycine (glycine betaine, Bet). NIPES reveals a progressive spectral simplification of the iodide clusters with increasing methylation due to the presence of fewer contributing structures, a reduction in the number of NH and OH hydrogen bond donating sites, and an increase in the basicity of the amine. Low energy conformers and tautomers of each cluster are computationally identified, and those observed in the experiments are assigned based on excellent agreement between the NIPE spectra and theoretical simulations. Clusters with zwitterionic structures for Gly, Sar, and Dmg are found to be less stable than their canonical forms and do not contribute to the observed spectra. This work demonstrates the power of “iodide-tagging” NIPES in probing conformational and tautomeric structures of amino acid-iodide clusters and provides a molecular level understanding on the effect of methyl substitution on the active binding sites of amino acids.

Cao, Wenjin↗

NASA NTRS1973

Evaluation of computer-aided procedure for detecting surface water

Results from an evaluation of a computer-aided procedure for processing ERTS-1 data to detect and locate surface water are presented. The procedure was evaluated using data from a study area in the vicinity of the Lake Somerville area in Washington County, Texas. The procedure consisted of (1) selecting water training fields, (2) aggregating the training samples together and clustering them into unimodal clusters, (3) computing the mean vector and covariance matrix for each cluster, (4) classifying all of the study area into classes corresponding to the clusters using the maximum likelihood classifier, and (5) thresholding out the nonwater pixels.

Source record↗

DOE OSTI2024

Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces

The objective of this project is to develop, and integrate, high-performance simulation tools capable of predicting plasma-facing component (PFC) operating lifetime and the impact of the evolving surface morphology of tungsten-based PFCs on plasma contamination, including the dynamic recycling of fuel species and tritium retention, in future magnetic fusion devices. Establishing a fundamental physical understanding and developing predictive capabilities of plasma-surface interactions (PSI) requires simultaneously addressing complex and diverse physics occurring over a wide range of length (Angstroms to meters) and time (femtoseconds to years) scales, as well as integrating extensive physical processes across the plasma–surface interface. This requires development of not only detailed physics models and computational strategies at each scale, but also algorithms and methods to couple them effectively in a way that can be robustly validated. Deploying these tools requires the continued development and coupling of leadership-scale computational codes to describe the boundary plasma and the evolving PFC surface, as well as a host of simulations that bridge disparate scales to address complex physical and computational issues at the plasma–surface interface in multi-component materials systems for magnetic fusion energy development beyond ITER.

36 MATERIALS SCIENCE↗

NASA NTRS1992

Would a Galactic bar destroy the globular cluster system?

Five different dynamical Galaxy models are presented for the Galactic potential which satisfy the observed rotation curve but contain a central bar so that the 3-kpc nonintersecting streamlines have a radial velocity of 50 km/s when viewed at 45 deg to the bar axis. The effect of the central bars on the destruction rates of globular clusters in the Galaxy is investigated. The method of Aguilar et al. (1988) is applied to these barred Galaxy models. The unknown tangential velocity components of each observed cluster are drawn randomly from an assumed distribution function. The cluster's orbit is integrated, and the bulge shocking rate is calculated. The median destruction rate of the cluster is computed by sampling a large number of such orbits. The addition of the rotating bar does not strongly affect the destruction rates of globular clusters. There is a small increase in the destruction rate for those clusters within about 2.5 kpc. Thus it is not possible to rule out the existence of a rotating bar on these grounds.

Long, Kevin↗

NASA NTRS1995

Parallel processing for scientific computations

The scope of this project dealt with the investigation of the requirements to support distributed computing of scientific computations over a cluster of cooperative workstations. Various experiments on computations for the solution of simultaneous linear equations were performed in the early phase of the project to gain experience in the general nature and requirements of scientific applications. A specification of a distributed integrated computing environment, DICE, based on a distributed shared memory communication paradigm has been developed and evaluated. The distributed shared memory model facilitates porting existing parallel algorithms that have been designed for shared memory multiprocessor systems to the new environment. The potential of this new environment is to provide supercomputing capability through the utilization of the aggregate power of workstations cooperating in a cluster interconnected via a local area network. Workstations, generally, do not have the computing power to tackle complex scientific applications, making them primarily useful for visualization, data reduction, and filtering as far as complex scientific applications are concerned. There is a tremendous amount of computing power that is left unused in a network of workstations. Very often a workstation is simply sitting idle on a desk. A set of tools can be developed to take advantage of this potential computing power to create a platform suitable for large scientific computations. The integration of several workstations into a logical cluster of distributed, cooperative, computing stations presents an alternative to shared memory multiprocessor systems. In this project we designed and evaluated such a system.

Alkhatib, Hasan S.↗

NASA NTRS1993

Textures and clusters

We discuss the properties of galaxy clusters expected in a texture-seeded, CDM-dominated, Omega = 1 universe. Assuming that the textures are spherical, we use the spherical collapse model to compute the cluster velocity dispersion (or temperature) distribution function. For objects of mass 10 exp 11 to 10 exp 15 solar masses, we find v varies as M super gamma with gamma of about 0.25. An unbiased (b = 1) texture model predicts too many high-velocity dispersion clusters. A biased texture model appears to be compatible with cluster properties inferred from optical and X-ray observations. In the texture model, the cluster velocity distribution functio does not evolve rapidly; thus, the model predicts the existence of rich clusters at moderate redshift (about 1-2).

Bartlett, James G.↗