Search NASA⌕ Search

SEARCH · Search NASA

Results for “cluster computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Revisiting Scaling Relations for Giant Radio Halos in Galaxy Clusters

Many galaxy clusters host megaparsec-scale radio halos, generated by ultrarelativistic electrons in the magnetized intracluster medium. Correlations between the synchrotron power of radio halos and the thermal properties of the hosting clusters were established in the last decade, including the connection between the presence of a halo and cluster mergers. The X-ray luminosity and redshift-limited Extended GMRT Radio Halo Survey provides a rich and unique dataset for statistical studies of the halos. We uniformly analyze the radio and X-ray data for the GMRT cluster sample, and use the new Planck Sunyaev-Zel'dovich (SZ) catalog to revisit the correlations between the power of radio halos and the thermal properties of galaxy clusters. We find that the radio power at 1.4 GHz scales with the cluster X-ray (0.1-2.4 keV) luminosity computed within R(sub 500) as P(sub 1.4) approx. L(2.1+/-0.2) - 500). Our bigger and more homogenous sample confirms that the X-ray luminous (L(sub 500) > 5 × 10(exp 44) erg/s)) clusters branch into two populations-radio halos lie on the correlation, while clusters without radio halos have their radio upper limits well below that correlation. This bimodality remains if we excise cool cores from the X-ray luminosities. We also find that P(sub 1.4) scales with the cluster integrated SZ signal within R(sub 500), measured by Planck, as P(sub 1.4) approx. Y(2.05+/-0.28) - 500), in line with previous findings. However, contrary to previous studies that were limited by incompleteness and small sample size, we find that "SZ-luminous" Y(sub 500) > 6×10(exp −5) Mpc(exp 2) clusters show a bimodal behavior for the presence of radio halos, similar to that in the radio-X-ray diagram. Bimodality of both correlations can be traced to clusters dynamics, with radio halos found exclusively in merging clusters. These results confirm the key role of mergers for the origin of giant radio halos, suggesting that they trigger the relativistic particle acceleration.

radiation mechanisms↗
NASA NTRS1989

Transonic viscous flow computations about a complete aircraft using the Navier-Stokes equations

A numerical approach for simulating the viscous transonic flow about the complete F-16A fighter aircraft is presented using the Navier-Stokes equations. This finite difference approach utilizes a body conforming zonal grid system to provide appropriate viscous clustering near all body surfaces. A comparison between computational and experimental pressure coefficients is good, and integrated quantities such as lift and drag are within 2.6 percent and 1.6 percent, respectively. The versatility of the method is demonstrated by further modeling the flow inside the inlet up to the compressor face and the exhaust nozzle plume. Results for the F-16A in sideslip are also presented and indicate the proper trends.

Chaderjian, Neal M.↗
NASA NTRS2010

Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

Mengshoel, Ole J.↗
NASA NTRS2005

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Venter, Gerhard↗
DOE OSTI2024

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1995

Job Management Requirements for NAS Parallel Systems and Clusters

A job management system is a critical component of a production supercomputing environment, permitting oversubscribed resources to be shared fairly and efficiently. Job management systems that were originally designed for traditional vector supercomputers are not appropriate for the distributed-memory parallel supercomputers that are becoming increasingly important in the high performance computing industry. Newer job management systems offer new functionality but do not solve fundamental problems. We address some of the main issues in resource allocation and job scheduling we have encountered on two parallel computers - a 160-node IBM SP2 and a cluster of 20 high performance workstations located at the Numerical Aerodynamic Simulation facility. We describe the requirements for resource allocation and job management that are necessary to provide a production supercomputing environment on these machines, prioritizing according to difficulty and importance, and advocating a return to fundamental issues.

Saphir, William↗
NASA NTRS2008

A New Technique using Electron Velocity Data from the Four Cluster Spacecraft to Explore Magnetofluid Turbulence in the Solar Wind

It is now possible in certain circumstances to use velocity moments computed from the Plasma Electron and Current Experiment (PEACE) on the four Cluster spacecraft to determine a number of turbulence properties of the solar wind, including direct measurements of the vorticity and compressibility. Assuming that the four spacecraft are not co-planar and that there is only a linear variation of the plasma variables across the volume defined by the four satellites, one can estimate the curl of the fluid velocity, i.e., the vorticity. From the vorticity it is possible to explore directly intermittent regions in the solar wind where dissipation is likely to be enhanced. In addition, one can estimate directly the Taylor microscale.

Goldstein, Melvyn L.↗
DOE OSTI2024

Modeling Clustered DNA Damage by Ionizing Radiation Using Multinomial Damage Probabilities and Energy Imparted Spectra

Simple and complex clustered DNA damage represent the critical initial damage caused by radiation. In this paper, a multinomial probability model of clustered damage is developed with probabilities dependent on the energy imparted to DNA and surrounding water molecules. The model consists of four probabilities: (A) direct damage of sugar-phosphate moieties leading to SSB, (B) OH− radical formation with subsequent SSB and BD formation, (C) direct damage to DNA bases, and (D) energy imparted to histone proteins and other molecules in a volume not leading to SSB or BD. These probabilities are augmented by introducing probabilities for the relative location of SSB using a ≤10 bp criteria for a double-strand break (DSB) and for the possible success of a radical attack that leads to SSB or BD. Model predictions for electrons, 4He, and 12C ions are compared to the experimental data and show good agreement. Thus, the developed model allows an accurate and rapid computational method to predict simple and complex clustered DNA damage as a function of radiation quality and to explore the resulting challenges to DNA repair.

Biochemistry & Molecular Biology↗
DOE OSTI2024

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray-selected superclusters

Numerical simulations indicate that the clustering of dark matter halos is not only dependent on the halo masses but has a secondary dependence on other properties, such as the assembly history of the halo. This phenomenon, known as the halo assembly bias (HAB), has been found mostly on galaxy scales; observational evidence on larger scales is scarce. In this work, we propose a novel method for exploring HAB on cluster scales using large samples of superclusters. Leveraging the largest-ever X-ray galaxy cluster and supercluster samples obtained from the first SRG/eROSITA all-sky survey, we constructed two subsamples of galaxy clusters that consist of supercluster members and isolated clusters, respectively. After correcting for the selection effects on redshift, mass, and survey depth, we computed the excess in the concentration of the intracluster gas of isolated clusters with respect to supercluster members, defined as δc gas ≡ c gas , ISO /c gas , SC –1 , to investigate the environmental effect on the concentration of clusters, a sign of HAB on cluster scales. We find that the average gas mass concentration of isolated clusters is a few percent higher than that of supercluster members, with a maximum significance of 2.8σ. The result for δc gas varies with the overdensity ratio, f, in supercluster identification, cluster mass proxies, and mass and redshift ranges but remains positive in almost all the measurements. We measure slightly larger δc gas when adopting a higher f for supercluster identification. The δc gas is also higher for low-mass and low-redshift clusters. We performed weak lensing analyses to compare the total mass concentration of the two classes and find a similar trend in total mass concentration as obtained from the gas mass concentration. Our results are consistent with the prediction of HAB on cluster scales, where halos located in denser environments are less concentrated; this trend is stronger for halos with lower masses and at lower redshifts. These phenomena can be explained by the fact that clusters in denser environments, such as superclusters, have experienced more mergers than isolated clusters in their assembling history. This work paves the way to explore HAB with X-ray superclusters and demonstrates that large samples of superclusters with X-ray and weak-lensing data can advance our understanding of the evolution of the large-scale structure.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

ICAP: An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. The algorithm interfaces the rapid numerical processing capacity of a computer with the human ability to integrate qualitative information. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters and the analyst, who evaluate and elect to modify the cluster structure. Clusters can be deleted or lumped pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The ICAP was implemented in APL (A Programming Language), an interactive computer language. The flexibility of the algorithm was evaluated using data from different LANDSAT scenes to simulate two situations: one in which the analyst is assumed to have no prior knowledge about the data and wishes to have the clusters formed more or less automatically; and the other in which the analyst is assumed to have some knowledge about the data structure and wishes to use that information to closely supervise the clustering process. For comparison, an existing clustering method was also applied to the two data sets.

Wharton, S. W.↗
NASA NTRS2020

Predicting Cell Death and Mutation Frequency for a Wide Spectrum of LET by Assuming DNA Break Clustering Inside Repair Domains

Cosmic radiation, which is composed of high charged and energy (HZE) particles, is responsible for cell death and mutation, which may be involved in cancer induction. Mutations are consequences of mis-repaired DNA breaks – especially double-strand breaks (DSBs) – that induce inter- and intra-chromosomal rearrangements (translocations, deletions, inversion). In this study, a computer simulation model is used to investigate the clustering of DSBs in repair domains, previously evidenced by our group in human breast cells [1]. This model is calibrated with experimental data measuring persistent 53BP1 radiation-induced foci (RIF) and is used to explain the high relative biological effectiveness (RBE) of HZE for both cell death and DNA mutation frequencies. We first validate our DSB cluster model using a new track structure model deployed on a simple geometrical configuration for repair domains in the nucleus; then we extend the scope from cell death to mutation induction. This work suggests that mechanism based on DSB repair process can explain several biological effects induced by HZE particles on different type of living cells

Plante, Ianik↗
NASA NTRSDate not supplied

Predicting cell death and mutation frequency for a wide spectrum of LET and across cell lines from DNA break clustering inside repair domains

Cosmic radiation, which is composed of high charged and energy (HZE) particles, is responsible for cell death and mutation, which may be involved in cancer induction. Mutations are consequences of mis-repaired DNA breaks – especially double-strand breaks (DSBs) – that induce inter- and intra-chromosomal rearrangements (translocations, deletions, inversion). In this study, a computer simulation model is used to investigate the clustering of DSBs in repair domains, previously evidenced by our group in human breast cells. This model is calibrated with experimental data measuring persistent 53BP1 radiation-induced foci (RIF) and is used to explain the high relative biological effectiveness (RBE) of HZE for both cell death and DNA mutation frequencies. We first validate our DSB cluster model using a new track structure model deployed on a simple geometrical configuration for repair domains in the nucleus; then we extend the scope from cell death to mutation induction. This work suggests that mechanism based on DSB repair process can explain several biological effects induced by HZE particles on different type of living cells.

Ianik Plante↗
DOE OSTI2025

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need

The promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limited by the accuracy of the energies and atomic forces in the training dataset. Unfortunately, most of these datasets are computed with relatively low-accuracy QM methods, e.g. density functional theory with a moderate basis set. Due to the increased computational cost of more accurate QM methods, e.g. coupled-cluster theory with a complete basis set (CBS) extrapolation, most high-accuracy datasets are much smaller and often do not contain atomic forces. The lack of high-accuracy atomic forces is quite troubling, as training with force data greatly improves the stability and quality of the MLIP compared to training to energy alone. Because most datasets are computed with a unique level of theory, traditional single-fidelity (SF) learning is not capable of leveraging the vast amounts of published QM data. In this study, we apply multi-fidelity learning (MFL) to train an MLIP to multiple QM datasets of different levels of accuracy, i.e. levels of fidelity. Specifically, we perform three test cases to demonstrate that MFL with both low-level forces and high-level energies yields an extremely accurate MLIP—far more accurate than a SF MLIP trained solely to high-level energies and almost as accurate as a SF MLIP trained directly to high-level energies and forces. Therefore, MFL greatly alleviates the need for generating large and expensive datasets containing high-accuracy atomic forces and allows for more effective training to existing high-accuracy energy-only datasets. Indeed, low-accuracy atomic forces and high-accuracy energies are all that are needed to achieve a high-accuracy MLIP with MFL.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Automated clustering-based workload characterization

The demands placed on the mass storage systems at various federal agencies and national laboratories are continuously increasing in intensity. This forces system managers to constantly monitor the system, evaluate the demand placed on it, and tune it appropriately using either heuristics based on experience or analytic models. Performance models require an accurate workload characterization. This can be a laborious and time consuming process. It became evident from our experience that a tool is necessary to automate the workload characterization process. This paper presents the design and discusses the implementation of a tool for workload characterization of mass storage systems. The main features of the tool discussed here are: (1)Automatic support for peak-period determination. Histograms of system activity are generated and presented to the user for peak-period determination; (2) Automatic clustering analysis. The data collected from the mass storage system logs is clustered using clustering algorithms and tightness measures to limit the number of generated clusters; (3) Reporting of varied file statistics. The tool computes several statistics on file sizes such as average, standard deviation, minimum, maximum, frequency, as well as average transfer time. These statistics are given on a per cluster basis; (4) Portability. The tool can easily be used to characterize the workload in mass storage systems of different vendors. The user needs to specify through a simple log description language how the a specific log should be interpreted. The rest of this paper is organized as follows. Section two presents basic concepts in workload characterization as they apply to mass storage systems. Section three describes clustering algorithms and tightness measures. The following section presents the architecture of the tool. Section five presents some results of workload characterization using the tool.Finally, section six presents some concluding remarks.

Pentakalos, Odysseas I.↗
DOE OSTI2025

Galaxy cluster profiles: a Gaussian mixture model approach to halo miscentering

Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center defined as the minimum in the potential well. Departure of the assumed cluster center from the true halo center bias the resultant profile measurements, an effect known as miscentering bias. Currently, miscentering is typically modeled in stacked profiles of clusters with a two parameter model. We use an alternate approach in which the profiles of individual clusters are used with the corresponding likelihood computed using a Gaussian mixture model. We test the approach using halos and the corresponding subhalo profiles from the IllustrisTNG hydrodynamic simulations. We obtain significantly improved estimates of the miscentering parameters for both 3D and projected 2D profiles relevant for imaging surveys. We discuss applications to upcoming cosmological surveys. Our Python package for the Gaussian mixture model is publicly available at https://github.com/KyleMiller1/Halo-Miscentering-Mixture-Model.

Bayesian reasoning↗
NASA NTRS1983

Computation of shock wave/target interaction

Computational results of shock waves impinging on targets and the ensuing diffraction flowfield are presented. A number of two-dimensional cases are computed with finite difference techniques. The classical case of a shock wave/cylinder interaction is compared with shock tube data and shows the quality of the computations on a pressure-time plot. Similar results are obtained for a shock wave/rectangular body interaction. Here resolution becomes important and the use of grid clustering techniques tend to show good agreement with experimental data. Computational results are also compared with pressure data resulting from shock impingement experiments for a complicated truck-like geometry. Here of significance are the grid generation and clustering techniques used. For these very complicated bodies, grids are generated by numerically solving a set of elliptic partial differential equations.

Mark, A.↗
DOE OSTI2024

Grover-QAOA for 3-SAT: quadratic speedup, fair-sampling, and parameter clustering

Abstract The SAT problem is a prototypical NP-complete problem of fundamental importance in computational complexity theory with many applications in science and engineering; as such, it has long served as an essential benchmark for classical and quantum algorithms. This study shows numerical evidence for a quadratic speedup of the Grover Quantum Approximate Optimization Algorithm (G-QAOA) over random sampling for finding all solutions to 3-SAT (All-SAT) and Max-SAT problems. G-QAOA is less resource-intensive and more adaptable for these problems than Grover’s algorithm, and it surpasses conventional QAOA in its ability to sample all solutions. We show these benefits by classical simulations of many-round G-QAOA on thousands of random 3-SAT instances. We also observe G-QAOA advantages on the IonQ Aria quantum computer for small instances, finding that current hardware suffices to determine and sample all solutions. Interestingly, a single-angle-pair constraint that uses the same pair of angles at each G-QAOA round greatly reduces the classical computational overhead of optimizing the G-QAOA angles while preserving its quadratic speedup. We also find parameter clustering of the angles. The single-angle-pair protocol and parameter clustering significantly reduce obstacles to classical optimization of the G-QAOA angles.

Zhang, Zewen (ORCID:000000032258613X)↗
DOE OSTI2026

The high level trigger and express data production at STAR

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗