Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cluster computer”

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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Power API Prototype

The software serves two purposes. The first purpose of the software is to prototype the Sandia High Performance Computing Power Application Programming Interface Specification effort. The specification can be found at http://powerapi.sandia.gov . Prototypes of the specification were developed in parallel with the development of the specification. Release of the prototype will be instructive to anyone who intends to implement the specification. More specifically, our vendor collaborators will benefit from the availability of the prototype. The second is in direct support of the PowerInsight power measurement device, which was co-developed with Penguin Computing. The software provides a cluster wide measurement capability enabled by the PowerInsight device. The software can be used by anyone who purchases a PowerInsight device. The software will allow the user to easily collect power and energy information of a node that is instrumented with PowerInsight. The software can also be used as an example prototype implementation of the High Performance Computing Power Application Programming Interface Specification.

Source record↗
NASA NTRS1983

Radiative accretion of intracluster gas onto dominant galaxies in poor clusters

An analysis of X-ray images of MKW 4, MKW 3, AMW 4, and AMW 7 indicates that the hot gas in poor clusters is cooling and accreting onto the central, dominant galaxies. The deprojection technique of Fabian et al (1980) is applied to deduce the detailed density and temperature profiles for a wide range of assumed parameters. For each cluster, the mass accretion rates computed vary by less than a factor of 2 over the full range of the assumed parameters. Examinations of the gas and light density profiles, and of the effects of contraction of the gravitational potential, lead to the conclusion that the high gas densities giving rise to cooling flows are not primarily the result of dynamical evolution of the cluster, especially the merger of galaxies to form the dominant galaxy. Instead, the general initial condition of high material density favors both mergers and cooling flows.

Stewart, G. C.↗
DOE OSTI2015

Accurate transport properties for H–CO and H–CO{sub 2}

Transport properties for collisions of hydrogen atoms with CO and CO{sub 2} have been computed by means of quantum scattering calculations. The carbon oxides are important species in hydrocarbon combustion. The following potential energy surfaces (PES’s) for the interaction of the molecule fixed in its equilibrium geometry were employed: for H–CO, the PES was taken from the work of Song et al. [J. Phys. Chem. A 117, 7571 (2013)], while the PES for H–CO{sub 2} was computed in this study by a restricted coupled cluster method that included single, double, and (perturbatively) triple excitations. The computed transport properties were found to be significantly different from those computed by the conventional approach that employs isotropic Lennard-Jones (12-6) potentials. The effect of using the presently computed accurate transport properties in 1-dimensional combustion simulations of methane-air flames was investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Arcs from gravitational lensing

The proposal made by Paczynski (1987) that the arcs of blue light found recently in two cluster cores are gravitationally lensed elongated images of background galaxies is investigated. It is shown that lenses that are circularly symmetric in projection produce pairs of arcs, in conflict with the observations. However, more realistic asymmetric lenses produce single arcs, which can become as elongated as the observed ones whenever the background galaxy is located on or close to a cusp caustic. Detailed computer simulations of lensing by clusters using a reasonable model of the mass distribution are presented. Elongated and curved lensed images longer than 10 arcsec occur in 12 percent of the simulated clusters. It is concluded that the lensing hypothesis must be taken seriously.

Grossman, Scott A.↗
DOE OSTI2025

Structure and Synthesizability of Iron–Sulfur Metal–Organic Frameworks

Sulfur-based metal–organic frameworks (MOFs) and coordination polymers (CPs) are an emerging class of hybrid materials that have received growing attention due to their magnetic, conductive, and catalytic properties with potential applications in electrocatalysis and energy storage. In this work, we report a high-throughput virtual screening protocol to predict the synthesizability of candidate metal–sulfur MOFs/CPs by computing the thermodynamically stable structures resulting from a particular combination of metal cluster, linker, cation, and synthetic conditions. Free energies are computed by using all-atom classical mechanical thermodynamic integration. Low-free-energy structures are refined using ab initio density functional theory, and pair distribution functions and powder X-ray diffraction patterns are calculated to complement and guide experimental structure determination. We validate the computational approach by retrospective predictions of the stable structure produced by experimental syntheses, and a subsequent screen predicts Fe 4 S 4 -BDT–TPP as a new thermodynamically stable one-dimensional (1D) CP comprising a redox-active Fe 4 S 4 cluster, a 1,4-benzenedithiolate (BDT) linker, and a tetraphenylphosphonium (TPP) countercation. Furthermore, this material is experimentally synthesized, and the 1D chain structure of the crystal is confirmed using microcrystal electron diffraction. The computational screening pipeline is generically transferable to neutral and ionic MOFs/CPs comprising arbitrary metal clusters, linkers, cations, and synthetic conditions, and we make it freely available as an open source tool to guide and accelerate the discovery and engineering of novel porous materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Interactions of mobile helium clusters with surfaces and grain boundaries of plasma-exposed tungsten

We report results of atomistic computations for the interactions of small mobile helium clusters (He{sub n}) with free surfaces and grain boundaries (GBs) in tungsten toward development of continuum drift-diffusion-reaction models for the dynamics of mobile helium clusters in plasma-exposed tungsten. Molecular-statics (MS) simulations based on reliable many-body interatomic potentials are carried out for He{sub n} (1 ≤ n ≤ 7) clusters near sinks to obtain the potential energy profiles of the He{sub n} clusters as a function of the clusters' center-of-mass distance from a sink. Sinks investigated include surfaces, GBs, and regions in the vicinity of junctions where GBs intersect free surfaces. Elastic interaction potentials based on elastic inclusion theory provide an excellent description of the MS results for the cluster-sink interactions. The key parameter in the elastic models is the sink segregation strength, which is found to increase with increasing cluster size. Such cluster-sink interactions are responsible for the migration of small helium clusters by drift and for helium segregation on surfaces and grain boundaries in tungsten. Such helium segregation on sinks is observed in large-scale molecular-dynamics simulations of helium aggregation in model polycrystalline tungsten at 933 K upon helium implantation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Understanding GPU errors on large-scale HPC systems and the implications for system design and operation

Increase in graphics hardware performance and improvements in programmability has enabled GPUs to evolve from a graphics-specific accelerator to a general-purpose computing device. Titan, the world's second fastest supercomputer for open science in 2014, consists of more dum 18,000 GPUs that scientists from various domains such as astrophysics, fusion, climate, and combustion use routinely to run large-scale simulations. Unfortunately, while the performance efficiency of GPUs is well understood, their resilience characteristics in a large-scale computing system have not been fully evaluated. We present a detailed study to provide a thorough understanding of GPU errors on a large-scale GPU-enabled system. Our data was collected from the Titan supercomputer at the Oak Ridge Leadership Computing Facility and a GPU cluster at the Los Alamos National Laboratory. We also present results from our extensive neutron-beam tests, conducted at Los Alamos Neutron Science Center (LANSCE) and at ISIS (Rutherford Appleron Laboratories, UK), to measure the resilience of different generations of GPUs. We present several findings from our field data and neutron-beam experiments, and discuss the implications of our results for future GPU architects, current and future HPC computing facilities, and researchers focusing on GPU resilience.

Tiwari, Devesh↗
DOE OSTI2024

Performance Analysis of Data Processing in Distributed File Systems with Near Data Processing

In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark and Hadoop manage the increasing amount and velocity of data by improving data placement and processing speeds. However, they face inherent limitations due to the essential data movement required for processing. In this paper, we explore the Skyhook framework, a novel extension of the Ceph distributed system, which significantly reduces the need for data movement. We present an extensive case study using the Skyhook framework, applying it with the TPC-H and K-means clustering algorithms. More specifically, we leverage the TPC-H benchmark to distinguish between CPU-intensive and I/O-intensive tasks. We explore the integration of K-means clustering into SQL, coupled with a near-data processing system to offload the computational burden of the K-means clustering algorithm to storage nodes. We conduct a comprehensive performance evaluation of distributed data processing applications across three processing approaches: traditional layout (baseline), optimized layout, and near-data processing. Additionally, we introduce the use of the FIO tool to simulate real-world system workloads, enabling the measurement of performance metrics such as average latency and CPU utilization. Our research is a significant advance in understanding how to optimize data processing systems to meet the demands of the modern data landscape.

Hou, Shiyue↗
DOE OSTI2025

Uncertainty estimation of bifurcated solutions in the Rayleigh–Bénard problem for advanced nuclear reactors applications

Multiphysics models of nuclear reactors frequently comprise nonlinear systems of equations. The nonlinear nature of these models could lead to solution bifurcations, where a small change in a certain parameter, e.g., the thermophysical properties of the coolant, can lead to a sudden change in the system’s behavior. At the point in parameter space where this happens, called a critical point, the Jacobian matrix of the model’s nonlinear operator becomes singular potentially permitting multiple solutions to coexist. In this paper, we perform uncertainty estimation (UE) in a parameter range that includes bifurcated solutions within the context of Rayleigh–Bénard problem. We perform this analysis assuming uncertain temperature difference, and tilt angle for the iterative solution algorithm with a unit Prandtl number (Pr = 1). Also, we perform this analysis under uncertain thermophysical properties for both FLiBe molten salt and liquid sodium as working fluid. We deploy two approaches to compute statistical moments for the resulting distributions of selected flow-field variables. The first approach is the blind computation of the mean and the standard deviation without any consideration of solution bifurcation, while the second approach utilizes k-means clustering to cluster each branch’s solutions together and compute separate statistical moments for each branch. The statistical distributions are obtained by perturbing the selected parameters about nominal values that correspond to a solution on one of the valid branches, and that solution is used as initial guess for the iterative solution algorithm. We found that perturbation of any parameter when its nominal value is close to its critical point always leads to branch jumping, i.e., the iterations converge to a solution on a branch different from the branch of the initial guess. This produces a statistical ensemble comprised of fundamentally different solutions leading to wrong mean values and uncertainty estimates, whereas clustering provides an efficient way to deal with this type of computation. This work is important for developing Gen IV nuclear systems because many of these systems rely on natural convection for cooling especially in accident conditions.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nowcasting Earthquakes in Southern California With Machine Learning: Bursts, Swarms, and Aftershocks May Be Related to Levels of Regional Tectonic Stress

Abstract Seismic bursts in Southern California are sequences of small earthquakes strongly clustered in space and time and include seismic swarms and aftershock sequences. A readily observable property of these events, the radius of gyration ( R G ), allows us to connect the bursts to the temporal occurrence of the largest M ≥ 7 earthquakes in California since 1984. In the Southern California earthquake catalog, we identify hundreds of these potentially coherent space‐time structures in a region defined by a circle of radius 600 km around Los Angeles. We compute R G for each cluster then filter them to identify those bursts with large numbers of events closely clustered in space, which we call “compact” bursts. Our basic assumption is that these compact bursts reflect the dynamics associated with large earthquakes. Once we have filtered the burst catalog, we apply an exponential moving average to construct a time series for the Southern California region. We observe that the R G of these bursts systematically decreases prior to large earthquakes, in a process that we might term “radial localization.” The R G then rapidly increases during an aftershock sequence, and a new cycle of “radial localization” then begins. These time series display cycles of recharge and discharge reminiscent of seismic stress accumulation and release in the elastic rebound process. The complex burst dynamics we observe are evidently a property of the region as a whole, rather than being associated with individual faults. This new method allows us to improve earthquake nowcasting, which is a technique to evaluate the current state of hazard in a seismically active region.

58 GEOSCIENCES↗
DOE OSTI2013

Final Report: Super Instruction Architecture for Scalable Parallel Computations

The most advanced methods for reliable and accurate computation of the electronic structure of molecular and nano systems are the coupled-cluster techniques. These high-accuracy methods help us to understand, for example, how biological enzymes operate and contribute to the design of new organic explosives. The ACES III software provides a modern, high-performance implementation of these methods optimized for high performance parallel computer systems, ranging from small clusters typical in individual research groups, through larger clusters available in campus and regional computer centers, all the way to high-end petascale systems at national labs, including exploiting GPUs if available. This project enhanced the ACESIII software package and used it to study interesting scientific problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Analytical and EZmock covariance validation for the DESI 2024 results

The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full-Shape two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on an ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full-Shape analyses, which motivates the use of a corrected mock covariance for DESI's 2024 Full Shape analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.

Comparative network analysis↗
DOE OSTI2024

Formation and transformation of iron oxy-hydroxide precursor clusters to ferrihydrite

Iron (Fe) oxy-hydroxide minerals such as ferrihydrite (Fh) are ubiquitous in Earth-surface environments and important in biogeochemical element cycling. Recent research has suggested that their formation is preceded by the precipitation of ultrasmall (~1 nm) Keggin-like Fe oxy-hydroxide clusters. However, relatively little is understood about the structure of the precursor clusters and the impacts of pH and time on their growth and transformation to more stable phases. Here, we used a new method that involves mixed flow reactors (MFR) to synthesize these Fe oxy-hydroxide precursor clusters at pH 1.0, 1.5, 2.5, and 4.5. In situ and ex situ synchrotron scattering measurements and laboratory small-angle X-ray scattering (SAXS) were used to study the structure and size of Fe oxy-hydroxide clusters and their transformation products, respectively. Results show that with increasing pH, the particle size and structural order of samples increase, forming solids that resemble 2-line Fh at pH 4.5. The experimental data were compared with X-ray pair distribution functions (PDF) calculated for a range of Fe(III) oxyhydroxide clusters, including Fe 13 Keggin isomers computed previously using density functional theory (DFT), which yielded at best only partial agreement at short range (<5 Å). Aging of the clusters synthesized at pH 1.5 and 2.5 results in growth and transformation via Ostwald ripening to mixtures of goethite (Gt) and lepidocrocite (Lp). This process was inhibited by immediately reacting the early-formed clusters with phosphate (PO 4 3– ), suggesting that oxyanion surface complexes can stabilize the initial clusters by preventing growth and crystallization to more stable phases.

58 GEOSCIENCES↗
NASA NTRS2010

Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

Shameldin, Ramez Ahmed↗
DOE OSTI2016

Performance Improvements to the Cross Section Calculation in MPACT

The MPACT code is the primary deterministic neutron transport solver available in the Virtual Environment for Reactor Applications (VERA) as part of the Consortium for Advanced Simulation of Light Water Reactors (CASL). In contrast to the conventional two-step method using homogenization techniques, MPACT employs a direct transport methodology, where the transport calculation is performed with the explicit reactor core geometry, material compositions, and temperature distribution. As the direct whole-core transport calculation requires significant computational resources, performance improvements have been recently conducted in various components of MPACT. The initial MPACT profiling was performed on the EOS cluster at the Oak Ridge Leadership Computing Facility. Table I shows that the cross section processing accounted for 74% and 33% of the total runtime for VERA Progression Problems 5 and 7. Starting with a problem-independent multigroup (MG) library, two primary functionalities are performed in MPACT to obtain the problem-dependent MG cross sections: (1) the resonance self-shielding calculation, which currently MPACT is approaching with the subgroup method; (2) use of equivalence cross section from resonance calculation to compute the microscopic cross sections, and thus, the macroscopic cross sections of material regions. As shown in Table I, the time distribution in cross section processing varies from case to case. For Problem 5, a 2-D quarter core depletion case, the macroscopic XS dominates due to the increased number of isotopes in the depleted fuel. For Problem 7, a 3-D quarter core PWR at HFP and BOL, more subgroup calculations are performed to obtain updated self-shielded cross sections during the iteration. In this summary, the approaches to reduce the computing time in cross section processing are presented. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Symplectic multi-particle tracking on GPUs

A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

ENSIGN

ENSIGN is a data analytics software package offering a modern unsupervised machine learning solution for scalable discovery in Big Data. The analytics in ENSIGN are based on an advanced mathematical tool called tensor decomposition and they are optimized to run efficiently on a range of computing platforms (from small multicore Desktop platforms to large Supercomputing clusters and novel high-end memory-driven computing platforms such as HPE Superdome Flex). ENSIGN enables the user to extract deep insights from the entirety of massive-scale (100s of Gigabytes or Terabytes scale) multidimensional data. ENSIGN uncovers latent patterns in data without the user having to specify or describe what the patterns are; the user, in the first place, may not even know such patterns existed and that they have to look for such patterns. The insights gained from ENSIGN could be trailheads that can be used as starting points for deeper forensic investigation.

Baskaran, Muthu↗