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All sourcesNASA NTRSDOE OSTI
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.↗
NASA NTRS2003

Eigensolver for a Sparse, Large Hermitian Matrix

A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).

Tisdale, E. Robert↗
DOE OSTI2014

Understanding I/O workload characteristics of a Peta-scale storage system

Understanding workload characteristics is critical for optimizing and improving the performance of current systems and software, and architecting new storage systems based on observed workload patterns. In this paper, we characterize the I/O workloads of scientific applications of one of the world's fastest high performance computing (HPC) storage cluster, Spider, at the Oak Ridge Leadership Computing Facility (OLCF). OLCF flagship petascale simulation platform, Titan, and other large HPC clusters, in total over 250 thousands compute cores, depend on Spider for their I/O needs. We characterize the system utilization, the demands of reads and writes, idle time, storage space utilization, and the distribution of read requests to write requests for the Peta-scale Storage Systems. From this study, we develop synthesized workloads, and we show that the read and write I/O bandwidth usage as well as the inter-arrival time of requests can be modeled as a Pareto distribution. We also study the I/O load imbalance problems using I/O performance data collected from the Spider storage system.

I/O↗
NASA NTRS1978

Compton scattering of microwave background radiation by gas in galaxy clusters

Based on data on the X-ray spectrum of the Coma cluster, interpreted as thermal bremsstrahlung, the expected brightness depletion from Compton scattering of the microwave background in the direction of the cluster is computed. The calculated depletion is about one-third that recently observed by Gull and Northover, and the discrepancy is discussed. In comparing the observed microwave depletion in the direction of other clusters which are X-ray sources it is found that there is no correlation with the cluster X-ray luminosity. Consequently, the microwave depletion observations cannot yet be taken as good evidence for a thermal bremsstrahlung origin for the X-ray emission. The perturbation from Compton scattering of photons on the high-frequency (Wien) tail of the blackbody distribution is computed and found to be much larger than predicted in previous calculations. In the Wien tail the effect is a relative increase in the blackbody intensity that is appreciably greater in magnitude than the depletion in the Rayleigh-Jeans domain.

Gould, R. J.↗
DOE OSTI2018

The Hardware of Smaller Clusters

Chris Saunders and three technologists are in high demand from Sandia's deep learning teams, and they're kept busy by building new clusters of computer nodes for researchers who need the power of supercomputing on a smaller scale. Sandia researchers working on Laboratory Directed Research & Development (LDRD) projects, or innovative ideas for solutions on short timeframes, formulate new ideas on old themes and frequently rely on smaller cluster machines to help solve problems before introducing their code to larger HPC resources. These research teams need an agile hardware and software environment where nascent ideas can be tested and cultivated on a smaller scale. Saunders and his team at Sandia's Science and Engineering Computing Environments are successfully enabling this research by creating pipelines for emerging code—from Cloud, to containers, to virtual machines—that build the right environment quickly to help teams solve their problems in a matter of days rather than months. While the larger HPC sources are available, it's these smaller clusters that can rapidly build a foundation for teams to build on for later development on larger systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Quantum chemical study of the geometrical and electronic structures of ScSi{sub 3}{sup −/0} clusters and assignment of the anion photoelectron spectra

The geometrical and electronic structures of ScSi{sub 3}{sup −/0} clusters have been studied with the B3LYP, CCSD(T), and CASPT2 methods. The ground state of the anionic cluster was evaluated to be the {sup 1}A{sub 1} of rhombic η{sup 2}-(Si{sub 3})Sc{sup −} isomer, whereas that of the neutral cluster was computed to be the {sup 2}A{sub 1} of the same isomer. All features in the 266 and 193 nm photoelectron spectra of ScSi{sub 3}{sup −} cluster were interpreted by the one- and two-electron detachments from the {sup 1}A{sub 1} of rhombic η{sup 2}-(Si{sub 3})Sc{sup −} isomer. The Franck-Condon factor simulation results show that the first broad band starting at 1.78 eV in the spectra comprises several vibrational progression peaks of two totally symmetric modes with the corresponding frequencies of 296 and 354 cm{sup −1}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Cluster partition function and invariants of 3-manifolds

We review some recent developments in Chern-Simons theory on a hyperbolic 3-manifold M with complex gauge group G. We focus on the case G = SL(N, C) and with M a knot complement. The main result presented in this note is the cluster partition function, a computational tool that uses cluster algebra techniques to evaluate the Chern-Simons path integral. We also review various applications and open questions regarding the cluster partition function and some of its relation with string theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
DOE OSTI2025

Parallel Programming in MCNP6

Monte Carlo N-Particle (MCNP)1 is a general-purpose Monte Carlo particle transport code developed by Los Alamos National Laboratory (LANL). To efficiently handle long simulations, MCNP version 6 (MCNP6) supports parallel execution using two primary programming models: • Shared-memory task-based threading using OpenMP (Open Multi-Processing), and • Distributed-memory calculations using MPI (Message Passing Interface). The OpenMP and MPI programming models enable MCNP6 to scale from desktop systems to high-performance computing (HPC) clusters, allowing users to run MCNP in one of three parallel modes: • OpenMP-only, • MPI-only, and • Hybrid (MPI + OpenMP). The choice of parallelization mode depends on the underlying computer architecture and the characteristics of the simulation problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cluster Configuration Spaces of Finite Type

For each Dynkin diagram $D$, we define a ''cluster configuration space'' ${\mathcal{M}}_D$ and a partial compactification ${\widetilde {\mathcal{M}}}_D$. For $D = A_{n-3}$, we have ${\mathcal{M}}_{A_{n-3}} = {\mathcal{M}}_{0,n}$, the configuration space of $n$ points on ${\mathbb P}^1$, and the partial compactification ${\widetilde {\mathcal{M}}}_{A_{n-3}}$ was studied in this case by Brown. The space ${\widetilde {\mathcal{M}}}_D$ is a smooth affine algebraic variety with a stratification in bijection with the faces of the Chapoton-Fomin-Zelevinsky generalized associahedron. The regular functions on ${\widetilde {\mathcal{M}}}_D$ are generated by coordinates $u_\gamma$, in bijection with the cluster variables of type $D$, and the relations are described completely in terms of the compatibility degree function of the cluster algebra. As an application, we define and study cluster algebra analogues of tree-level open string amplitudes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

First principle calculations of effective exchange integrals: Comparison between SR (BS) and MR computational results

First principle calculations of effective exchange integrals (J) in the Heisenberg model for diradical species were performed by both symmetry-adapted (SA) multi-reference (MR) and broken-symmetry (BS) single reference (SR) methods. Mukherjee-type (Mk) state specific (SS) MR coupled-cluster (CC) calculations by the use of natural orbital (NO) references of ROHF, UHF, UDFT and CASSCF solutions were carried out to elucidate J values for di- and poly-radical species. Spin-unrestricted Hartree Fock (UHF) based coupled-cluster (CC) computations were also performed to these species. Comparison between UHF-NO(UNO)-MkMRCC and BS UHF-CC computational results indicated that spin-contamination of UHF-CC solutions still remains at the SD level. In order to eliminate the spin contamination, approximate spin-projection (AP) scheme was applied for UCC, and the AP procedure indeed corrected the error to yield good agreement with MkMRCC in energy. The CC double with spin-unrestricted Brueckner's orbital (UBD) was furthermore employed for these species, showing that spin-contamination involved in UHF solutions is largely suppressed, and therefore AP scheme for UBCCD removed easily the rest of spin-contamination. We also performed spin-unrestricted pure- and hybrid-density functional theory (UDFT) calculations of diradical and polyradical species. Three different computational schemes for total spin angular momentums were examined for the AP correction of the hybrid (H) UDFT. HUDFT calculations followed by AP, HUDFT(AP), yielded the S-T gaps that were qualitatively in good agreement with those of MkMRCCSD, UHF-CC(AP) and UB-CC(AP). Thus a systematic comparison among MkMRCCSD, UCC(AP) UBD(AP) and UDFT(AP) was performed concerning with the first principle calculations of J values in di- and poly-radical species. It was found that BS (AP) methods reproduce MkMRCCSD results, indicating their applicability to large exchange coupled systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud

Here we present a unique function-as-a-service (FaaS) dataset capturing the use of the Globus Compute (previously funcX) platform. Globus Compute implements a federated model via which users may deploy endpoints on arbitrary remote computers, from the edge to high performance computing (HPC) cluster, and they may then invoke Python functions on those endpoints via a reliable cloud -hosted service. The dataset covers 31 weeks and includes 2121472 task submissions from 252 users executed on 580 remote computing endpoints. It includes 277386 registered functions. We describe the dataset and various observations, some that are similar to other FaaS datasets, for example, that 74% of tasks run for less than 1 s, and some that are unique to Globus Compute, for example, that endpoints are used in different ways and that the majority of functions are related to scientific computing and machine learning. To the best of our knowledge, this dataset represents the first federated FaaS dataset that includes user workloads, distributed computing endpoints, and analysis of registered function bodies. We expect the dataset to be useful for researching FaaS architectures, workload modeling, container warming, and other distributed computing architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effects of Ionic Group Distribution on the Structure and Dynamics of Amorphous Polymer Melts

Ionizable groups tethered to a polymer backbone often associate to form ionic clusters, whose features are determined by the balance of the polymer backbone and electrostatics. These assemblies impact the dynamics of the macromolecules and their ability to transport ions. Here, using fully atomistic molecular dynamics (MD) simulations, we investigate the effects of the distribution of ionizable groups along the polymer backbone on cluster characteristics and the resulting impacts on the structure and dynamics of amorphous polymers. Particularly, we probe polystyrene sulfonates (PSS) with random, precise, and block configurations of the SO 3 – sulfonate groups along the backbone with Na + as the counterion. We find that the distribution of the ionic groups affects the shape and distribution of the clusters as well as the internal packing of the ionizable groups in the cluster and the number of unique chains that participate in each cluster, affecting the structure and the dynamics of the polymers. Furthermore, the signature of ionic clusters, observed in the static structure factor S(q) for all three distributions, is significantly more pronounced for the precise and blocky polymers compared to the random one. Remarkably, we find that the local mobility of the polymer segments is not only affected by the number and size of the clusters but also by the number of polymer chains associated with clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗
NASA NTRS2011

EOS MLS Level 2 Data Processing Software Version 3

This software accepts the EOS MLS calibrated measurements of microwave radiances products and operational meteorological data, and produces a set of estimates of atmospheric temperature and composition. This version has been designed to be as flexible as possible. The software is controlled by a Level 2 Configuration File that controls all aspects of the software: defining the contents of state and measurement vectors, defining the configurations of the various forward models available, reading appropriate a priori spectroscopic and calibration data, performing retrievals, post-processing results, computing diagnostics, and outputting results in appropriate files. In production mode, the software operates in a parallel form, with one instance of the program acting as a master, coordinating the work of multiple slave instances on a cluster of computers, each computing the results for individual chunks of data. In addition, to do conventional retrieval calculations and producing geophysical products, the Level 2 Configuration File can instruct the software to produce files of simulated radiances based on a state vector formed from a set of geophysical product files taken as input. Combining both the retrieval and simulation tasks in a single piece of software makes it far easier to ensure that identical forward model algorithms and parameters are used in both tasks. This also dramatically reduces the complexity of the code maintenance effort.

Livesey, Nathaniel J.↗
DOE OSTI2020

The Hardware of Smaller Clusters (V.3.0)

Chris Saunders and three technologists are in high demand from Sandia’s deep learning teams, and they’re kept busy by building new clusters of computer nodes for researchers who need the power of supercomputing on a smaller scale. Sandia researchers working on Laboratory Directed Research & Development (LDRD) projects, or innovative ideas for solutions on short timeframes, formulate new ideas on old themes and frequently rely on smaller cluster machines to help solve problems before introducing their code to larger HPC resources. These research teams need an agile hardware and software environment where nascent ideas can be tested and cultivated on a smaller scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Searching for metastable particles using graph computing

The reconstruction of charged particle trajectories at the Large Hadron Collider and future colliders relies on energy depositions in sensors placed at distances ranging from a centimeter to a meter from the colliding beams. We propose a method of detecting charged particles that decay invisibly after traversing a short distance of about 25 cm inside the experimental apparatus. One of the decay products may constitute the dark matter known to be 84% of all matter at galactic and cosmological distance scales. Our method uses graph computing to cluster spacepoints recorded by two-dimensional silicon pixel sensors into mathematically-defined patterns. The algorithm may be implemented on silicon-based integrated circuits using field-programmable gate array technology to augment or replace traditional computing platforms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2011

Structure of 2,4-Diaminopyrimidine - Theobromine Alternate Base Pairs

We report the structure of clusters of 2,4-diaminopyrimidine with 3,7-dimethylxanthine (theobromine) in the gas phase determined by IR-UV double resonance spectroscopy in both the near-IR and mid-IR regions in combination with ab initio computations. These clusters represent potential alternate nucleobase pairs, geometrically equivalent to guanine-cytosine. We have found the four lowest energy structures, which include the Watson-Crick base pairing motif. This Watson-Crick structure has not been observed by resonant two-photon ionization (R2PI) in the gas phase for the canonical DNA base pairs.

Gengeliczki, Zsolt↗