Parallel linear equation solvers for finite element computations

The overall objective of this research is to develop efficient methods for the solution of linear and nonlinear systems of equations on parallel and supercomputers, and to apply these methods to the solution of problems in structural analysis. Attention has been given so far only to linear equations. The methods considered for the solution of the stiffness equation Kx=f have been Choleski factorization and the conjugate gradient iteration with SSOR and Incomplete Choleski preconditioning. More detail on these methods will be given on subsequent slides. These methods have been used to solve for the static displacements for the mast and panel focus problems in conjunction with the CSM testbed system based on NICE/SPAR

MiniGhost : a miniapp for exploring boundary exchange strategies using stencil computations in scientific parallel computing.

A broad range of scientific computation involves the use of difference stencils. In a parallel computing environment, this computation is typically implemented by decomposing the spacial domain, inducing a 'halo exchange' of process-owned boundary data. This approach adheres to the Bulk Synchronous Parallel (BSP) model. Because commonly available architectures provide strong inter-node bandwidth relative to latency costs, many codes 'bulk up' these messages by aggregating data into a message as a means of reducing the number of messages. A renewed focus on non-traditional architectures and architecture features provides new opportunities for exploring alternatives to this programming approach. In this report we describe miniGhost, a 'miniapp' designed for exploration of the capabilities of current as well as emerging and future architectures within the context of these sorts of applications. MiniGhost joins the suite of miniapps developed as part of the Mantevo project

Physics-assisted Generative Adversarial Network for X-Ray Tomography

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory reconstructions. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Synthetic objects with spatial correlations are integrated circuits (IC) from a proposed model CircuitFaker. Compared with maximum-likelihood estimation, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. We further attribute the improvement to the learned prior by reconstructing objects created without spatial correlations. The advantages of using a prior from deep learning in X-ray tomography may further enable low-photon nanoscale imaging.Comment: arXiv admin note: text overlap with arXiv:2111.0801

HIV-1 Epidemic in the Caribbean Is Dominated by Subtype B

The molecular epidemiology of HIV-1 in the Caribbean has been described using partial genome sequencing; subtype B is the most common subtype in multiple countries. To expand our knowledge of this, nearly full genome amplification, sequencing and analysis was conducted.Virion RNA from sera collected in Haiti, Dominican Republic, Jamaica and Trinidad and Tobago were reverse transcribed, PCR amplified, sequenced and phylogenetically analyzed. Nearly full genomes were completed for 15 strains; partial pol was done for 67 strains. All but one of the 67 strains analyzed in pol were subtype B; the exception was a unique recombinant of subtypes B and C collected in the Dominican Republic. Of the nearly full genomes of 14 strains that were subtype B in pol, all were subtype B from one end of the genome to the other and not inter-subtype recombinants. Surprisingly, the Caribbean subtype B strains clustered significantly with each other and separate from subtype B from other parts of the pandemic.The more complete analysis of HIV-1 from 4 Caribbean countries confirms previous research using partial genome analysis that the predominant subtype in circulation was subtype B. The Caribbean strains are phylogenetically distinct from other subtype B strains although the biological meaning of this finding is unclear

A Tabletop X-Ray Tomography Instrument for Nanometer-Scale Imaging: Integration of a Scanning Electron Microscope with a Transition-Edge Sensor Spectrometer

X-ray nanotomography is a powerful tool for the characterization of nanoscale materials and structures, but is difficult to implement due to competing requirements on X-ray flux and spot size. Due to this constraint, state-of-the-art nanotomography is predominantly performed at large synchrotron facilities. Compact X-ray nanotomography tools operated in standard analysis laboratories exist, but are limited by X-ray optics and destructive sample preparation techniques. We present a laboratory-scale nanotomography instrument that achieves nanoscale spatial resolution while changing the limitations of conventional tomography tools. The instrument combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) microcalorimeter. The electron beam generates a highly focused X-ray spot in a metal target, while the TES spectrometer isolates target photons with high signal-to-noise. This combination of a focused X-ray spot, energy-resolved X-ray detection, and unique system geometry enable nanoscale, element-specific X-ray imaging in a compact footprint. The proof-of-concept for this approach to X-ray nanotomography is demonstrated by imaging 160 nm features in three dimensions in a Cu-SiO2 integrated circuit, and a path towards finer resolution and enhanced imaging capabilities is discussed.Comment: The following article has been submitted to Physical Review Applie

Supercomputer and cluster performance modeling and analysis efforts:2004-2006.

This report describes efforts by the Performance Modeling and Analysis Team to investigate performance characteristics of Sandia's engineering and scientific applications on the ASC capability and advanced architecture supercomputers, and Sandia's capacity Linux clusters. Efforts to model various aspects of these computers are also discussed. The goals of these efforts are to quantify and compare Sandia's supercomputer and cluster performance characteristics; to reveal strengths and weaknesses in such systems; and to predict performance characteristics of, and provide guidelines for, future acquisitions and follow-on systems. Described herein are the results obtained from running benchmarks and applications to extract performance characteristics and comparisons, as well as modeling efforts, obtained during the time period 2004-2006. The format of the report, with hypertext links to numerous additional documents, purposefully minimizes the document size needed to disseminate the extensive results from our research

Application characteristics and performance on a Cray XE6

ABSTRACT: In this paper, we will explore the performance of two applications on a Cray XE6 and their performance improvement from previous machines, including the XT5 and the XT6. These two applications show different scaling effects as we go from machine to machine and we will explore the differences in the applications to explain these differences. We will use profiling and other tools to better understand resource contention within and between nodes and how that changes with the evolution of the machines with changes in processors and network

Characterizing Compiler Performance for the AMD . . .

Application performance on a high performance, parallel platform depends on a variety of factors, the most important being the performance of the high speed interconnect and the compute node processor. The performance of the compute processor depends on how well the compiler optimizes for a given processor architecture, and how well it optimizes the applications source code. An analysis of uni-processor and parallel performance using different AMD Opteron compilers on key SNL application codes is presented.

The Optimization of a Shaped-Charge Design Using Parallel Computers

Current supercomputers use large parallel arrays of tightly coupled processors to achieve levels of performance far surpassing conventional vector supercomputers. Shock-wave physics codes have been developed for these new supercomputers at Sandia National Laboratories and elsewhere. These parallel codes run fast enough on many simulations to consider using them to study the effects of varying design parameters on the performance of models of conventional munitions and other complex systems. Such studies maybe directed by optimization software to improve the performance of the modeled system. Using a shaped-charge jet design as an archetypal test case and the CTH parallel shock-wave physics code controlled by the Dakota optimization software, we explored the use of automatic optimization tools to optimize the design for conventional munitions. We used a scheme in which a lower resolution computational mesh was used to identify candidate optimal solutions and then these were verified using a higher resolution mesh. We identified three optimal solutions for the model and a region of the design domain where the jet tip speed is nearly optimal, indicating the possibility of a robust design. Based on this study we identified some of the difficulties in using high-fidelity models with optimization software to develop improved designs. These include developing robust algorithms for the objective function and constraints and mitigating the effects of numerical noise in them. We conclude that optimization software running high-fidelity models of physical systems using parallel shock wave physics codes to find improved designs can be a valuable tool for designers. While current state of algorithm and software development does not permit routine, ''black box'' optimization of designs, the effort involved in using the existing tools may well be worth the improvement achieved in designs