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Thumbnail image of graphical abstract Heat-treated alloy FePt nanoparticles transform into L10 FePt and mixed L10/L12 FePt3 intermetallic phases. Enrichment in Pt in some nanoparticles, rather than intrinsic thermodynamic effects, drives phase segregation. FePt nanoparticles of uniform, equimolar composition are expected to transform into phase-pure, highly ordered L10 FePt nanoparticles.
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For some covering arrays, there are some wild card positions in the array where there is flexibility about what factor levels can be chosen with no impact on the basic properties of covering array, because all of the required pairs have been covered. The choice of how to fill these wild card positions can influence other properties, such as the degrees of orthogonality or three-way coverage of the array. Here in this article, some criteria are proposed for identifying the best choices for the wild card positions to create covering arrays with highly desirable properties. Accompanying graphical summaries are also described to highlight differing performance for several examples.
When there are constraints on resources, an unreplicated factorial or fractional factorial design can allow efficient exploration of numerous factor and interaction effects. A half-normal plot is a common graphical tool used to compare the relative magnitude of effects and to identify important effects from these experiments when no estimate of error from the experiment is available. An alternative is to use a least absolute shrinkage and selection operation plot to examine the pattern of model selection terms from an experiment. We examine how both the half-normal and least absolute shrinkage and selection operation plots are impacted by the absence of individual observations or an outlier, and the robustness of conclusions obtained from these 2 techniques for identifying important effects from factorial experiments. As a result, the methods are illustrated with 2 examples from the literature.
Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This work describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting.
Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi-antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in length to T-cell epitopes. This optimal coverage problem can be formulated in terms of a directed graph, with candidate antigens represented as paths that traverse this graph. Epigraph protein sequences can also be used as the basis for designing peptides for experimental evaluation of immune responses in natural infections to highly variable proteins. The epigraph tool suite also enables rapid characterization of populations of diverse sequences from an immunological perspective. Fundamental distance measures are based on immunologically relevant shared potential epitope frequencies, rather than simple Hamming or phylogenetic distances. Here, we provide a mathematical description of the epigraph algorithm, include a comparison of different heuristics that can be used when graphs are not acyclic, and we describe an additional tool we have added to the web-based epigraph tool suite that provides frequency summaries of all distinct potential epitopes in a population. Lastly, we also show examples of the graphical output and summary tables that can be generated using the epigraph tool suite and explain their content and applications.
Organo-lead halide perovskite photovoltaics have developed faster than our understanding of the material itself. Using the vast body of work on perovskite processing created in just the past few years, it is possible to create a better picture of this material's complex phase-transformation behavior. This concept article summarizes and correlates the current understanding of structural intermediates, kinetic controls, and structure–property relationships of organo-lead iodide perovskites. To this end, a new way of graphically relating information is developed, allowing the simultaneous mapping of schematic kinetic relationships between all currently prevailing perovskite deposition and growth techniques.
Turbomole is a highly optimized software package for large‐scale quantum chemical simulations of molecules, clusters, and periodic solids. Turbomole uses G aussian basis sets and specializes on predictive electronic structure methods with excellent cost to performance characteristics, such as (time‐dependent) density functional theory ( TDDFT ), second‐order M øller– P lesset theory, and explicitly correlated coupled cluster ( CC ) methods. These methods are combined with ultraefficient and numerically stable algorithms such as integral‐direct and Laplace transform methods, resolution‐of‐the‐identity, pair natural orbitals, fast multipole, and low‐order scaling techniques. Apart from energies and structures, a variety of optical, electric, and magnetic properties are accessible from analytical energy derivatives for electronic ground and excited states. Recent additions include post‐ K ohn– S ham calculations within the random phase approximation, periodic calculations, spin–orbit couplings, explicitly correlated CC singles doubles and perturbative triples methods, CC singles doubles excitation energies, and nonadiabatic molecular dynamics simulations using TDDFT . A dedicated graphical user interface and a user support network are also available. This article is categorized under: Software > Quantum Chemistry
Abstract An open‐source software library for wavefunction analysis, libwfa, provides a comprehensive and flexible toolbox for post‐processing excited‐state calculations, featuring a hierarchy of interconnected visual and quantitative analysis methods. These tools afford compact graphical representations of various excited‐state processes, provide detailed insight into electronic structure, and are suitable for automated processing of large data sets. The analysis is based on reduced quantities, such as state and transition density matrices (DMs), and allows one to distill simple molecular orbital pictures of physical phenomena from intricate correlated wavefunctions. The implemented descriptors provide a rigorous link between many‐body wavefunctions and intuitive physical and chemical models, for example, exciton binding, double excitations, orbital relaxation, and polyradical character. A broad range of quantum‐chemical methods is interfaced with libwfa via a uniform interface layer in the form of DMs. This contribution reviews the structure of libwfa and highlights its capabilities by several representative use cases. This article is categorized under: Software > Quantum Chemistry Theoretical and Physical Chemistry > Spectroscopy
This paper introduces a new Metric Space to guide the design of advanced wind energy systems and hydrokinetic energy converters such as tidal, ocean current and riverine turbines. The Metric Space can analyse farms that combine different or identical turbines and stand-alone turbines. The first metric (M1) of the space considers the efficiency of the turbines in the farm, which is also proportional to the specific power per swept area at a given wind/water velocity (W/m 2 ). The second metric (M2) describes the specific rotor area per unit of mass of the turbines (m 2 /kg). Both metrics depend on the primary design characteristics of the turbines, such as swept area, system size and mass, materials and efficiency, and are independent at first from external characteristics, such as atmospheric and ocean site conditions, cost of materials and economic factors. Combining both metrics, and for a given set of external characteristics, the resulting Metric Space M2/M1 displays the Levelized Cost of Energy (LCOE) standards as isolines. This graphical representation provides a quick understanding of the cost and state of the technology. It also offers a practical guidance to choose the research tasks and strategy to design advanced wind and hydrokinetic energy systems. The paper applies the new Metric Space to several case studies, including large and small onshore wind turbines, floating and bottom-fixed offshore wind turbines, downwind rotors, multi-rotor and hybrid systems, airborne wind energy systems, wind farms and tidal energy converters.
ABSTRACT Fast and accurate large‐eddy simulation (LES) of the atmospheric boundary layer plays a crucial role in advancing wind energy research. Long‐duration wind farm studies at turbine‐resolving scales have become increasingly important to understand the intricate interactions between large wind farms and the atmospheric boundary layer. However, the prohibitive computational cost of these turbulence‐ and turbine‐resolving simulations has precluded such modeling to be exercised on a regular basis. To that end, we implement and validate the generalized actuator disk (GAD) model in the computationally efficient, graphics processing unit (GPU)–resident, LES model FastEddy. We perform single‐turbine simulations under three atmospheric stabilities (neutral, unstable, and stable) and compare them against observations from the Scaled Wind Farm Technology (SWiFT) facility and other LES codes from the recent Wakebench turbine wake model benchmark. Our idealized LES results agree well with observed wake velocity deficit and downstream recovery across stability regimes. Turbine response in terms of rotational speed, generated power, torque, and thrust coefficient are well predicted across stability regimes and are consistent with the LES results from the benchmark. The FastEddy simulations are found to be at least two orders of magnitude more efficient than the traditional CPU‐based LES models, opening the door for realistic LES simulations of full wind plants as a viable standard practice.
We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.
Recent reliability issues with one of the fastest supercomputers in the world, Titan at Oak Ridge National Laboratory (ORNL), demonstrated the need for resilience in large-scale heterogeneous computing. OpenMP currently does not address error and failure behavior. This paper takes a first step toward resilience for heterogeneous systems by providing the concepts for resilient OpenMP offload to devices. Using real-world error and failure observations, the paper describes the concepts and terminology for resilient OpenMP target offload, including error and failure classes and resilience strategies. It details the experienced general-purpose computing graphics processing unit (GPGPU) errors and failures in Titan. It further proposes improvements in OpenMP, including a preliminary prototype design, to support resilient offload to devices for efficient handling of errors and failures in heterogeneous high-performance computing (HPC) systems.
SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this paper, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, use of local memory, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.
This document describes the effort to implement the Graph 500 benchmark using OpenSHMEM based on the MPI-2 one-side version. The Graph 500 benchmark performs a breadth-first search in parallel on a large randomly generated undirected graph and can be implemented using basic MPI-1 and MPI-2 one-sided communication. Graph 500 requires atomic bit-wise operations on unsigned long integers but neither atomic bit-wise operations nor OpenSHMEM for unsigned long are available in OpenSHEM. Such needed bit-wise atomic operations and support for unsigned long are implemented using atomic condition swap (CSWAP) on signed long integers. Preliminary results on comparing the OpenSHMEM and MPI-2 one-sided implementations on a Silicon Graphics Incorporated (SGI) cluster and the Cray XK7 are presented.
In this paper we propose and analyze a set of batched linear solvers for small matrices on Graphic Processing Units (GPUs), evaluating the various alternatives depending on the size of the systems to solve. We discuss three different solutions that operate with different level of parallelization and GPU features. The first, exploiting the CUBLAS library, manages matrices of size up to 32x32 and employs Warp level (one matrix, one Warp) parallelism and shared memory. The second works at Thread-block level parallelism (one matrix, one Thread-block), still exploiting shared memory but managing matrices up to 76x76. The third is Thread level parallel (one matrix, one thread) and can reach sizes up to 128x128, but it does not exploit shared memory and only relies on the high memory bandwidth of the GPU. The first and second solution only support partial pivoting, the third one easily supports partial and full pivoting, making it attractive to problems that require greater numerical stability. We analyze the trade-offs in terms of performance and power consumption as function of the size of the linear systems that are simultaneously solved. We execute the three implementations on a Tesla M2090 (Fermi) and on a Tesla K20 (Kepler).
We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.
Evidence for Higgs boson decay to a pair of muons is presented. This result combines searches in four exclusive categories targeting the production of the Higgs boson via gluon fusion, via vector boson fusion, in association with a vector boson, and in association with a top quark-antiquark pair. The analysis is performed using proton-proton collision data at $ \sqrt{s} $ = 13 TeV, corresponding to an integrated luminosity of 137 fb$^{−1}$, recorded by the CMS experiment at the CERN LHC. An excess of events over the back- ground expectation is observed in data with a significance of 3.0 standard deviations, where the expectation for the standard model (SM) Higgs boson with mass of 125.38 GeV is 2.5. The combination of this result with that from data recorded at $ \sqrt{s} $ = 7 and 8 TeV, corresponding to integrated luminosities of 5.1 and 19.7 fb$^{−1}$, respectively, increases both the expected and observed significances by 1%. The measured signal strength, relative to the SM prediction, is $ {1.19}_{-0.39}^{+0.40}{\left(\mathrm{stat}\right)}_{-0.14}^{+0.15}\left(\mathrm{syst}\right) $. This result constitutes the first evidence for the decay of the Higgs boson to second generation fermions and is the most precise measurement of the Higgs boson coupling to muons reported to date.[graphic not available: see fulltext]