Scientific Software Package Research Articles

NetSci: A Library for High Performance Biomolecular Simulation Network Analysis Computation.

We present the NetSci program-an open-source scientific software package designed for estimating mutual information (MI) between data sets using GPU acceleration and a k-nearest-neighbor algorithm. This approach significantly enhances calculation speed, achieving improvements of several orders of magnitude over traditional CPU-based methods, with data set size limits dictated only by available hardware. To validate NetSci, we accurately compute MI for an analytically verifiable two-dimensional Gaussian distribution and replicate the generalized correlation (GC) analysis previously conducted on the B1 domain of protein G. We also apply NetSci to molecular dynamics simulations of the Sarcoendoplasmic Reticulum Calcium-ATPase (SERCA) pump, exploring the allosteric mechanisms and pathways influenced by ATP and 2'-deoxy-ATP (dATP) binding. Our analysis reveals distinct allosteric effects induced by ATP compared to dATP, with predicted information pathways from the bound nucleotide to the calcium-binding domain differing based on the nucleotide involved. NetSci proves to be a valuable tool for estimating MI and GC in various data sets and is particularly effective for analyzing intraprotein communication and information transfer.

CMaize: Simplifying inter-package modularity from the build up.

There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. The need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.

PlanetProfile: Self‐Consistent Interior Structure Modeling for Ocean Worlds and Rocky Dwarf Planets in Python

AbstractThe open‐source PlanetProfile framework was developed to investigate the interior structure of icy moons based on collectively matching their observed properties and comparative planetology. The software relates observed and measured properties, assumptions such as the type of materials present, and laboratory equation‐of‐state (EOS) data through geophysical and thermodynamic models to evaluate radial profiles of mechanical, thermodynamic, and electrical properties, as self‐consistently as possible. We have created a Python version of PlanetProfile. In the process, we have made optimization improvements and added parallelization and parameter‐space search features to utilize fast operation for investigating unresolved questions in planetary geophysics, in which many model inputs are poorly constrained. The Python version links to other scientific software packages, including for evaluating EOS data, magnetic induction calculations, and seismic calculations. Physical models in PlanetProfile have been reconfigured to improve self‐consistency and generate the most realistic relationships between properties. In this work, we focus on the description of the software design and algorithms in detail, summarize model results for major moons across the outer solar system, and discuss new inferences about the interior structure of several bodies. The high values and narrow uncertainty ranges reported for the axial moments of inertia for Callisto, Titan, and Io are difficult to reconcile with the realistic physical and chemical approximations applied, requiring highly porous rock layers equivalent to incomplete differentiation for Callisto and Titan, and a high rock melt fraction for Io. Radial profiles for each model and comparison to prior work are provided as Zenodo archives.

PluginPlay: Enabling exascale scientific software one module at a time.

For many computational chemistry packages, being able to efficiently and effectively scale across an exascale cluster is a heroic feat. Collective experience from the Department of Energy's Exascale Computing Project suggests that achieving exascale performance requires far more planning, design, and optimization than scaling to petascale. In many cases, entire rewrites of software are necessary to address fundamental algorithmic bottlenecks. This in turn requires a tremendous amount of resources and development time, resources that cannot reasonably be afforded by every computational science project. It thus becomes imperative that computational science transition to a more sustainable paradigm. Key to such a paradigm is modular software. While the importance of modular software is widely recognized, what is perhaps not so widely appreciated is the effort still required to leverage modular software in a sustainable manner. The present manuscript introduces PluginPlay, https://github.com/NWChemEx-Project/PluginPlay, an inversion-of-control framework designed to facilitate developing, maintaining, and sustaining modular scientific software packages. This manuscript focuses on the design aspects of PluginPlay and how they specifically influence the performance of the resulting package. Although, PluginPlay serves as the framework for the NWChemEx package, PluginPlay is not tied to NWChemEx or even computational chemistry. We thus anticipate PluginPlay to prove to be a generally useful tool for a number of computational science packages looking to transition to the exascale.

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations.

Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge-Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.

On The Power Series Solution to The Nonlinear Pendulum

Summary The exact solution to the simple pendulum problem has long been known in terms of Jacobi elliptic functions, of which an efficient numerical evaluation is standard in most scientific computing software packages. Alternatively, and as done in V. Fairén, López and L. Conde (Power series approximation to solutions of nonlinear systems of differential equation, Am. J. Phys. 56 (1988) 57–61], the pendulum equation can be analytically solved exactly by the power series solution method. Although recursive formulae for the series coefficients were provided in V. Fairén et al., the series itself—as well as the optimal location about which an expansion should be chosen to maximize the domain of convergence—has not yet been examined, and this is provided here. By virtue of its representation as an elliptic function, the pendulum function has singularities that lie off of the real axis in the complex time plane. This, in turn, imposes a radius of convergence on the power series solution in real time. By choosing the expansion point at the top of the trajectory, the power series converges all the way to the bottom of the trajectory without being affected by these singularities. We provide an exact resummation of the pendulum series that accelerates the series’ convergence uniformly from the top to the bottom of the trajectory. We also provide the formulae needed to extend the pendulum series for all time via symmetry. The pendulum problem, in its relative simplicity, provides an explicit demonstration of the effect of singularity structure and initial condition location on convergence properties of power series solutions.

The PETSc Community as Infrastructure

The communities that develop and support open-source scientific software packages are crucial to the utility and success of such packages. Moreover, they form an important part of the human infrastructure that enables scientific progress. This article discusses aspects of the Portable Extensible Toolkit for Scientific Computation community, its organization, and technical approaches that enable community members to help each other efficiently and effectively.

Livermore tomography tools: Accurate, fast, and flexible software for tomographic science

This paper introduces the capabilities and availability of a customizable scientific software package called Livermore Tomography Tools (LTT) built for computed tomography (CT) research. It was initially developed to process x-ray and neutron CT data accurately and rapidly from raw detector counts to reconstructed volumes with the flexibility to handle many special cases. Our goals were to provide quantitatively accurate results reported in physical units (e.g., mm−1 or cm−1) while exploiting all available computational advantages to maximize speed and conserve memory. Written in C/C++ with support for multiple CPUs and GPUs, LTT runs on many computing platforms (Linux/Unix, Windows, and Mac; laptops to supercomputers). As a result, LTT can: process data acquired from various custom-built and commercially available CT scanners, model and simulate x-ray and neutron interactions to encourage algorithm prototyping, and allow for rapid insertion of the latest algorithms. We describe LTT's software architecture, user interfaces, and its 88 algorithms (as of this writing) for pre-processing, reconstruction, post-processing, and simulation that support many scanner geometries (parallel-, fan-, cone-beam, and custom). Several applications are presented that illustrate LTT's accuracy, speed, and flexibility relative to other solutions.

Fiber identification of braided composites using micro-computed tomography

Braided composites contain interwoven fibers that are embedded in a matrix material. Advanced measurement methods are required to accurately measure and characterize braided composites due to their interwoven composition. Micro-computed tomography (μCT) is an X-ray based measurement method that allows for the internal structure of objects to be examined. High-resolution μCT of braided composites allows for their internal geometry to be accurately measured. Braid samples were measured with a voxel size of 1.0 μm3, which resulted in a field of view of 4.904 x 4.904 x 3.064 mm3. With this field of view, individual fibers within the braid yarns could be identified and measured. The scientific visualization software package Avizo and the XFiber extension was used to identify and measure braid yarn fibers from the collected μCT measurements. Fiber properties such as orientation angles (φ and θ), curved fiber length, tortuosity, and fiber diameter were obtained. Additionally, finite element mesh geometries of the braid yarns within a braided structure were created. The presented methodology provides a roadmap for the accurate modeling of braided composite unit cell geometries using high-resolution μCT data.

A Systematic Mapping Study of Software Usability Studies

Among software quality attributes “software usability” is considered as one of the vital factors in software engineering literature. Software usability is the ability for users to generally understand, use, and learn a software with ease. Due to the importance of usability in software quality, a considerable amount of literature is published in the past decade. Few review and survey studies are also published to critically review the existing literature in the domain. However, there is limited research covering systematic mapping study of software usability. Mapping studies help in analyzing the general trends and research productivity in a research area. To fill this gap, this work critically examines the overall research productivity, demographics, trends, and challenges of software usability. The objective is to classify the current contributions and trends in the area of software usability. We retrieved 9,874 research articles from six research databases and 62 works are selected as primary studies using an evidence-based approach. The result of this mapping study shows that software usability is an active research area, with a promising number of works published in the last decade (2011 - 2020). We identified that the current literature spans over multiple article classes of which investigative papers, model proposals and evaluation papers are the most frequently published article types. We found experiments and theoretical validations to be the most common validation techniques. In terms of application domains; web, software development and mobile applications are the most frequent domains where usability studies are conducted. We identified that future usability studies should focus more on field studies as well as on the usability testing of scientific software packages. It will be of importance to consider ethical issues in usability testing as well.

Corvus: a framework for interfacing scientific software for spectroscopic and materials science applications.

Corvus, a Python-based package designed for managing workflows of physical simulations that utilize multiple scientific software packages, is presented. Corvus can be run as an executable script with an input file and automatically generated or custom workflows, or interactively, in order to build custom workflows with a set of Corvus-specific tools. Several prototypical examples are presented that link density functional, vibrational and X-ray spectroscopy software packages and are of interest to the synchrotron community. These examples highlight the simplification of complex spectroscopy calculations that were previously limited to expert users, and demonstrate the flexibility of the Corvus infrastructure to tackle more general problems in other research areas.

A note on the use of workstation software programs for quantification

Most chromatographic and mass spectrometric instruments include workstations that allow data processing and target compound quantification. However, the software used for quantification does not all use common approaches for statistical treatment of data. Presented here is a brief description of three commonly used workstation software packages (WSPs) and the degree to which they obey widely accepted approaches to statistical treatment of data. The Thermo Fisher Scientific software packages Xcalibur and TraceFinder provided calibration lines and accompanying parameters that were similar or identical to those obtained by generic treatment using the Microsoft Excel “linest()” function, whereas the Agilent OpenLab ChemStation software did not. It is recommended to always perform calculations manually via spreadsheets for at least a few representative samples to allow better statistical treatment of data and to confirm whether the WSP used employs the commonly accepted approach to linear calibration.

Reproducibility, accuracy and performance of the Feltor code and library on parallel computer architectures

Feltor is a modular and free scientific software package. It allows developing platform independent code that runs on a variety of parallel computer architectures ranging from laptop CPUs to multi-GPU distributed memory systems. Feltor consists of both a numerical library and a collection of application codes built on top of the library. Its main targets are two- and three-dimensional drift- and gyro-fluid simulations with discontinuous Galerkin methods as the main numerical discretization technique.We observe that numerical simulations of a recently developed gyro-fluid model produce non-deterministic results in parallel computations. First, we show how we restore accuracy and bitwise reproducibility algorithmically and programmatically. In particular, we adopt an implementation of the exactly rounded dot product based on long accumulators, which avoids accuracy losses especially in parallel applications. However, reproducibility and accuracy alone fail to indicate correct simulation behavior. In fact, in the physical model slightly different initial conditions lead to vastly different end states. This behavior translates to its numerical representation. Pointwise convergence, even in principle, becomes impossible for long simulation times. We briefly discuss alternative methods to ensure the correctness of results like the convergence of reduced physical quantities of interest, ensemble simulations, invariants or reduced simulation times.In a second part, we explore important performance tuning considerations. We identify latency and memory bandwidth as the main performance indicators of our routines. Based on these, we propose a parallel performance model that predicts the execution time of algorithms implemented in Feltor and test our model on a selection of parallel hardware architectures. We are able to predict the execution time with a relative error of less than 25% for problem sizes between 10−1 and 103 MB. Finally, we find that the product of latency and bandwidth gives a minimum array size per compute node to achieve a scaling efficiency above 50% (both strong and weak).

Demystifying Fixed <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Nearest Neighbor Information Estimators

Estimating mutual information from independent identically distributed samples drawn from an unknown joint density function is a basic statistical problem of broad interest with multitudinous applications. The most popular estimator is the one proposed by Kraskov, Stogbauer, and Grassberger (KSG) in 2004 and is nonparametric and based on the distances of each sample to its $k^{\mathrm{ th}}$ nearest neighboring sample, where $k$ is a fixed small integer. Despite of its widespread use (part of scientific software packages), theoretical properties of this estimator have been largely unexplored. In this paper, we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the $\ell _{2}$ error as a function of a number of samples. We argue that the performance benefits of the KSG estimator stems from a curious “correlation boosting” effect and build on this intuition to modify the KSG estimator in novel ways to construct a superior estimator. As a by-product of our investigations, we obtain nearly tight rates of convergence of the $\ell _{2}$ error of the well-known fixed $k$ -nearest neighbor estimator of differential entropy by Kozachenko and Leonenko.

Differences in GPS coordinate time series resulting from the use of individual instead of type-mean antenna phase center calibration model

It is well-known that the phase center of a Global Navigation Satellite System (GNSS) antenna is not a stable point coinciding with a mechanical reference. The phase center position depends on the direction of the received signal, and is antenna-and signaldependent. Phase center corrections (PCC) models of GNSS antennas have been available for several years. The first method to create antenna PCC models was the relative field calibration procedure. Currently only absolute calibration models are generally recommended for use. In this study we investigate the differences between position estimates obtained using individual and type-mean absolute antenna calibrations in order to better understand how receiver antenna calibration models contribute to the Global Positioning System (GPS) positioning error budget. The station positions were estimated with two absolute calibration models: the igs08.atx model, which contains typemean calibration results, and individual antenna calibration models. Continuous GPS observations from selected Polish European Permanent Network (EPN) stations were used for these studies. The position time series were derived from the precise point positioning (PPP) technique using the NAPEOS scientific GNSS software package. The results show that the differences in the calibrations models propagate directly into the position domain, affecting daily as well sub-daily results. In daily solutions, the position offsets, resulting from the use of individual calibrations instead of type-mean igs08.atx calibrations, can reach up to 5 mm in the Up component, while in the horizontal one they generally stay below 1 mm. It was found that increasing the frequency of sub-daily coordinate solutions amplifies the effects of type-mean vs individual PCC-dependent differences, and also gives visible periodic variations in time series of GPS position differences.

Evaluating a campaign GNSS velocity field derived from an online precise point positioning service

Traditional processing of Global Navigation Satellite System (GNSS) data using dedicated scientific software has provided the highest levels of positional accuracy, and has been used extensively in geophysical deformation studies. To achieve these accuracies a significant level of understanding and training is required, limiting their availability to the general scientific community. Various online GNSS processing services, now freely available, address some of these difficulties and allow users to easily process their own GNSS data and potentially obtain high quality results. Previous research into these services has focused on Continually Operating Reference Station (CORS) GNSS data. Less research exists on the results achievable with these services using large campaign GNSS data sets, which are inherently noisier than CORS data. Even less research exists on the quality of velocity fields derived from campaign GNSS data processed through online precise point positioning services. Particularly, whether they are suitable for geodynamic and deformation studies where precise and reliable velocities are needed. In this research, we process a very large campaign GPS data set (spanning 10 yr) with the online Jet Propulsion Laboratory Automated Precise Positioning Service. This data set is taken from a GNSS network specifically designed and surveyed to measure deformation through the central North Island of New Zealand. This includes regional CORS stations. We then use these coordinates to derive a horizontal and vertical velocity field. This is the first time that a large campaign GPS data set has been processed solely using an online service and the solutions used to determine a horizontal and vertical velocity field. We compared this velocity field to that of another well utilized GNSS scientific software package. The results show a good agreement between the CORS positions and campaign station velocities obtained from the two approaches. We discuss the implications of these results for how future GNSS campaign field surveys might be conducted and how their data might be processed

GUM2DFT—a software tool for uncertainty evaluation of transient signals in the frequency domain

The Fourier transform and its counterpart for discrete time signals, the discrete Fourier transform (DFT), are common tools in measurement science and application. Although almost every scientific software package offers ready-to-use implementations of the DFT, the propagation of uncertainties in line with the guide to the expression of uncertainty in measurement (GUM) is typically neglected. This is of particular importance in dynamic metrology, when input estimation is carried out by deconvolution in the frequency domain. To this end, we present the new open-source software tool GUM2DFT, which utilizes closed formulas for the efficient propagation of uncertainties for the application of the DFT, inverse DFT and input estimation in the frequency domain. It handles different frequency domain representations, accounts for autocorrelation and takes advantage of the symmetry inherent in the DFT result for real-valued time domain signals. All tools are presented in terms of examples which form part of the software package. GUM2DFT will foster GUM-compliant evaluation of uncertainty in a DFT-based analysis and enable metrologists to include uncertainty evaluations in their routine work.

Macroeco: reproducible ecological pattern analysis in Python

macroeco is a Python package that supports the analysis of empirical macroecological patterns and the comparison of these patterns to theoretical predictions. Here we describe the use of macroeco and the various functions that it contains. We also highlight a unique high‐level interface included with the package, MacroecoDesktop, that allows non‐programmers to access the functionality of macroeco. MacroecoDesktop takes simple text‐based metadata and parameter files as inputs and generates both tabular and graphical outputs, supporting users in creating reproducible workflows that follow the principles of simplicity, provenance, and automation. Both macroeco and MacroecoDesktop provide case studies for developers of analytically‐focused scientific software packages who wish to better support the reproducible use of their tools.

Special Section on Two Themes: CSE Software and Big Data in CSE

The 2015 SIAM Conference on Computational Science and Engineering (CSE) was held March 14-18, 2015, in Salt Lake City, Utah. The SIAM Journal on Scientific Computing (SISC) created this special section in association with the CSE15 conference. The special section focuses on two topics that are of significant current interest to CSE researchers: CSE software and big data in CSE. The development of efficient, robust, and sustainable software is at the core of CSE. Software itself is now broadly recognized as a key crosscutting technology that connects advances in mathematics, computer science, and domain-specific science and engineering. Software encapsulates progress in algorithms, methods, and implementations across CSE and thereby provides a foundation for long-term CSE collaboration and scientific progress. A second topic of great current interest in CSE and beyond is how the data revolution enables new ways of generating quantitative insight for applications in science, engineering, technology, and society. The world is experiencing an explosion of digital data, and there are important challenges in designing scalable algorithms and methods to turn the data deluge into a source of scientific and engineering progress. The special section contains 34 papers. There are 22 papers on topics related to CSE software and 10 papers on algorithms with applications in big data. The final two papers combine aspects of software and big data; this is not a surprising combination, since open source software for data analysis is of timely interest. Among the 22 software-related papers, there are 11 papers on scientific software packages that use differential equation models for science and engineering for such diverse applications as soft materials, explosive devices, and insect flight. Aspects of multiphysics and multiresolution modeling, scalability, and mesh management are highlighted. Two of these papers discuss efficiency and performance of adaptive grid refinement packages, and a further paper presents a model order reduction package. There are four papers on notable aspects of simulation software, including algorithmic resilience, automatic simulation workflow change, data and task parallelism, and systems to guide solver choices. There are also seven papers on linear algebra software, including preconditioners, eigensolvers, and hierarchical and multilevel system solvers. Several of the software papers focus on aspects of high-level software organization, while others highlight algorithms, efficient parallel implementations, and performance tests. As such, the scope of this special section is somewhat broader, e.g., in terms of scientific software functionality, than the papers that usually appear in SISC’s Software and High-Performance Computing section. Indeed, CSE software is a valuable research product and a cornerstone of CSE collaboration, and as part of the growing realization of the importance of software for the CSE endeavor, the field continues to see evolution as to where and how software contributions of various nature are best published. This special section is part of that exploration and evolution. The special section contains 10 papers on algorithms with applications in big data. There are two papers on matrix and tensor completion, with applications in, e.g., large-scale recommendation and stochastic PDE simulation. Two papers discuss randomized algorithms for large linear algebra problems. One paper considers an adaptive metropolis approach for scalable parallel Bayesian inference, and another paper considers a multilevel method for large-scale sparse inverse covariance estimation. There are two papers on parallel algorithms for matching and partitioning on large graphs targeting problems with up to billions of vertices and edges. There is a paper on parallel density estimation for 100 million particles on 500 billion grid points and a paper on parallel nearest neighbor search in high dimensions with scalability tests on more than 100,000 computing cores for 4 billion points in 1,000 dimensions. Finally, the first paper that combines software and big data aspects presents an extension of the Apache Spark distributed data analytics environment to enable high-performance visual computing applications on GPU-based cluster systems. The second software-big data paper discusses a parallel open-source library for high-dimensional kernel summations with applications in machine learning and computational statistics, scaling to billions of points in hundreds of dimensions. The guest editorial board worked very hard to ensure a rigorous peer-review process while meeting deadlines. Special thanks are due to Mitch Chernoff (SIAM Publications Manager) and Brittni Holland (SIAM Editorial Associate) for their efforts on this special section.

Comparison of Various GPS Processing Solutions toward an Efficient Validation of the Hellenic Vertical Network: The ELEVATION Project

AbstractA research project called ELEVATION (Evaluation of the HelLEnic Vertical Network in the FrAme of the European SysTems and Control Networks InterconnectiON—Application in the Areas of Attica and Thessaloniki) for the validation of the Hellenic vertical network is currently in progress. Two investigation areas in central and northern Greece were chosen for the project. The areas include several benchmarks of the national trigonometric and leveling networks. In the current research, static global positioning system (GPS) observations as well as classical terrestrial leveling were performed to assess the internal accuracy of the two networks. This paper presents some of the numerical tests based on GPS and leveling measurements and outlines the goals of the project. The strategies followed in the processing of GPS data are presented, with emphasis on their future use within this project. GPS observations were processed using various commercial as well as scientific software packages to examine the inf...