Academic literature on the topic 'Programming science'

We present the format for a workshop on introductory computer programming, which was held at the 2015 American Society for Horticultural Science (ASHS) Annual Conference in New Orleans, LA. The main workshop objective was to familiarize attendees with basic computer programming, including data structures, data management, and data analysis. The workshop used the general programming language R, though the concepts and principles presented are transferable across programming languages. Given the increased importance of statistical analysis in the agricultural sciences, the workshop was well attended. Participants appreciated the opportunity to improve their computational literacy and supported follow-up workshops like this at future ASHS events. We have released the presentation and the companion R script online.

Data Science workflows have become a must to progress in many scientific areas such as life, health, and earth sciences. In contrast to traditional HPC workflows, they are more heterogeneous; combining binary executions, MPI simulations, multi-threaded applications, custom analysis (possibly written in Java, Python, C/C++ or R), and real-time processing. Furthermore, in the past, field experts were capable of programming and running small simulations. However, nowadays, simulations requiring hundreds or thousands of cores are widely used and, to this point, efficiently programming them becomes a challenge even for computer sciences. Thus, programming languages and models make a considerable effort to ease the programmability while maintaining acceptable performance. This thesis contributes to the adaptation of High-Performance frameworks to support the needs and challenges of Data Science workflows by extending COMPSs, a mature, general-purpose, task-based, distributed programming model. First, we enhance our prototype to orchestrate different frameworks inside a single programming model so that non-expert users can build complex workflows where some steps require highly optimised state of the art frameworks. This extension includes the @binary, @OmpSs, @MPI, @COMPSs, and @MultiNode annotations for both Java and Python workflows. Second, we integrate container technologies to enable developers to easily port, distribute, and scale their applications to distributed computing platforms. This combination provides a straightforward methodology to parallelise applications from sequential codes along with efficient image management and application deployment that ease the packaging and distribution of applications. We distinguish between static, HPC, and dynamic container management and provide representative use cases for each scenario using Docker, Singularity, and Mesos. Third, we design, implement and integrate AutoParallel, a Python module to automatically find an appropriate task-based parallelisation of affine loop nests and execute them in parallel in a distributed computing infrastructure. It is based on sequential programming and requires one single annotation (the @parallel Python decorator) so that anyone with intermediate-level programming skills can scale up an application to hundreds of cores. Finally, we propose a way to extend task-based management systems to support continuous input and output data to enable the combination of task-based workflows and dataflows (Hybrid Workflows) using one single programming model. Hence, developers can build complex Data Science workflows with different approaches depending on the requirements without the effort of combining several frameworks at the same time. Also, to illustrate the capabilities of Hybrid Workflows, we have built a Distributed Stream Library that can be easily integrated with existing task-based frameworks to provide support for dataflows. The library provides a homogeneous, generic, and simple representation of object and file streams in both Java and Python; enabling complex workflows to handle any data type without dealing directly with the streaming back-end.<br>Els fluxos de treball de Data Science s’han convertit en una necessitat per progressar en moltes àrees científiques com les ciències de la vida, la salut i la terra. A diferència dels fluxos de treball tradicionals per a la CAP, els fluxos de Data Science són més heterogenis; combinant l’execució de binaris, simulacions MPI, aplicacions multiprocés, anàlisi personalitzats (possiblement escrits en Java, Python, C / C ++ o R) i computacions en temps real. Mentre que en el passat els experts de cada camp eren capaços de programar i executar petites simulacions, avui dia, aquestes simulacions representen un repte fins i tot per als experts ja que requereixen centenars o milers de nuclis. Per aquesta raó, els llenguatges i models de programació actuals s’esforcen considerablement en incrementar la programabilitat mantenint un rendiment acceptable. Aquesta tesi contribueix a l’adaptació de models de programació per a la CAP per afrontar les necessitats i reptes dels fluxos de Data Science estenent COMPSs, un model de programació distribuïda madur, de propòsit general, i basat en tasques. En primer lloc, millorem el nostre prototip per orquestrar diferent programari per a que els usuaris no experts puguin crear fluxos complexos usant un únic model on alguns passos requereixin tecnologies altament optimitzades. Aquesta extensió inclou les anotacions de @binary, @OmpSs, @MPI, @COMPSs, i @MultiNode per a fluxos en Java i Python. En segon lloc, integrem tecnologies de contenidors per permetre als desenvolupadors portar, distribuir i escalar fàcilment les seves aplicacions en plataformes distribuïdes. A més d’una metodologia senzilla per a paral·lelitzar aplicacions a partir de codis seqüencials, aquesta combinació proporciona una gestió d’imatges i una implementació d’aplicacions eficients que faciliten l’empaquetat i la distribució d’aplicacions. Distingim entre la gestió de contenidors estàtica, CAP i dinàmica i proporcionem casos d’ús representatius per a cada escenari amb Docker, Singularity i Mesos. En tercer lloc, dissenyem, implementem i integrem AutoParallel, un mòdul de Python per determinar automàticament la paral·lelització basada en tasques de nius de bucles afins i executar-los en paral·lel en una infraestructura distribuïda. AutoParallel està basat en programació seqüencial, requereix una sola anotació (el decorador @parallel) i permet a un usuari intermig escalar una aplicació a centenars de nuclis. Finalment, proposem una forma d’estendre els sistemes basats en tasques per admetre dades d’entrada i sortida continus; permetent així la combinació de fluxos de treball i dades (Fluxos Híbrids) en un únic model. Conseqüentment, els desenvolupadors poden crear fluxos complexos seguint diferents patrons sense l’esforç de combinar diversos models al mateix temps. A més, per a il·lustrar les capacitats dels Fluxos Híbrids, hem creat una biblioteca (DistroStreamLib) que s’integra fàcilment amb els models basats en tasques per suportar fluxos de dades. La biblioteca proporciona una representació homogènia, genèrica i simple de seqüències contínues d’objectes i arxius en Java i Python; permetent gestionar qualsevol tipus de dades sense tractar directament amb el back-end de streaming.<br>Los flujos de trabajo de Data Science se han convertido en una necesidad para progresar en muchas áreas científicas como las ciencias de la vida, la salud y la tierra. A diferencia de los flujos de trabajo tradicionales para la CAP, los flujos de Data Science son más heterogéneos; combinando la ejecución de binarios, simulaciones MPI, aplicaciones multiproceso, análisis personalizados (posiblemente escritos en Java, Python, C/C++ o R) y computaciones en tiempo real. Mientras que en el pasado los expertos de cada campo eran capaces de programar y ejecutar pequeñas simulaciones, hoy en día, estas simulaciones representan un desafío incluso para los expertos ya que requieren cientos o miles de núcleos. Por esta razón, los lenguajes y modelos de programación actuales se esfuerzan considerablemente en incrementar la programabilidad manteniendo un rendimiento aceptable. Esta tesis contribuye a la adaptación de modelos de programación para la CAP para afrontar las necesidades y desafíos de los flujos de Data Science extendiendo COMPSs, un modelo de programación distribuida maduro, de propósito general, y basado en tareas. En primer lugar, mejoramos nuestro prototipo para orquestar diferentes software para que los usuarios no expertos puedan crear flujos complejos usando un único modelo donde algunos pasos requieran tecnologías altamente optimizadas. Esta extensión incluye las anotaciones de @binary, @OmpSs, @MPI, @COMPSs, y @MultiNode para flujos en Java y Python. En segundo lugar, integramos tecnologías de contenedores para permitir a los desarrolladores portar, distribuir y escalar fácilmente sus aplicaciones en plataformas distribuidas. Además de una metodología sencilla para paralelizar aplicaciones a partir de códigos secuenciales, esta combinación proporciona una gestión de imágenes y una implementación de aplicaciones eficientes que facilitan el empaquetado y la distribución de aplicaciones. Distinguimos entre gestión de contenedores estática, CAP y dinámica y proporcionamos casos de uso representativos para cada escenario con Docker, Singularity y Mesos. En tercer lugar, diseñamos, implementamos e integramos AutoParallel, un módulo de Python para determinar automáticamente la paralelización basada en tareas de nidos de bucles afines y ejecutarlos en paralelo en una infraestructura distribuida. AutoParallel está basado en programación secuencial, requiere una sola anotación (el decorador @parallel) y permite a un usuario intermedio escalar una aplicación a cientos de núcleos. Finalmente, proponemos una forma de extender los sistemas basados en tareas para admitir datos de entrada y salida continuos; permitiendo así la combinación de flujos de trabajo y datos (Flujos Híbridos) en un único modelo. Consecuentemente, los desarrolladores pueden crear flujos complejos siguiendo diferentes patrones sin el esfuerzo de combinar varios modelos al mismo tiempo. Además, para ilustrar las capacidades de los Flujos Híbridos, hemos creado una biblioteca (DistroStreamLib) que se integra fácilmente a los modelos basados en tareas para soportar flujos de datos. La biblioteca proporciona una representación homogénea, genérica y simple de secuencias continuas de objetos y archivos en Java y Python; permitiendo manejar cualquier tipo de datos sin tratar directamente con el back-end de streaming.

As the number of applications of computers controlling safety-critical operations increases, the need to ensure the safety and reliability of the software that controls those computers increases proportionally. Ultimately, such software properties are the result of appropriate design and implementation. However, certain characteristics of the language in which the software is written can have an impact on how that language facilitates both design and implementation, and how it encourages safety and reliability in the resulting software.<br>This paper explores the language characteristics that can impact the safety and reliability of the software produced. The goal is to provide a set of criteria that can be used for the selection of an appropriate language for real-time, safety-critical development. It proposes a set of characteristics that can affect the suitability of a language to such development, and compares a selection of common programming languages, including Ada, C, C++ and Java, against this framework.

Two studies of factors affecting the programming performance of first- and second year Computer Science students were conducted. In one study students used GIL, a simple application framework, for their programming assignments in a second-semester programming course. Improvements in student performance were realized. In the other study, students submitted detailed logs of how time was spent on projects, along with their programs. Software metrics were computed on the students' source code. Correlations between student performance and the log data and software metric data were sought. No significant indicators of performance were found, even with factors that are commonly expected to indicate performance. However, results from previous research concerning variations in individual programmer performance and relationships between software metrics were obtained.<br>Master of Science

Once women were the pioneers within the tech industry, but during the last decades the amount of women who choose a career within computer science has decreased rapidly. Programming languages have evolved during the last decades and because of the growing gender gap in the industry, they have done so involuntarily in the absence of women. The imbalance raises the question if the tech industry and the programming languages have been adapted for a more masculine way of developing software. A quantitative study and a literature review evaluates if there is a need for a computerlanguage developed towards women. The study comes to the conclusion that there is no need for a female inspired computer language, but the way computer languages are taught suits the male way of thinking better than the female way.

There has been relatively little work done in the compiler research community for incorporating aspect-oriented features in scientific and dynamic programming languages.<br>MATLAB is a dynamic scientific programming language that is commonly used by scientists because of its convenient and high-level syntax for arrays, the fact that type declarations are not required, and the availability of a rich set of application libraries. This thesis introduces a new aspect-oriented scientific language, AspectMatlab.<br>AspectMatlab introduces key aspect-oriented features in a way that is both accessible to scientists and where the aspect-oriented features concentrate on array accesses and loops, the core computation elements in scientific programs. One of the main contributions of this thesis is to provide a compiler implementation of the AspectMatlab language. It is supported by a collection of scientific use cases, which portray the potential of the aspectorientation for scientific problems.<br>Introducing aspects into a dynamic language such as MATLAB also provides some new challenges. In particular, it is difficult to statically determine precisely where patterns match, resulting in many dynamic checks in the woven code. The AspectMatlab compiler uses flow analyses to eliminate many of those dynamic checks.<br>This thesis reports on the language design of AspectMatlab, the amc compiler implementation, and also provides an overview of the use cases that are specific to scientific programming. By providing clear extensions to an already popular language, AspectMatlab will make aspect-oriented programming accessible to a new group of programmers including scientists and engineers.<br>Relativement peu de travail a été accomplis dans le milieu de la recherche du compilateur pour l'intégration des caractéristiques orientées à l'aspect dans les domaines scientifique et dynamique des langages de programmation. Matlab est un langage de programmation scientifique dynamique qui est couramment utilisé par les scientifiques en raison de sa pratique et la syntaxe de qualité pour des tableaux ; du fait que les déclarations de type ne sont pas nécessaires, et de la disponibilité de vastes bibliothèques d'applications. Cette thèse introduit un nouvel aspect de langue de recherche scientifique : AspectMatlab.<br>AspectMatlab introduit fonctionnalités d'aspect orientées d'une manière qui est à la fois accessible aux scientifiques et où les fonctionnalités d'aspect orientées se concentrent sur les accès réseau et des boucles, les éléments de calcul de base dans les programmes scientifiques. L'une des principales contributions de cette thèse est de fournir une implémentation du compilateur du langage AspectMatlab. Il est soutenu d'une collection de cas d'utilisation scientifique, qui montre le potentiel de l'orientation aspect pour des problèmes scientifiques.<br>L'introduction des aspects dans un langage dynamique comme Matlab représente aussi quelques nouveaux défis. En particulier, il est difficile de déterminer statiquement où les modèles coïncident, résultant dans de nombreux contrôles dynamiques dans le code tissé. Le compilateur d'AspectMatlab utilise le flux d'analyses pour éliminer un grand nombre de ces contrôles dynamiques.<br>Cette thèse signale la conception du language d'AspectMatlab et l'implementation du compilateur amc. Elle fournit également un aperçu de l'utilisation des cas qui sont spécifiques à la programmation scientifique. En fournissant des extensions claires avec un langage déjà populaire, AspectMatlab rendra la programmation orientée à l'aspect accessible à un nouveau groupe de programmeurs y compris des scientifiques et des ingénieurs.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 141-153).<br>This thesis attempts to unite and consolidate two large and often culturally disjoint programming paradigms: declarative (focusing on specifying what a program is supposed to do, e.g., shuffle an array so that its elements are ordered) and imperative (detailing how the program intention is to be implemented, e.g., by applying the QuickSort algorithm). The ultimate result of such an effort would be a unified programming environment in which both paradigms are seamlessly integrated, specifications are fully and efficiently executable, and programs are written by freely mixing imperative statements and declarative specifications. With the advent of automated constraint solving, executing declarative specifications as standalone programs has become feasible. A number of challenges still remain. To achieve full automation, constraint solvers often impose restrictions on specification languages and their expressiveness; compromises are also made when integrating a (typically logic-based) specification language with a traditional procedural programming language; and finally, applicability is usually limited to specialized algorithmic domains (for which constraint solving is particularly suitable) and programmers comfortable with writing formal logic. This thesis proposes several advances to address these issues. First, a novel constraint solving framework is presented, Alloy*, the first of its kind capable of automatically and reliably solving arbitrary higher-order formulas (written in standard predicate logic) over bounded domains. Second, a new approach to integrating a specification and an implementation language is proposed, where Alloy, a relational logic-based modeling and specification language, is deeply embedded in Ruby. The resulting platform, called [alpha]Rby, uses Alloy* as its back end, and serves both as an Alloy modeling environment with added Ruby scripting layer around it, and as a Ruby programming environment with added executable specifications. Third, the general idea of declarative programming (focusing on what instead of how) is applied to web programming, producing SUNNY, a model-based reactive web framework with a clear separation between data, events (business logic), and security policies. SUNNY is (1) policy-agnostic-allows security policies to be specified individually and independently from the rest of the code, (2) reactive-automatically propagates data updates to all connected clients while enforcing the security policies, (3) mostly declarative-offers a unified sequential view of the entire distributed web system, allowing events to be implemented only in terms of simple modifications to the data model.<br>by Aleksandar Milicevic.<br>Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 129-136).<br>Dynamic Programming (DP) is a fundamental problem-solving technique that has been widely used for solving a broad range of search and optimization problems. While DP can be invoked when more specialized methods fail, this generality often incurs a cost in efficiency. We explore a unifying toolkit for speeding up DP, and algorithms that use DP as subroutines. Our methods and results can be summarized as follows. - Acceleration via Compression. Compression is traditionally used to efficiently store data. We use compression in order to identify repeats in the table that imply a redundant computation. Utilizing these repeats requires a new DP, and often different DPs for different compression schemes. We present the first provable speedup of the celebrated Viterbi algorithm (1967) that is used for the decoding and training of Hidden Markov Models (HMMs). Our speedup relies on the compression of the HMM's observable sequence. - Totally Monotone Matrices. It is well known that a wide variety of DPs can be reduced to the problem of finding row minima in totally monotone matrices. We introduce this scheme in the context of planar graph problems. In particular, we show that planar graph problems such as shortest paths, feasible flow, bipartite perfect matching, and replacement paths can be accelerated by DPs that exploit a total-monotonicity property of the shortest paths. - Combining Compression and Total Monotonicity. We introduce a method for accelerating string edit distance computation by combining compression and totally monotone matrices.<br>(cont.) In the heart of this method are algorithms for computing the edit distance between two straight-line programs. These enable us to exploits the compressibility of strings, even if each string is compressed using a different compression scheme. - Partial Tables. In typical DP settings, a table is filled in its entirety, where each cell corresponds to some subproblem. In some cases, by changing the DP, it is possible to compute asymptotically less cells of the table. We show that [theta](n³) subproblems are both necessary and sufficient for computing the similarity between two trees. This improves all known solutions and brings the idea of partial tables to its full extent. - Fractional Subproblems. In some DPs, the solution to a subproblem is a data structure rather than a single value. The entire data structure of a subproblem is then processed and used to construct the data structure of larger subproblems. We suggest a method for reusing parts of a subproblem's data structure. In some cases, such fractional parts remain unchanged when constructing the data structure of larger subproblems. In these cases, it is possible to copy this part of the data structure to the larger subproblem using only a constant number of pointer changes. We show how this idea can be used for finding the optimal tree searching strategy in linear time. This is a generalization of the well known binary search technique from arrays to trees.<br>by Oren Weimann.<br>Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 105-108).<br>Modern applications provide interfaces for scripting, but many users do not know how to write script commands. However, many users are familiar with the idea of entering keywords into a web search engine. Hence, if a user is familiar with the vocabulary of an application domain, they may be able to write a set of keywords expressing a command in that domain. For instance, in the web browsing domain, a user might enter the keywords click search button. This thesis presents several algorithms for translating keyword queries such as this directly into code. A prototype of this system in the web browsing domain translates click search button into the code click(findButton("search")). This code may then be executed in the context of a web browser to carry out the effect. Another prototype in the Java domain translates append message to log into log.append(message), given an appropriate context of local variables and imported classes. The algorithms and prototypes are evaluated with several studies, suggesting that users can write keyword queries with little or no instructions, and that the resulting translations are often accurate. This is especially true in small domains like the web, whereas in a large domain like Java, the accuracy is comparable to the accuracy of writing syntactically correct Java code without assistance.<br>by Greg Little.<br>S.M.

The problems encountered by students in first year computer programming units are a common concern in many universities including Victoria University. A fundamental component of a computer science curriculum, computer programming is a mandatory unit in a computing course. It is also one of the most feared and hated units by many novice computing students who, having failed or performed poorly in a programming unit, often drop out from a course. This article discusses some of the difficulties experienced by first year programming students, and reviews some of the initiatives undertaken to counter the problems. The article also reports on the first stage of a current research project at Victoria University that aims to develop a balanced approach to teaching first year programming units; its goal is to ‘befriend’ computer programming to help promote success among new programming students.

HIV prevention efforts across sub-Saharan Africa are increasingly focused on engaging men, for their own health and that of their partners and families. We examined experiences with HIV services and prevention programing among men in Uganda whose partners were enrolled in DREAMS, a large-scale initiative to reduce new HIV infections among adolescent girls and young women (AGYW). The study is part of the Population Council’s implementation science research portfolio on the DREAMS Partnership.