The work deals with design and implementation of 2D ultrasound simulation. Applications of the ultrasound simulation can be found in medicine, biophysic or image reconstruction. As an example of using the ultrasound simulation we can mention High Intensity Focused Ultrasound that is used for diagnosing and treating cancer. The program is part of the k-Wave toolbox designed for supercomputer systems, specifically for machines with shared memory architecture. The program is implemented in the C++ language and using OpenMP acceleration.  Using the designed solution, it is possible to solve large-scale simulations in 2D space. The work also deals with merging and unification of the 2D and 3D simulation using modern C++. A realistic example of use is ultrasound simulation in transcranial neuromodulation and neurostimulation in large domains, which have more than 16384x16384 grid points. Simulation of such size may take several days if we use the original MATLAB 2D k-Wave. Speedup of the new implementation is up to 8 on the Anselm and Salomon supercomputers.

Práce se zaobírá problematikou implementace součtového datového typu v jazyce C++. Nejprve navrhuje vlastní abstrakce pro manipulaci s posloupnostmi hodnot a typů, které následně umožňují vyjadřovat komplexní operace deklarativně. V průběhu implementace jsou prezentovány návrhové vzory obvyklé pro oblast metaprogramování. Implementace přichází s rozhodnutími odlišnými od referenční implementace. Pro manipulaci se součtovým typem jsou představeny dvě šablony funkce, lišící se sémantikou. Užití součtového typu je ukázano na příkladu konečného stavového automatu.

Monomorphization and dynamic dispatch are two common techniques for implementing polymorphism in statically typed programming languages. Function templates in C++ use the former technique to enable algorithms written as generic functions to be efficiently reused with multiple different data types by producing a separate function instantiation for each invocation that uses a unique permutation of argument types. This avoids the overhead of indirection associated with dynamic dispatch and allows the generated code of each instantiation to be optimized by the compiler for its specific concrete types, which typically yields great improvements in runtime performance over any dynamic approach. The disadvantage of this implementation, compared to the type-erased generics found in many other programming languages, is that careless over-use of templates with many different argument types can lead to an excessive amount of redundant code being generated for the same function. This increase in code size may increase the binary size of the final program and reduce the amount of useful code that can fit into the processor's instruction cache during execution, reducing code locality and thereby potentially reducing performance. Monomorphization can also increase compilation time due to the increase in generated code that needs to be compiled and optimized. This thesis presents a heuristic-based approach to generic programming that allows function templates to be automatically converted to use dynamic dispatch in scenarios where the resulting negative impact on runtime performance is predicted to be low. The thesis project includes the development of a proof of concept plugin for the Clang compiler frontend that can be used to compile existing C++ projects with the conversions applied. The design of a heuristic function for determining whether a given function template should use monomorphization or dynamic dispatch based on statically known metrics is proposed based on the results of an experiment. This heuristic is shown to achieve a small general improvement in program size across a set of open-source C++ projects when they are compiled using the plugin. The key findings from the experiment and from the development of the plugin are summarized with a general strategy for how the approach can be integrated into the design of future programming languages to promote more extensive use of generic programming in performance-sensitive code while avoiding regressions in program size and compilation time.

en català:Un camp de recerca important dins del paradigma del Computer-Supported Collaborative Learning (CSCL) és la importància en la gestió eficaç de la informació d'esdeveniments generada durant l'activitat de l'aprenentatge col·laboratiu virtual, per a proporcionar coneixement sobre el comportament dels membres del grup. Aquesta visió és especialment pertinent en l'escenari educatiu actual que passa d'un paradigma tradicional - centrat en la figura d'un instructor magistral - a un paradigma emergent que considera els estudiants com actors centrals en el seu procés d'aprenentatge. En aquest nou escenari, els estudiants aprenen, amb l'ajuda de professors, la tecnologia i els altres estudiants, el que potencialment necessitaran per a desenvolupar les seves activitats acadèmiques o professionals futures.Els principals aspectes a tenir en compte en aquest context són, primer de tot, com dissenyar una plataforma sota el paradigma del CSCL, que es pugui utilitzar en situacions reals d'aprenentatge col·laboratiu complexe i a llarg termini, basades en el model d'aprenentatge de resolució de problemes. I que permet al professor una anàlisi del grup més eficaç així com donar el suport adequat als estudiants quan sigui necessari. En segon lloc, com extreure coneixement pertinent de la col·laboració per donar consciència i retorn als estudiants a nivell individual i de rendiment del grup, així com per a propòsits d'avaluació. L'assoliment d'aquests objectius impliquen el disseny d'un model conceptual d'interacció durant l'aprenentatge col·laboratiu que estructuri i classifiqui la informació generada en una aplicació col·laborativa en diferents nivells de descripció. A partir d'aquesta aproximació conceptual, els models computacionals hi donen resposta per a proporcionar una extracció eficaç del coneixement produït per l'individu i per l'activitat del grup, així com la possibilitat d'explotar aquest coneixement com una eina metacognitiva pel suport en temps real i regulat del procés d'aprenentatge col·laboratiu.A més a més, les necessitats dels entorns CSCL han evolucionat en gran mesura durant els darrers anys d'acord amb uns requisits pedagògics i tecnològics cada cop més exigents. Els entorns d'aprenentatge col·laboratius virtuals ara ja no depenen de grups d'estudiants homogenis, continguts i recursos d'aprenentatge estàtics, ni pedagogies úniques, sinó que exigeixen una forta personalització i un alt grau de flexibilitat. En aquest nou escenari, les organitzacions educatives actuals necessiten estendre's i moure's cap a paradigmes d'ensenyament altament personalitzats, amb immediatesa i constantment, on cada paradigma incorpora el seu propi model pedagògic, el seu propi objectiu d'aprenentatge i incorpora els seus propis recursos educatius específics. Les demandes de les organitzacions actuals també inclouen la integració efectiva, en termes de cost i temps, de sistemes d'aprenentatge llegats i externs, que pertanyen a altres institucions, departaments i cursos. Aquests sistemes llegats es troben implementats en llenguatges diferents, suportats per plataformes heterogènies i distribuïdes arreu, per anomenar alguns dels problemes més habituals. Tots aquests problemes representen certament un gran repte per la comunitat de recerca actual i futura. Per tant, els propers esforços han d'anar encarats a ajudar a desenvolupadors, recercaires, tecnòlegs i pedagogs a superar aquests exigents requeriments que es troben actualment en el domini del CSCL, així com proporcionar a les organitzacions educatives solucions ràpides i flexibles per a potenciar i millorar el rendiment i resultats de l'aprenentatge col·laboratiu. Aquesta tesi proposa un primer pas per aconseguir aquests objectius. / An important research topic in Computer Supported Collaborative Learning (CSCL) is to explore the importance of efficient management of event information generated from group activity in collaborative learning practices for its further use in extracting and providing knowledge on interaction behavior. The essential issue here is first how to design a CSCL platform that can be used for real, long-term, complex collaborative problem solving situations and which enables the instructor to both analyze group interaction effectively and provide an adequate support when needed. Secondly, how to extract relevant knowledge from collaboration in order to provide learners with efficient awareness and feedback as regards individual and group performance and assessment. The achievement of these tasks involve the design of a conceptual framework of collaborative learning interaction that structures and classifies the information generated in a collaborative application at several levels of description. Computational models are then to realize this conceptual approach for an efficient management of the knowledge produced by the individual and group activity as well as the possibility of exploiting this knowledge further as a metacognitive tool for real-time coaching and regulating the collaborative learning process.In addition, CSCL needs have been evolving over the last years accordingly with more and more demanding pedagogical and technological requirements. On-line collaborative learning environments no longer depend on homogeneous groups, static content and resources, and single pedagogies, but high customization and flexibility are a must in this context. As a result, current educational organizations' needs involve extending and moving to highly customized learning and teaching forms in timely fashion, each incorporating its own pedagogical approach, each targeting a specific learning goal, and each incorporating its specific resources. These entire issues certainly represent a great challenge for current and future research in this field. Therefore, further efforts need to be made that help developers, technologists and pedagogists overcome the demanding requirements currently found in the CSCL domain as well as provide modern educational organizations with fast, flexible and effective solutions for the enhancement and improvement of the collaborative learning performance and outcomes. This thesis proposes a first step toward these goals.Índex foliat:The main contribution in this thesis is the exploration of the importance of an efficient management of information generated from group activity in Computer-Supported Collaborative Learning (CSCL) practices for its further use in extracting and providing knowledge on interaction behavior. To this end, the first step is to investigate a conceptual model for data analysis and management so as to identify the many kinds of indicators that describe collaboration and learning and classify them into high-level potential categories of effective collaboration. Indeed, there are more evident key discourse elements and aspects than those shown by the literature, which play an important role both for promoting student participation and enhancing group and individual performance, such as, the impact and effectiveness of students' contributions, among others, that are explored in this work. By making these elements explicit, the discussion model proposed accomplishes high students' participation rates and contribution quality in a more natural and effective way. This approach goes beyond a mere interaction analysis of asynchronous discussion in the sense that it builds a multi-functional model that fosters knowledge sharing and construction, develops a strong sense of community among students, provides tutors with a powerful tool for students' monitoring, discussion regulation, while it allows for peer facilitation through self, peer and group awareness and assessment.The results of the research described so far motivates the development of a computational system as the translation from the conceptual model into a computer system that implements the management of the information and knowledge acquired from the group activity, so as to be efficiently fed back to the collaboration. The achievement of a generic, robust, flexible, interoperable, reusable computational model that meets the fundamental functional needs shared by any collaborative learning experience is largely investigated in this thesis. The systematic reuse of this computational model permits a fast adaptation to new learning and teaching requirements, such as learning by discussion, by relying on the most advanced software engineering processes and methodologies from the field of software reuse, and thus important benefits are expected in terms of productivity, quality, and cost.Therefore, another important contribution is to explore and extend suitable software reuse techniques, such as Generic Programming, so as to allow the computational model to be successfully particularized in as many as situations as possible without losing efficiency in the process. In particular, based on domain analysis techniques, a high-level computational description and formalization of the CSCL domain are identified and modeled. Then, different specific-platform developments that realize the conceptual description are provided. It is also explored a certain level of automation by means of advanced techniques based on Service-Oriented Architectures and Web-services while passing from the conceptual specification to the desired realization, which greatly facilitates the development of CSCL applications using this computational model.Based on the outcomes of these investigations, this thesis contributes with computational collaborative learning systems, which are capable of managing both qualitative and quantitative information and transforming it into useful knowledge for all the implicated parties in an efficient and clear way. This is achieved by both the specific assessment of each contribution by the tutor who supervises the discussion and by rich statistical information about student's participation. This statistical data is automatically provided by the system; for instance, statistical data sheds light on the students' engagement in the discussion forum or how much interest drew the student's intervention in the form of participation impact, level of passivity, proactivity, reactivity, and so on. The aim is to provide both a deeper understanding of the actual discussion process and a more objective assessment of individual and group activity.This information is then processed and analyzed by means of a multivariate statistical model in order to extract useful knowledge about the collaboration. The knowledge acquired is communicated back to the members of the learning group and their tutor in appropriate formats, thus providing valuable awareness and feedback of group interaction and performance as well as may help identify and assess the real skills and intentions of participants. The most important benefit expected from the conceptual model for interaction data analysis and management is a great improvement and enhancement of the learning and teaching collaborative experiences.Finally, the possibilities of using distributed and Grid technology to support real CSCL environments are also extensively explored in this thesis. The results of this investigation lead to conclude that the features provided by these technologies form an ideal context for supporting and meeting demanding requirements of collaborative learning applications. This approach is taken one step further for enhancing the possibilities of the computational model in the CSCL domain and it is successfully adopted on an empirical and application basis. From the results achieved, it is proved the feasibility of distributed technologies to considerably enhance and improve the collaborative learning experience. In particular, the use of Grid computing is successfully applied for the specific purpose of increasing the efficiency of processing a large amount of information from group activity log files.

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