A time-predictable parallel programing model for real-time systems

The recent technological advancements and market trends are causing an interesting phenomenon towards the convergence of the high-performance and the embedded computing domains. Critical real-time embedded systems are increasingly concerned with providing higher performance to implement advanced functionalities in a predictable way. OpenMP, the de-facto parallel programming model for shared memory architectures in the high-performance computing domain, is gaining the attention to be used in embedded platforms. The reason is that OpenMP is a mature language that allows to efficiently exploit the huge computational capabilities of parallel embedded architectures. Moreover, OpenMP allows to express parallelism on top of the current technologies used in embedded designs (e.g., C/C++ applications). At a lower level, OpenMP provides a powerful task-centric model that allows to define very sophisticated types of regular and irregular parallelism. While OpenMP provides relevant features for embedded systems, both the programming interface and the execution model are completely agnostic to the timing requirements of real-time systems. This thesis evaluates the use of OpenMP to develop future critical real-time embedded systems. The first contribution analyzes the OpenMP specification from a timing perspective. It proposes new features to be incorporated in the OpenMP standard and a set of guidelines to implement critical real-time systems with OpenMP. The second contribution develops new methods to analyze and predict the timing behavior of parallel applications, so that the notion of parallelism can be safely incorporated into critical real-time systems. Finally, the proposed techniques are evaluated with both synthetic applications and real use cases parallelized with OpenMP. With the above contributions, this thesis pushes the limits of the use of task-based parallel programming models in general, and OpenMP in particular, in critical real-time embedded domains.Los recientes avances tecnológicos y tendencias de mercado estan causando un interesante fenómeno hacia la convergencia de dos dominios: la computacion de altas prestaciones y la computacion embebida. Hay cada vez mas interés en que los sistemas embebidos criticos de tiempo real proporcionen un mayor rendimiento para implementar funcionalidades avanzadas de una manera predecible. OpenMP, el modelo de programación paralela estándar para arquitecturas de memoria compartida en el dominio de la computación de altas prestaciones, está ganando atención para ser utilizado en systemas embebidos. La razón es que OpenMP es un lenguaje asentado que permite explotar eficientemente las enormes capacidades computacionales de las arquitecturas paralelas embebidas. Además, OpenMP permite expresar paralelismo sobre las tecnologías actuales utilizadas en los diseños embebidos (por ejemplo, aplicaciones C/C++). A un nivel inferior, OpenMP proporciona un potente modelo centrado en tareas que permite expresar tipos muy sofisticados de paralelismo regular e irregular. Si bien OpenMP proporciona funciones relevantes para los sistemas embebidos, tanto la interfaz de programación como el modelo de ejecución son completamente ajenos a los requisitos temporales de los sistemas de tiempo real. Esta tesis evalúa el uso de OpenMP para desarrollar los futuros sistemas embebidos criticos de timepo real. La primera contribución analiza la especificación de OpenMP desde una perspectiva temporal. Se propone nuevas funcionalidades que podrian ser incorporadas en el estándar de OpenMP y un conjunto de pautas para implementar sistemas críticos de tiempo real con OpenMP. La segunda contribución desarrolla nuevos métodos para analizar y predecir el comportamiento temporal de las aplicaciones paralelas, de modo que la noción de paralelismo se pueda incorporar de manera segura en sistemas críticos de tiempo real. Finalmente, las técnicas propuestas se evaluan con aplicaciones sintéticas y casos de uso reales paralelizados con OpenMP. Con las mencionadas contribuciones, esta tesis amplía los límites en el uso de los modelos de programación paralela basados en tarea en general, y de OpenMP en particular, en dominios embebidos criticos de tiempo real

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications