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Algorithmen spielen eine immer wichtigere Rolle in fast allen Bereichen der Mathematik. Dieses Lehrbuch eignet sich für den Studienbeginn und stellt den klassischen Vorlesungen über Analysis und Lineare Algebra eine dritte mathematische Grundvorlesung zur Seite, die die Autoren in den letzten Jahren mehrfach an der Universität Bonn gehalten haben. Ziel dieses Werkes ist die Vermittlung grundlegender mathematischer Fähigkeiten, besonders im Hinblick auf Entwurf und Analyse von Algorithmen, sowie deren Implementierung. Neben einigen fundamentalen Algorithmen (z.B. Sieb des Eratosthenes, Euklidischer Algorithmus, Sortieralgorithmen, Algorithmen auf Graphen, Gauß-Elimination) werden auch elementare Datenstrukturen, graphentheoretische Grundlagen und numerische Fragen behandelt. Zudem werden grundlegende Programmierkenntnisse vermittelt, und es wird gezeigt, wie man Algorithmen in C++ implementiert. Die Autoren: Prof. Dr. Stefan Hougardy, Universität Bonn, Forschungsinstitut für Diskrete Mathematik Prof. Dr. Jens Vygen, Universität Bonn, Forschungsinstitut für Diskrete Mathematik.
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Annotation International Symposium on Symbolic and Numeric Algorithms for Scientific Computing is an international conference that aims to stimulate the interaction between the two scientific communities of symbolic and numeric computing and to exhibit interesting applications of the areas both in theory and in practice.
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Novel scalable scientific algorithms are needed in order to enable key science applications to exploit the computational power of large-scale systems. This is especially true for the current tier of leading petascale machines and the road to exascale computing as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, and have no synchronization points. With the advent of Big Data in the past few years the need of such scalable mathematical methods and algorithms able to handle data and compute intensive applications at scale becomes even more important. Scientific algorithms for multi-petaflop and exa-flop systems also need to be fault tolerant and fault resilient, since the probability of faults increases with scale. Resilience at the system software and at the algorithmic level is needed as a crosscutting effort. Finally, with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs, scientific algorithms need to match these architectures to extract the most performance. This includes different system-specific levels of parallelism as well as co-scheduling of computation. Key science applications require novel mathematics and mathematical models and system software that address the scalability and resilience challenges of current- and future-generation extreme-scale HPC systems. The goal of this workshop is to bring together experts in the area of scalable algorithms to present the latest achievements and to discuss the challenges ahead.
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L'évolution des architectures des ordinateurs, mais aussi celle des besoins relatifs au volume de données ou à la complexification des algorithmes sont un véritable défi pour les utilisateurs et développeurs R. Il faut nécessairement s'adapter au parallélisme intrinsèque des ordinateurs, et cet ouvrage a pour objectif principal d'initier ses lecteurs aux multiples facettes du calculparallèle avec R. Le premier chapitre pose la problématique de l’efficacité des programmes et de leur optimisation en abordant en particulier les bonnes pratiques à adopter pour améliorer son code. L’architecture des ordinateurs est détaillée dans le deuxième chapitre qui permet de comprendre l’impact du matériel sur les performances des programmes et qui invite à penser « parallèle ». Les chapitres 3 et 4 sont le coeur de cet ouvrage et détaillent les outils disponibles pour programmer efficacement avec R sur une machine multi-coeurs, d’une part nativement dans le langage R et d’autre part en utilisant des fonctions C++ appelées depuis R. Enfin le dernier chapitre aborde l’exploitation de clusters de calcul au travers de R.
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Novel scalable scientific algorithms are needed in order to enable key science applications to exploit the computational power of large-scale systems. This is especially true for the current tier of leading petascale machines and the road to exascale computing as HPC systems continue to scale up in compute node and processor core count. These extreme-scale systems require novel scientific algorithms to hide network and memory latency, have very high computation/communication overlap, have minimal communication, and have no synchronization points. With the advent of Big Data in the past few years the need of such scalable mathematical methods and algorithms able to handle data and compute intensive applications at scale becomes even more important. Scientific algorithms for multi-petaflop and exa-flop systems also need to be fault tolerant and fault resilient, since the probability of faults increases with scale. Resilience at the system software and at the algorithmic level is needed as a crosscutting effort. Finally, with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs, scientific algorithms need to match these architectures to extract the most performance. This includes different system-specific levels of parallelism as well as co-scheduling of computation. Key science applications require novel mathematics and mathematical models and system software that address the scalability and resilience challenges of current- and future-generation extreme-scale HPC systems. The goal of this workshop is to bring together experts in the area of scalable algorithms to present the latest achievements and to discuss the challenges ahead.
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