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Cluster computing : praktische Einführung in das by Heiko Bauke

By Heiko Bauke

Das Buch ist eine praktische Einführung in das Hochleistungsrechnen auf Linux-Clustern. In vier Teilen (Grundlagen, Technik, Programmierung, Praxis) wird ausführlich erklärt, wie guy einen Haufen (Cluster) preiswerter Standard-PCs in einen Parallelcomputer verwandelt und diesen dann zur Lösung rechenintensiver Probleme einsetzt. Insbesondere enthält das Buch eine fundierte Einführung in MPI, dem grundlegenden Programmiermodell für Cluster-Computer. Dabei werden anhand konkreter Beispiele die wichtigsten Paradigmen paralleler Programmierung präsentiert. Vorgestellt werden außerdem Entwicklungswerkzeuge, die Fehlersuche in parallelen Programmen und nützliche Bibliotheken.

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Extra info for Cluster computing : praktische Einführung in das Hochleistungsrechnen auf Linux-Clustern

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Ein derart abgespeckter Prozess heißt Leichtgewichtsprozess oder Thread (Faden). Ein Thread ist allein nicht le” bensf¨ahig“, denn er ben¨otigt immer den Code, den globalen Speicher, den Heap und die Tabellen eines ausgewachsenen Prozesses. Ein Prozess kann Threads erzeugen und damit nebenl¨aufige Anweisungsstr¨ange schaffen wie in Abb. 1. Die Erzeugung von Threads ist dabei ein schneller Vorgang, da nur neue Registerinhalte und Platz f¨ur einen neuen Stack angelegt werden m¨ussen. Es ist daher nicht un¨ublich, dass ein Prozess hunderte von Threads erzeugt, wie z.

Die Flynn’sche Klassifikation ist sehr akademisch, fu¨ r praktische Aspekte ist die Unterscheidung zwischen shared und distributed memory viel wichtiger. Sowohl SMPs als auch Cluster sind im Flynn’schen Sinne MIMD-Maschinen. 2 PRAM. F¨ur den Entwurf und die Analyse von Algorithmen beno¨ tigt man ein abstraktes Rechnermodell. Bei parallelen Algorithmen verwendet man meistens PRAM, die parallel random access machine. In diesem Modell haben alle Prozessoren uneingeschr¨ankten, gleichm¨aßig effizienten Zugriff auf den gesamten Speicher.

Multitasking-Betriebssysteme wie Unix verteilen per definitionem mehrere Prozesse auf einer CPU. Dank der homogenen Struktur der SMP-Architektur bedarf es keines großen Aufwands, diese Verteilung auf mehrere Prozessoren auszudehnen. Folglich unterst¨utzt Linux ebenso wie die meisten kommerziellen Unix-Varianten das symmetrische Multiprocessing. SMP-Maschinen bieten so einen insgesamt h¨oheren Durchsatz, auch wenn die einzelnen Anwendungen ganz gew o¨ hnliche sequentielle Programme sind. Der ungebremste Zugriff auf den gemeinsamen Speicher (und andere gemeinsam genutzte Komponenten) erfordert eine aufw¨andige Elektronik, und der Aufwand steigt schnell mit der Anzahl der Prozessoren.

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