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teaching:autumn_term_2020:distributed_parallel_computing [2020-09-24 14:38] Julian Kunkel [Distributed and Parallel Programming (CS3DP19)] |
teaching:autumn_term_2020:distributed_parallel_computing [2020-09-24 14:42] (current) Julian Kunkel |
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| The official [[https:// | The official [[https:// | ||
| - | More information is provided in RISIS and your timetable. | + | Additional |
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| + | The module is taught online due to COVID-19, I make the [[https:// | ||
| ===== Summary module description ===== | ===== Summary module description ===== | ||
| - | This module introduces concepts, principles, tools, techniques and algorithms for distributed systems and parallel computing, and examines the deployment of relevant applications in Cloud, big data analytics, and massive-parallel environment. In this context, this module covers the topic ranging from hardware and software architectures and algorithms in the development of distributed systems, MapReduce program paradigm and Hadoop ecosystems, and in-memory and stream computing tools such as Spark, Storm, and Flink; to parallel programming paradigms for relevant hardware and software applications, | + | This module introduces concepts, principles, tools, techniques, and algorithms for distributed systems and parallel computing, and examines the deployment of relevant applications in the Cloud, big data analytics, and massive-parallel environment. In this context, this module covers the topic ranging from hardware and software architectures and algorithms in the development of distributed systems, MapReduce program paradigm and Hadoop ecosystems, and in-memory and stream computing tools such as Spark, Storm, and Flink; to parallel programming paradigms for relevant hardware and software applications, |
| ===== Aims ===== | ===== Aims ===== | ||
| - | The module provides students with fundamentals of distributed systems and parallel computing and state-of-the-art tools that enable students to understand the concepts and principles underpinning distributed systems and utilize industry standard tools. Students are then prepared to specialize further in the field of distributed systems and parallel computing, e.g., in big data analytics or as scientific programmer. | + | The module provides students with fundamentals of distributed systems and parallel computing and state-of-the-art tools that enable students to understand the concepts and principles underpinning distributed systems and utilize industry-standard tools. Students are then prepared to specialize further in the field of distributed systems and parallel computing, e.g., in big data analytics or as a scientific programmer. |
| - | This module also encourages students to develop a set of professional skills, such as creativity, software design and development, | + | This module also encourages students to develop a set of professional skills, such as creativity, software design and development, |
| + | ===== Content ===== | ||
| + | * Overview to distributed and parallel computing; hardware and software architectures | ||
| + | * Use-cases for distributed and parallel applications from industry and science | ||
| + | * Issues in designing of distribution and parallel systems and algorithms | ||
| + | * Industry relevant processing models for big data and tools like Hadoop and Spark | ||
| + | * Relevant algorithms and data structures | ||
| + | * Introduction to scientific computing | ||
| + | * Parallel programming paradigms and concepts with a focus on OpenMP and MPI | ||