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Lehrveranstaltung: High-Performance Data Analytics

Data-driven science requires the handling of large volumes of data in a quick period of time. Executing efficient workflows is challenging for users but also for systems. This module introduces concepts, principles, tools, system architectures, techniques, and algorithms toward large-scale data analytics using distributed and parallel computing. We will investigate the state-of-the-art of processing data of workloads using solutions in High-Performance Computing and Big Data Analytics.

Contact Julian Kunkel
Location Virtual, meeting room
Time TBD
SWS 4
Credits 6
Contact time 56 hours
Independent study 124 hours

Topics cover:

  • Challenges in high-performance data analytics
  • Use-cases for large-scale data analytics
  • Performance models for parallel systems and workload execution
  • Data models to organize data and (No)SQL solutions for data management
  • Industry relevant processing models with tools like Hadoop, Spark, and Paraview
  • System architectures for processing large data volumes
  • Relevant algorithms and data structures
  • Visual Analytics
  • Parallel and distributed file systems

Guest talks from academia and industry will be incorporated in teaching that demonstrates the applicability of this topic.

Weekly laboratory practicals and tutorials will guide students to learn the concepts and tools. In the process of learning, students will form a learning community and integrate peer learning into the practicals. Students will have opportunities to present their solutions to the challenging tasks in the class. Students will develop presentation skills and gain confidence in the topics.

  • Assign big data challenges to a given use-case
  • Outline use-cases for high-performance data analytics
  • Estimate performance and runtime for a given workload and system
  • Create a suitable hardware configuration to execute a given workload within a deadline
  • Construct suitable data models for a given use-case and discuss their pro/cons
  • Discuss the rationales behind the design decisions behind our learned tools
  • Describe the concept of visual analytics and its potential in scientific workflows
  • Compare the features and architectures of NoSQL solutions to the abstract concept of a parallel file system
  • Appraise the requirements for designing system architectures for systems storing and processing data
  • Apply distributed algorithms and data structures to a given problem instance and illustrate their processing steps
  • Explain the importance of hardware characteristics when executing a given workload
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  • teaching/autumn_term_2021/hpda.1631201929.txt.gz
  • Last modified: 2021-09-09 17:38
  • by Julian Kunkel