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Course Outline

Introduction to Programming Big Data with R (bpdR)

  • Configuring your environment for pbdR
  • Exploring the scope and tools available in pbdR
  • Packages commonly utilized alongside pbdR for Big Data

Message Passing Interface (MPI)

  • Leveraging pbdR MPI 5
  • Parallel processing techniques
  • Point-to-point communication
  • Sending matrices
  • Summing matrices
  • Collective communication strategies
  • Summing matrices using Reduce
  • Scatter and Gather operations
  • Additional MPI communications

Distributed Matrices

  • Constructing a distributed diagonal matrix
  • Performing SVD on a distributed matrix
  • Building a distributed matrix in parallel

Statistics Applications

  • Monte Carlo Integration
  • Reading datasets
  • Accessing data across all processes
  • Broadcasting from a single process
  • Processing partitioned data
  • Distributed Regression
  • Distributed Bootstrap methods
 21 Hours

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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