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Session Overview
Session
Short Course 4: Go fastR: High Performance Computing with R
Time:
Sunday, 03/Sept/2023:
9:00am - 10:30am

Location: Lecture Room U1.101


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Presentations

Go fastR: High Performance Computing with R

Michael Mayer1, Lukas Widmer2

1Posit PBC, Boston, USA; 2Novartis Pharma AG, Basel, Switzerland

This course will help participants to optimize their R code as well as parallelizing and debugging it on their own machines as well as high-performance computing environments. Example use cases include commonly performed activities for trial design, bootstrapping, cross-validation and related workloads. The following topics will be covered:

Part I: Identifying bottlenecks in your R code, debugging, and optimizing

  • Debugging R code & checking correctness
  • Profiling R code to identify bottlenecks
  • Optimizing bottlenecks locally: packages, vectorizing, logical indexing

Part II: R parallelization on high performance computing environments (HPCE)

  • Amdahl’s law and limits of achievable speed up
  • Parallelizing work onto compute clusters via clusterMQ and batchtools
  • Consistently loading packages, .libPaths() and options() on R workers
  • Uncorrelated random number generation for parallel R code
  • Debugging R code in batchtools and clusterMQ jobs

Part III: Case studies and code examples

  • Bootstrapping
  • Cross-validation
  • Trial simulations under replication
  • Within-chain parallelization with several chains in Stan
  • Bring your own problem: start to speed-up your own code with the help of the instructors.


 
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