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.