These examples show data analyses with explicit multithreading and multiprocessing.
When using RDataFrame, implicit multithreading can be enabled by simply calling ROOT::EnableImplicitMT().
| Tutorial | Description | ||
|---|---|---|---|
| Multiprocessing | Multithreading | ||
| mp_parallelHistoFill.C | mt_parallelHistoFill.C | mtbb_parallelHistoFill.C | Fill histograms in parallel |
| mt_fillHistos.C | mtbb_fillHistos.C | Fill histograms in parallel and write them on file | |
| mp_processSelector.C | Usage of TTreeProcessorMP and TSelector with h1analysis.C | ||
Files | |
| file | mp_parallelHistoFill.C |
  Fill histogram in parallel with a multiprocessing approach using TProcessExecutor and TExecutor::MapReduce. | |
| file | mp_processSelector.C |
| Illustrate the usage of the multiprocessing TTreeProcessorMP and TSelector interfaces with the h1analysis.C example. | |
| file | mt_fillHistos.C |
| Fill histograms in parallel and write them on file with a multithreaded approach using std::thread. | |
| file | mt_parallelHistoFill.C |
| Fill histogram in parallel with a multithreaded approach using TThreadedObject and TThreadedObject::SnapshotMerge. | |
| file | mtbb_fillHistos.C |
| Fill histograms in parallel and write them on file with a multithreaded approach using TThreadExecutor and TExecutor::Map. | |
| file | mtbb_parallelHistoFill.C |
| Fill histogram in parallel with a multithreaded approach using TThreadExecutor and TExecutor::MapReduce. | |
