Rivet tutorials

Merging MPI-parallelised Rivet runs

When analysing events produced on multiple cores there are two common appraches one could take to merge the Rivet output into a single output file at the end of the run:

File-based merging

The “brute force” approach is to initialise an AnalysisHandler per rank and have each rank write out a YODA file. At the end of the runs the various YODA files can be merged using the rivet-merge script (see additional file-based merging documentation here).

Merging AnalysisHandlers in memory

Disk space is expensive, however, and it might be more attractive to merge the output from each individual rank in memory first, such that only a single file needs writing out at the very end.

This can be achieved by using YODA’s (de-)serialisation methods, which allows representing the numerical content of the AnalysisHandler as a long list of floating-point values. The streams of values can then be collapsed using an MPI-reduce operation and deserialised into the AnalysisHandler of the root/master rank. Of course the deserialisation must then happen with an AnalysisHandler that has been

In Python, this could look something like this:

from mpi4py import MPI
import rivet, io

def processRank(rank):
  ah = rivet.AnalysisHandler("AH%i" % rank)
  # ... analyse some events ...
  ah.collapseEventGroup()
  return ah.serializeContent(True)

mpi_comm = MPI.COMM_WORLD
mpi_rank = mpi_comm.Get_rank()
mpi_size = mpi_comm.Get_size()

res = processRank(mpi_rank)
res = mpi_comm.reduce(res)

if mpi_rank == 0:
  ah.deserializeContent(red[0], mpi_size)
  ah.finalize()
  ah.writeData("mpi_merged_output.yoda.gz")