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Parallel Writing with Big Data

NOTE: this approach is not currently supported at LCLS since maintenance of the mpi-aware hdf5 package is time-consuming and the same parallel-writing can often be achieved using the newer "virtual dataset" feature of hdf5.

Next we use the mpi file driver of the Hdf5 library to distribute our processing of the cspad. A common practice with MPI is to use reduce or gather for rank local results at the end of the script. In this example, we

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  • With the mpi hdf5 file driver, you must collectively
    • create the file
    • create the datastes
    • resize the datasets
  • Individual ranks may write to different elements of the dataset
  • To collectively increase the size of the datasets, all ranks read all of the small data events and resize when all items are presentcollectively resize every 100 events or so.
    • This means that every 100 events, all ranks are doing the same thing. In between these resizes, they may be able to do parallel processing.
  • The extend of parallel processing performed is limited by the filesystem. Each rank is independently writing to different points in the Hdf5 file, however they filesystem may serialize these operations. Setting the lustre stripe size on the output file ahead of time can help - but in short, getting good performance out of parallel disk output can be involved.
  • To detect that the cspad is present without reading the big data behind it, we inspect the EventKeys
    • This is the trickiest part - it involves checking that there is an
    To detect that the cspad is present without reading the big data behind it, we inspect the EventKeys
    • This is the trickiest part - it involves checking that there is an event key whose source has the alias 'cspad'
  • The NUM_EVENTS _TO_WRITE can be adjusted to have the script run faster (it is presently set to more than the number of events in the run)stop earlier.
  • Since datasets must be created collectively, it is much more awkward (but not impossible) to not create the cspad hproj dataset until the first event is read, so we assume the size of 1691 from previous work

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  • , but it is not to hard to figure out the size ahead of time.


Code Block
languagepython
import numpy as np
from mpi4py import MPI
import math
import psana
import h5py
import time
startTime = time.time()
ds = psana.DataSource('exp=xpptut15:run=54:smd')
 
def hasCspad(evt, aliasMap):
    for key in evt.keys():
        if 'cspad' == aliasMap.alias(key.src()):
            return True
    return False
 
LASER_ON_EVR_CODE = 92 
CSPAD_HPROJ_LEN = 16911738
NUM_EVENTS_TO_WRITE=1300

ds = psana.DataSource('exp=xpptut15:run=54:smd')
 = 120  # set to non-zero to stop early
RESIZE_GROW = 100  # how many events between collective resizes
currentDsetSize = 0
numResizes=0
 
chunkSizeSmallData = 2048
h5out = h5py.File("my_saved_data_for_xppdaq12xpptut15_run54_cspad_hproj.h5", 'w',
                   driver='mpio', comm=MPI.COMM_WORLD)
eventDataGroup = h5out.create_group('EventData')
evtSecDs = eventDataGroup.create_dataset('event_seconds',
  (currentDsetSize,),                        (0dtype='i4', chunks=(chunkSizeSmallData,), maxshape=(None,))
evtNanoDs = eventDataGroup.create_dataset('event_nanoseconds',
  (currentDsetSize,), dtype='i4', chunks=True(chunkSizeSmallData,), maxshape=(None,))
evtNanoDsevtFiducialsDs = eventDataGroup.create_dataset('event_nanosecondsfiducials',
                          (0(currentDsetSize,), dtype='i4', chunks=True(chunkSizeSmallData,), maxshape=(None,))
ebeamL3Ds = eventDataGroup.create_dataset('ebeam_L3',
                          (0currentDsetSize,), dtype='f4', chunks=True(chunkSizeSmallData,), maxshape=(None,))
laserOnDs = eventDataGroup.create_dataset('laser_on',
                          (0(currentDsetSize,), dtype='i1', chunks=True=(chunkSizeSmallData,), maxshape=(None,))
cspadHprojDs = eventDataGroup.create_dataset('cspadHproj',
                          (0currentDsetSize,CSPAD_HPROJ_LEN), dtype='f4', 
                          chunks=True(2000,CSPAD_HPROJ_LEN), maxshape=(None, CSPAD_HPROJ_LEN))
 
cspad = psana.Detector('cspad', ds.env())
 
histogram_bin_edges = [-500, -400, -200, -150, -100, -50, -25, 0, 5, 10, 15, 20, 25, 50, 100, 200, 500]
local_cspad_histogram = np.zeros((len(histogram_bin_edges)-1,), np.int64)
timeResize = 0.0
timeWriting = 0.0
 
nextDsIdx = -1
for evtIdx, evt in enumerate(ds.events():
  )):
  if NUM_EVENTS > 0 and evtIdx >= NUM_EVENTS:
    break
  eventId = evt.get(psana.EventId)
    evr = evt.get(psana.EvrData.DataV4, psana.Source('evr0'))
    ebeam = evt.get(psana.Bld.BldDataEBeamV7, psana.Source('EBeam'))
 
  # check for event that has everything we want to write
  if (eventId is None) or (evr is None) or (ebeam is None) or \
 # check for event that has everything we want to write(not hasCspad(evt, ds.env().aliasMap())):
    continue
  nextDsIdx += if1
 (eventId is
 None) orif (evrnextDsIdx is>= NonecurrentDsetSize):
 or \
   t0 = time.time()
  (ebeam is None)# orcollectively (not hasCspad(evt, ds.env().aliasMap())):
  resize all the chunked datasets
    currentDsetSize += continueRESIZE_GROW

    nextDsIdxnumResizes += 1

    if# nextDsIdx >= NUM_EVENTS_TO_WRITE: 
        break
this MPI_Barrier is not necessary, but helps to illustrate the collective operation
    # collectively resize chunked datasetsMPI.COMM_WORLD.Barrier()
    evtSecDs.resize((nextDsIdx+RESIZE_GROW,))
    evtSecDsevtNanoDs.resize((nextDsIdx+1RESIZE_GROW,))
    evtNanoDsevtFiducialsDs.resize((nextDsIdx+1RESIZE_GROW,))
    ebeamL3Ds.resize((nextDsIdx+1+RESIZE_GROW,))
    laserOnDs.resize((nextDsIdx+1RESIZE_GROW,))
    cspadHprojDs.resize((nextDsIdx+1RESIZE_GROW, CSPAD_HPROJ_LEN))

      timeResize += time.time()-t0
  # only process this ranks events
    if nextDsIdx % MPI.COMM_WORLD.Get_size() != MPI.COMM_WORLD.Get_rank():
    continue
  t0  continue

  = time.time()
  # useexpensive detectorper xface for cspad imagerank processing:
    cspadImage = cspad.image(evt)
    local_cspad_histogram += np.histogram(cspad.calib(evt), 
                                 histogram_bin_edges)[0]
    cspadHproj = np.sum(cspadImage, 0)
 
    evtSecDs[nextDsIdx] = eventId.time()[0]
    evtNanoDs[nextDsIdx] = eventId.time()[1]
  evtFiducialsDs[nextDsIdx] = eventId.fiducials()
  ebeamL3Ds[nextDsIdx] = ebeam.ebeamL3Energy()
    laserOnDs[nextDsIdx] = evr.present(LASER_ON_EVR_CODE)
  cspadHprojDs[nextDsIdx,:] =   cspadHprojDs[nextDsIdx,:] = cspadHproj[:]

cspadHproj
  timeWriting += time.time()-t0
## finished processing, collectively form summary
# datasets, and get final historgram
summaryGroup = h5out.create_group('Summary')
summaryGroup.create_dataset('cspad_histogram_bins', 
                                data = histogram_bin_edges)
finalHistDs = summaryGroup.create_dataset('cspad_histogram_values', 
                             (len(local_cspad_histogram),), dtype='i8')
 
if MPI.COMM_WORLD.Get_rank()==0:
    finalHistogram = np.zeros_like(local_cspad_histogram)
    MPI.COMM_WORLD.Reduce(sendbuf=[local_cspad_histogram, MPI.INT64_T],
                          recvbuf=[finalHistogram, MPI.INT64_T],
                          op=MPI.SUM, root=0)
    finalHistDs[:] = local_cspad_histogram[:]
else:
    MPI.COMM_WORLD.Reduce(sendbuf=[local_cspad_histogram, MPI.INT64_T],
                          recvbuf=[None, MPI.INT64_T],
                          op=MPI.SUM, root=0)
     
h5out.close()
totalTime = time.time()-startTime
hz = evtIdx/totalTime
print(f"rnk={MPI.COMM_WORLD.Get_rank():3d} evts={evtIdx:4d} time={totalTime:.2f} resizes={numResizes:d}    op=MPI.SUM, root=0)
    
h5out.close()
        

time_per_resize={timeResizes/float(numResizes):.2f} writing={timeWriting:.2f} Hz={hz:.2f}"  
#(MPI.COMM_WORLD.Get_rank(), evtIdx, totalTime, numResizes, timeResize/float(numResizes), timeWriting, hz)

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