Transcript Use of Root I/O Trees for CMS Crossings

Twelve Ways to Build CMS
Crossings from Root Files
Benefits and deficiencies of Root trees and clones when :
- NOT dealing with TObjects,
- reading the trees entries NOT sequentially,
- processing them NOT one by one.
CHEP ' 2003
David Chamont (CMS - LLR)
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Outline
• Goal & scope.
• Main use-case.
• 4 kinds of containers for the crossing data model.
• 3 kinds of persistency managers.
• Results.
• Conclusions.
CHEP ' 2003
David Chamont (CMS - LLR)
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Goal & Scope
• Evaluate the benefits of TTree and TClonesArray for
the persistency of CMS event data (whose
classes heavily rely on templates and external
packages).
• Focus on the generation of crossings (pile-up of
about 160 simulated events chosen pseudo
randomly).
• Not covered yet : meta-data, associations
between persistent objects, schema evolution .
CHEP ' 2003
David Chamont (CMS - LLR)
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Main Use-Case
Digitizer
signal event (hits)
Persistency
Manager
minbias event (hits)
Persistency Manager
digis
Persistency
Manager
minbias events file
signal events file
CHEP ' 2003
minbias events file
David Chamont (CMS - LLR)
digis file
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Crossing Data Model
• The folders //root/crossing/* represent the events
composing the current crossing.
• Each event folder contains a container for each
kind of event objects : TrackHit, CaloHit,…
• The kind of container is chosen among four :
– std::vector<> (by value).
– dynamic C array (each event object is wrapped inside a
class instrumented with classdef).
– TObjArray (each event object is wrapped inside a class
derived from TObject).
– TClonesArray (each event object is wrapped inside a
class derived from TObject).
CHEP ' 2003
David Chamont (CMS - LLR)
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Persistency Managers
• The task of a persistency manager is to transfer
an event from memory (TFolder) to disk (TFile) and
vice-versa.
• Three flavors have been implemented :
– RtbPomKeys : directly write the TFolder in the TFile, each
time with a different meaningful name.
– RtbPomTreeMatrix : for each container in the folder, creates
a TMatrixD and attach it to a branch of a TTree.
– RtbPomTreeDirect : attach directly each container to a
branch of a TTree.
CHEP ' 2003
David Chamont (CMS - LLR)
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Implementation issues
• Recent progress :
– can now use -ansi -pedantic.
– nice support of foreign classes.
– better and better support of templates and std
containers.
• Recurrent problems with Root I/O :
– must explicitly ask to parse namespaces, components
types and template instances.
– multiple containers sizes and misleading operator[],
– tuning of chain branchs,
– it is unclear which subset of C++ is supported by Root
I/O, and which in TTree, and which in TClonesArray.
CHEP ' 2003
David Chamont (CMS - LLR)
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Configuration
• Pentium 4, 1.8 GHz.
• ~ 512 Mo of RAM.
• IDE disk.
• RedHat Linux 7.3
• Gcc 3.2
• Root 3.05/03
CHEP ' 2003
David Chamont (CMS - LLR)
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Parameters of the Testbed
• compression level.
• size of buffers.
• split level.
• randomness : burst and jump.
• size of the containers within the events.
• number of crossings.
• direct inheritance or not from TObject, direct
instrumentation or not with ClassDef.
• resetting or not the values in the empty
constructors.
CHEP ' 2003
David Chamont (CMS - LLR)
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Best Results
CHEP ' 2003
File size (Kb/event)
Cpu time (s/crossing)
Stl
C
ObjArray
Clones
Keys
152
3.16
175
4.82
155
9.65
155
4.43
Matrix
149
2.44
149
2.85
149
3.15
149
2.72
Tree
153
2.63
176
4.05
156
7.27
54
1.87
David Chamont (CMS - LLR)
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Remove compression
CHEP ' 2003
File size (Kb/event)
Cpu time (s/crossing)
Stl
C
ObjArray
Clones
Keys
341
1.76
568
3.00
427
8.27
384
2.95
Matrix
400
1.01
400
1.45
400
1.71
400
1.23
Tree
343
1.53
570
2.70
429
6.17
214
1.16
David Chamont (CMS - LLR)
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Then increase random
CHEP ' 2003
File size (Kb/event)
Cpu time (s/crossing)
Stl
C
Keys
341
2.20
568
3.42
427
8.63
384
3.40
Matrix
400
1.31
400
1.78
400
2.03
400
1.59
Tree
343
2.45
570
3.71
429
6.98
214
2.71
David Chamont (CMS - LLR)
ObjArray Clones
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Then reduce containers /10
CHEP ' 2003
File size (Kb/event)
Cpu time (s/crossing)
Stl
C
ObjArray
Clones
Keys
34.9
0.82
54.2
0.96
43.6
1.51
39.5
1.05
Matrix
40.6
0.48
40.6
0.51
40.6
0.59
40.6
0.52
Tree
35.4
1.09
55.0
1.40
44.2
1.59
22.7
1.36
David Chamont (CMS - LLR)
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Then remove random
CHEP ' 2003
File size (Kb/event)
Cpu time (s/crossing)
Stl
C
ObjArray
Clones
Keys
34.9
0.32
54.2
0.44
43.6
0.95
39.5
0.52
Matrix
40.6
0.15
40.6
0.18
40.6
0.24
40.6
0.18
Tree
35.4
0.22
55.0
0.32
44.2
0.66
22.7
0.14
David Chamont (CMS - LLR)
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Conclusions
• We succeeded to read pseudo-random entries from a chain
and dispatch them to few hundred folders (despite tuning of
TChain branches has not been straightforward).
• Support for foreign classes, templates and C++ standard
library has greatly improved.
• The magic couple TTree/TClonesArray has proved very
efficient, yet it requires top level TObjects and the benefits
can become losses with less data or random access
pattern.
• One can simply use std vectors and store them directly into
root files. Their integration in a TTree is not as worth as a
TClonesArray. This could change in the next release of
ROOT.
• It would be interesting to retry with direct associations
between objects, and to apply the testbed to POOL.
CHEP ' 2003
David Chamont (CMS - LLR)
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