1 ROOT vs PAW Davide Grandi INFN Milano

2 Summary AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano ROOT and NT file size (different compressions and split) ROOT and PAW I/O performance (selection functions for analysis and compiled codes) PAW and ROOT functionality (C++ knowledge, GUI etc.)

3 Hardware & Software AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano MACHINE: pcamsf2 OS: RH Linux 7.2 RAM 1 GB local DISK RAID5 ROOT version: 3.03/04 19 April 2002 PAW version: 2.12/22 13 June 2001

4 ROOT and NT file size (10k ev) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano modeMB NT122.6 NT gzipped46.7 NT h2root53.4 Root indicesSplit = 1Comp = Root pointersSplit = 1Comp = RootSplit = 2Comp = 254.3

5 I/O performance AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano PAW features: UWFUNC Code automatically compiled by PAW Chain of ntuples Gzipping ntuples to compare with Root space occupancy ROOT features: MakeSelector (create a *.C and *.h) TChain ACLiC to compile, create shared lib and link TClonesArray

6 PAW & ROOT Chains AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano PAW: ntuples (6 of approx 120 MB) used for first test of PAW speed Ntuples gunzipped to compare with ROOT speed PAW tests with/without gunzipping ntuples ROOT: Trees written with normla compression level (1) and maximum (9) 6 trees loaded with TChain MakeSelector code executed with ACLiC calls (automatic creation of a shared lib, link and compilation from ROOT)

7 few words on ROOT (1) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano

8 few words on ROOT (2) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano Compression: gzip algorithm Branches (independent variables: object, list, folder, array) Leaves (variables in a branch) Split level default is 99 (object split to the maximum) N.B. Level 1 TClonesArray not splitted More split: fast to read slow to write Collections (object, pointers to cast)

9 Variables selection AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano PAW has a nice feature of reading only variables required by the code (optimized for end users) Selection function from V. Choutko (fortran function) ROOT hasnt the automatical feature of redaing only variables requird by the code Selection fortran function transformed to C/C++ style (not so easy…)

10 PAW & ROOT speed (1) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano filesAnalysis time (U S E CPU%) NT gunzipped : % Root comp=1Creating sh lib : % Root comp=1Sh lib created : % All ROOT tree branches (400) read when function requires only 40-50

11 PAW & ROOT speed (2) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano filesAnalysis time (U S E CPU%) NT no gunzip : % Root comp=1Creating sh lib : % Root comp=1Creating sh lib : % Root comp=9Creating sh lib : % Root comp=9Sh lib created : % Only required branches (40-50) read, but carefully in creation order (*.h file) !!!!

12 Clone sArray AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano New production (from A. Klimentov) with TClonesArray (manage objects of the same class, constant size of element, reuses memory), compression level 2 and 2 different split levels (1 and 2) Selection function always from V. Choutko For split level 1: all tree was loaded For split level 2: only needeb branches are readed (similar to h2root converted file…) N.B. probably optimization is possible…

13 Clone sArray (1K events) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano filesAnalysis time (U S E CPU%) NT gunzipped : % NT : % Root split 1Creating sh lib : % Root split 1Sh lib created : % Root split 2Creating sh lib : % Root split 2Sh lib created : %

14 Clone sArray (10K events) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano filesAnalysis time (U S E CPU%) NT gunzipped : % NT : % Root split 1Creating sh lib : % Root split 1Sh lib created : % Root split 2Creating sh lib : % Root split 2Sh lib created : %

15 Clone sArray (30K events) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano filesAnalysis time (U S E CPU%) NT gunzipped : % NT : % Root split 1Creating sh lib : % Root split 1Sh lib created : % Root split 2Creating sh lib : % Root split 2Sh lib created : %

16 PAW and ROOT functionality AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano CPU: PAW is almost always below 50% Well known analysis tool Easy to write a fortran code for ntuple analysis CPU: Root seems to optimize load (nearly 100% the machine is almost blocked!) Many features not yet explored Easy to produce.ps or.gif files

17 ROOT GUI & other features AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano Graphics in ROOT is greately improved: you can select with mouse TBrowser (window and folders) CINT support C/C++ (better than only C++) Fit is simple Insert Text & Latex math expression 3D histograms rotated with the mouse ……. sometimes not stable!

18 ROOT GUI(2) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano

19 ROOT GUI(3) AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano

20 C++ knowledge AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano For h2root converted: fortran users dont need big experience in C++ for normal selections For CloneArrays C++ needed is more complicated (MakeSelector doesnt recognize ClonesArray…. So they have been loaded by hand) Root Macros: easy to realize when skeleton produced by root features as MakeSelector or MakeClass A bit harder if made by hand (>400 branches/variables)

21 Preliminary conclusions AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano h2root: PAW is faster compared with root selections because of recognizing required variables Hand made modification of ROOT macro gives ROOT faster than PAW (2 times) ClonesArray: PAW is faster than ROOT (comparable speed: more tests are needed) Split level 1 seems better (need to study optimization as reading only branches required)

22 Aknowledgements AMS Computing Meeting june 10th 2002 Davide Grandi INFN Milano Id like to thank for the support and for important suggestions and programming lessons: Alexei Klimentov Vitaly Choutko of CERN AMS staff Matteo Boschini Mikhail Levtchenko of Milano AMS staff