1. Physics Analysis inside the Oracle DB Progress report 10 Octobre 2013

2. Performance with parallel scaling  Original result showed DB-version of the H+Z benchmark was faster than serial execution of the root-ntuple-analysis,on test3-setup 2  Parallelism with root can be mimicked by running multiple simultaneous jobs each running on a subset of data  Ntuple-version improves more with parallel execution as the DB is limited by IO more data needs to be read by the DB compared to the column-storage in the ntuples  Test3-setup has datafiles stored on nfs, resulting in relativly slow I/O speed of ~250 MB/s

3. Ttbar cutflow analysis  A cutflow analysis for the top-pair production cross-section measurement was implemented as a new benchmark.  Original “RootCore”-packages used by ATLAS top physics group are compared to a modified set of packages that retrieve data from the DB via an SQL-query. 3 More realistic than Higgs+Z benchark:  uses 262 variables (compared to 40 in Higgs+Z)  also uses data from photon and primary-vertex objects (photon- table=114 GB table!)  Another 3 external libraries added, used to call functions for corrections on electron and photon objects  Selection of electron and photon objects can not be done as single table select, the corrections depends on the number of vertices in the event (pile-up) so an inner join with the vertex-table is required Only “electron”-channel implemented so far but “muon”-channel is very similar…

4. Performance with parallel scaling Ttbar cutflow analysis from root- ntuples is very slow in serial! But when running multiple simultaneous root-jobs it again becomes faster than DB-version … this is on the test3-setup where I/O is relatively slow 4

5. Mapred-cluster  The mapred-cluster (described in previous report) has better I/O reads 5 nodes connected to 5 disk arrays with a total of 60 disks -> up to 2500 MB/s  As shown last time root-ntuple analysis is faster on this cluster for Higgs+Z benchmark: 5 40 root-jobs: 71 seconds SQL parallel 40: 135 seconds

6. Mapred-cluster But the root-ntuple analysis is slower than DB for ttbar cutflow analysis 6 Optimistic conclusion: Oracle DB beats root-ntuple analysis for realistic physics analysis, when given fast enough I/O reads! 40 root-jobs: 588 seconds SQL parallel 40: 372 seconds

7. ttbar cutflow: why so slow? But WHY is the ttbar cutflow analysis so much slower than Higgs+Z for the ntuple-analysis on the “mapred”-cluster ? As a test I rewrote the ttbar-analysis packages to return the result only for a specific sub-selection, and similary broke the SQL-version down to produce the same result This allows to compare the timing results for DB vs ntuple for different type of selections! 7  The DB analysis was always faster, even though much less branches need to be loaded for the separate object-selection than for the full ttbar cutflow (=262 branches)  This looked suspicious to me!

8. ttbar cutflow: why so slow? I wrote a single root-macro to reproduce the same results as the single-electron- select from the ttbar-packages This macro was much faster: 55 seconds instead of 214 seconds 8  I think the difference in time is due to the ttbar-packages disabling branches and calling Tree->GetEntry() while my root-macro call branch->GetEntry(), this turns out to make a large difference in CPU: good-electron, simple macro w. tree->GetEntry(), 40 root-jobs: Time = 156 seconds good-electron, simple macro w. load branches, 40 root-jobs: Time = 156 seconds

9. Summary  Real analysis code is not always optimized to run as fast as possible!  ttbar cutflow is an interesting case-study and it uses many different object-selections that can be studied separately  Experience from converting ttbar cutflow in SQL: Using a materialized view for the goodrunlist-selection Adding external libraries (PL/SQL calling Java calling C++) is easy once you know how to! It is more difficult when selection requires cross-table selection with INNER JOIN (photon/electron selection requiring info from vertex-table for pileup correction) but not impossible I’m using lots of MATERIALIZE hints, I don’t trust the SQL optimizer as it goes a crazy with all those JOINs… I still have a single analysis query to run entire cutflow, eventually might be better/easier to write the code by explicitly creating tables to hold intermediate selection (using NOLOGGING-option and parallel dml hints to speed up table-creation)  To do: Finish ttbar cutflow, there are still some cuts requiring cross-table matching not implemented Add muon-channel, ntuple-analysis produces 2 cut-flow for muon- and electron-channel in a single analysis as they use the results after good object selection, I can do the same in SQL but I will need to create intermediate tables to hold temporary selection 9