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PARALLEL IMPLEMENTATION OF PARTON STRING MODEL EVENT GENERATOR
A.Asryan, G.Feofilov, S.Nemnyugin, P.Naumenko,
V.Solodkov, V.Vechernin, A.Zarochencev, V.Zolotarev
Saint-Petersburg State University,Russian Federation
Table of contents
Problem (what is the matter?)ModelPSM event generatorWhy parallel?Parallel PSM event generatorPerformance testsResults of simulation
Keywords of the report:
software
Monte-Carlo event generator PSM(Parton String Model)
The method of simulation is based on the Parton String model with strings fusion taken into account.
The modified model has been suggested by M.A.Braun (Saint-Petersburg State University) and C.Pajares (University Santiago de Compostela).
The model was realized as PSM Monte Carlo event generator by N.Amelin (JINR, Dubna).
M.A.Braun, C.Pajares, Phys. Lett. B287 (1992) 154; Nucl. Phys. B390 (1993) 542, 549.
N.S. Amelin, M.A.Braun, C.Pajares, Phys. Lett. B306 (1993) 312; Z.Phys. C63 (1994) 507.
PSM PROGRAM GENERATORSHORT DESCRIPTION: FIRST VERSION IS WRITTEN BY DR. N. AMELIN FROM THE JOINT INSTITUTE FOR
NUCLEAR RESEARCH (DUBNA, RUSSIA) PURPOSE TO SIMULATE NUCLEON-NUCLEON, NUCLEON-NUCLEUS AND NUCLEUS-
NUCLEUS COLLISIONS AT ULTRA-RELATIVISTIC ENERGIES MODEL PARTON STRING MODELSee N. Armesto, M. A. Braun, E. G. Ferreiro and C. Pajares, Phys. Lett. B344 (1995) 301 SOME MODEL DETAILS:GLUON RADIATION AND HARD GLUON RESCATTERINGS ARE INCLUDED BEAM AND TARGET MAY BE: N, P, anti-P, D, He, Be, B, C, O, Al, Si, S, Ar, Ca, Cu, Ag,
Xe, W, Au, Pb, U
RANGE OF ENERGIES (in Gev): 10 < Ecm / NUCLEON < 15 000 IMPACT PARAMETER: FIXED OR RANDOM NUMBER OF EVENTS: > 1 STRING FUSION: MAY BE INCLUDED GLUON RESCATTERING: MAY BE INCLUDED BUT IS EXTREMELY TIME CONSUMING IT IS POSSIBLE TO SIMULATE +, –, K+, K– INDUCED REACTIONS METHOD OF SIMULATION: MONTE CARLO METHOD
PSM PROGRAM GENERATOR
Long-range correlations
Features of installation taken into account
WHY PARALLEL?
A lot of statistics (104 - 106 of simulated events) is required in a Monte Carlo simulation to get statistically reliable results.
WHY PARALLEL?
Simulation is time consuming if all options of the model are turned on:
hard gluon rescattering; string fusion; resonances decay; rescattering of secondaries (sourced from
string breaking) + secondaries and spectators (!).
WHY PARALLEL?
13.5 secs/event on 600 MHz CPU with all options of the model turned on.
Time-consuming? -> supercomputing/parallel programming should be applied.
SIMULATION WITH THE PSM MONTE CARLO GENERATOR
1. Getting “raw” output data with the PSM Monte Carlo generator
2. Processing of the output file by the PERL program (new). The result is a correlation diagram as the Postscript file
Parallel algorithms of PSM modeling have been developed
and tested on parallel clusters of Saint Petersburg State
University.
Parallel version of the PSM Monte-Carlo generator is realized with MPICH
library (version 1.2.4)
PROBLEMS TO BE SOLVED
1. Effective parallelization of the PSM Monte Carlo generator:configuration of cluster is important
2. Effective output operations:25 000 events ~ 1 Gbyte output file
500 000 events ~ 20 Gbytes output file3. Pseudorandom number generator: parallel, long enough period, good statistical
properties (low correlations) etc.
PARALLEL PSM GENERATOR FOR CLUSTER WITH LARGE LOCAL
DISKS
Master-slave model of parallel programming.
Distribution of events to be simulated between nodes of the parallel cluster.
Master process coordinates work of other (slave) processes.
Master process broadcasts (scatters) input data.
Results of modeling are saved in local files (/tmp directory of a hosts).
PARALLEL PSM GENERATOR FOR CLUSTER WITH LARGE LOCAL
DISKS
After completion of the PSM program local files are sent to the “master” host.
The program (Perl) of statistical data processing is started.
Result (correlation diagram) is saved in the file in Postscript-format.
PARALLEL PSM GENERATOR FOR CLUSTER WITH LARGE LOCAL
DISKS
Pro:
Communication network is not overloaded -> maximum scalability, theoretical limit of Ahmdal’s law
Contra:A lot of work when merging local output files
PARALLEL PSM MONTE CARLO GENERATOR FOR CLUSTER WITH FILE
SERVER
Master-slave model of parallel programming.
Parallel output in the shared file. It is located on the file server.
Loading of a communication subsystem of the multiprocessor computer grows.
PARALLEL PSM MONTE CARLO GENERATOR FOR CLUSTER WITH FILE
SERVER
Pro:More pleasant “postmortem” life
Contra:Communication network is overloaded > poor scalability
PARALLEL PSM MONTE CARLO GENERATOR FOR CLUSTER WITH FILE
SERVER
Another approach - compromise:
not use parallel output operations
use gathering operations.
HARDWARE*
“ALICE” cluster in Saint Petersburg:
7 x 2CPU hosts (2х600 MHz, 512 Mbyte RAM, 2х4,5 Gbyte hard disks)
+1 server (1200 MHz, 256 Mbyte RAM, 40 Gbyte hard disk)
---------------------------------------------------* Before September 2004, now 1 Gbyte RAM, 40 Gbyte
hard disks
SPEEDUP TEST (1st approach)
Speedup vs Number of processors for different PSM options (speedup=1 CPU time/N CPUs time) – ABCD:
A) HARD PART: True/False; B) STRING FUSION;
C) RESONANCE DECAY; D) RESCATTERING
PERFORMANCE TESTS1000 eventsTTTT--------------------------------------------------------------- HOSTs time---------------------------------------------------------------
1 224m21.353s 2 114m13.243s 3 87m59.043s 4 62m.83.205s 5 54m67.012s 6 48m71.642s 7 44m32.502s
PERFORMANCE TESTS1000 events TTTF--------------------------------------------------------------- HOSTs time---------------------------------------------------------------
1 14m46.613s
2 7m59.188s
3 5m24.613s
4 4m16.065s
5 3m35.322s
6 3m9.149s
7 2m49.423s
PERFORMANCE TESTS1000 eventsTTFF--------------------------------------------------------------- HOSTs time---------------------------------------------------------------
1 14m32.460s
2 7m24.718s
3 4m47.604s
4 3m41.443s
5 3m11.629s
6 2m41.460s
7 2m13.166s
PERFORMANCE TESTS1000 events -TFFF--------------------------------------------------------------- HOSTs time---------------------------------------------------------------
1 4m35.410s
2 2m20.976s
3 1m35.235s
4 1m10.788s
5 0m58.430s
6 0m46.027s
7 0m41.621s
RESULTS OF SIMULATION
RESULTS OF SIMULATION
PSEUDORANDOM NUMBERS GENERATOR
Copy of the CERN Library routine RECUSQ
Authors: R.Brun, F.Carminati
Up to 215 pseudorandom sequences
Each sequence has a period of 10**9 numbers – good for 104 105 statistics, not so good for 106 statistics.
PSEUDORANDOM NUMBERS GENERATOR
SUBROUTINE RANLUX(RVEC,LENV)C Subtract-and-borrow random number generator proposed byC Marsaglia and Zaman, implemented by F. James with the nameC RCARRY in 1991, and later improved by Martin LuescherC in 1993 to produce "Luxury Pseudorandom Numbers".C Fortran 77 coded by F. James, 1993C references: C M. Luscher, Computer Physics Communications 79 (1994) 100C F. James, Computer Physics Communications 79 (1994) 111C LUXURY LEVELS.C ------ ------ The available luxury levels are:C level 0 (p=24): equivalent to the original RCARRY of Marsaglia and Zaman, very long
period,C but fails many tests.C level 1 (p=48): considerable improvement in quality over level 0, now passes the
gap test,C but still fails spectral test.C level 2 (p=97): passes all known tests, but theoretically still defective.C level 3 (p=223): DEFAULT VALUE. Any theoretically possible correlations have very
smallC chance of being observed.C level 4 (p=389): highest possible luxury, all 24 bits chaotic.
CONCLUSIONS/FURTHER PLANS
Parallel PSM event generator is developed. Parallel PSM event generator works on “ALICE”
computing cluster (Saint Petersburg State University).
It is scalable. Parallelization methods for different cluster
configurations are analyzed. “Compromise” version is under development. High-performance -> more statistically reliable
results + more realistic physical model