14.2. Программа тестирования на Perl'е от Charles Nadeau

Программа на Perl для тестирования openMosix кластера.

Представляю небольшую программу, которую я написал для тестирования openMosix кластера. Она взята из постинга, который я сделал в список рассылки openMosix-devel 6 Марта 2002г.:

Charles wrote this little program (in Perl) to stress test his home cluster (3 P200MMX and a P166). It is a program simulating different sets of stocks in a portfolio for a given period of time. The code is well documented and it should be easy to add/remove stocks and change the average monthly yield and standard deviation for each stock. Since the problem of portfolio optimization cannot be solved analytically, it simulate a lot of portfolios and report the results at the end. Please note that this program does not take stock correlation into account. It is not finished yet but it's a good start. I plan to add more code at the end of the program to improve the reporting format of the data (generating SVG graph on the fly). But the simulation part works very well. In order to take advantage of the parallelism offered by openMosix, it uses the Perl module Parallel::?ForkManager (from CPAN) to span threads that openMosix can then assign to all the machines of the cluster (it also require another module for the statistical calculations, don't forget to install both, I provide the URLs in the comments of the code). Take a look at it and tell me what you think. Cheers!

#! /usr/bin/perl -w # this mill unlock this process and all tis childs sub unlock { open (OUTFILE,">/proc/self/lock") || die "Could not unlock myself!\n"; print OUTFILE "0"; } unlock; # this will count the nodes sub cpucount { $CLUSTERDIR="/proc/hpc/nodes/"; $howmany=0; opendir($nodes, $CLUSTERDIR); while(readdir($nodes)) { $howmany++; } $howmany--; $howmany--; closedir ($nodes); return $howmany; } my $processes=cpucount; $processes=$processes*3; print("starting $processes processes\n"); #Portfolio.pl, version 0.1 #Perl program that simulate a portfolios for various stock composition for a given period of time #We run various scenarios to find the mix of assets that give the best performance/risk ratio #This method is base on the book "The intelligent asset allocator" by William Bernstein #Can be used to test an OpenMosix cluster #This program is licensed under GPL #Author: Charles-E. Nadeau Ph.D., (c) 2002 #E-mail address: charlesnadeau AT hotmail DOT com use Parallel::ForkManager; #We use a module to parallelize the calculation #Available at http://theoryx5.uwinnipeg.ca/mod_perl/cpan-search?filetype=%20distribution%20name%20or%20description;join=and;arrange=file;download=auto;stem=no;case=clike;site=ftp.funet.fi;age=;distinfo=2589 use Statistics::Descriptive::Discrete; #A module providing statistical values #Available at http://theoryx5.uwinnipeg.ca/mod_perl/cpan-search?new=Search;filetype=%20distribution%20name%20or%20description;join=and;arrange=file;download=auto;stem=no;case=clike;site=ftp.funet.fi;age=;distinfo=2988 srand; #We initialize the random number generator #Initializing constant $NumberOfSimulation=$processes; #Number of simulation to run $NumberOfMonth=100000; #Number of month for wich to run the simulation $NumberOfStock=6; #Number of different stocks in the simulation #Portfolio to simulate #TODO: Read the stock details from a file $Stock[0][0]="BRKB"; #Stock ticker $Stock[0][1]=0.01469184; #Stock average monthly return $Stock[0][2]=0.071724934; #Stock average monthly standard deviation $Stock[1][0]="TEST "; #Stock ticker $Stock[1][1]=-0.01519; #Stock average monthly return $Stock[1][2]=0.063773903; #Stock average monthly standard deviation $Stock[2][0]="SPDR"; #Stock ticker $Stock[2][1]=0.008922718; #Stock average monthly return $Stock[2][2]=0.041688404; #Stock average monthly standard deviation $Stock[3][0]="BRKB"; #Stock ticker $Stock[3][1]=0.01469184; #Stock average monthly return $Stock[3][2]=0.071724934; #Stock average monthly standard deviation $Stock[4][0]="TEST "; #Stock ticker $Stock[4][1]=-0.01519; #Stock average monthly return $Stock[4][2]=0.063773903; #Stock average monthly standard deviation $Stock[5][0]="SPDR"; #Stock ticker $Stock[5][1]=0.008922718; #Stock average monthly return $Stock[5][2]=0.041688404; #Stock average monthly standard deviation my $pm = new Parallel::ForkManager($NumberOfSimulation); #Specify the number of threads to span $pm->run_on_start( sub { my ($pid,$ident)=@_; print "started, pid: $pid\n"; } ); #We initialize the array that will contain the results @Results=(); for $i (0..$NumberOfSimulation-1){ for $j (0..$NumberOfStock+3){ $Results[$i][$j]=0.0; #Equal to 0.0 to start } } for $i (0..$NumberOfSimulation-1){ #Loop on the number of simulation to run $Results[$i][0]=$i; #The first column of each line is the number of the simulation $pm->start and next; #Start the thread $TotalRatio=1; #The sum of the proprtion of each stock #Here we calculate the portion of each investment in the portfolio for a given simulation for $j (0..$NumberOfStock-2){ #We loop on all stock until the second to last one #TODO: Replace rand by something from Math::TrulyRandom $Ratio[$j]=rand($TotalRatio); $Results[$i][$j+1]=$Ratio[$j]; #We store the ratio associated to this stock $TotalRatio=$TotalRatio-$Ratio[$j]; } $Ratio[$NumberOfStock-1]=$TotalRatio; #In order to have a total of the ratios equal to one, we set the last ratio to be the remainder $Results[$i][$NumberOfStock]=$Ratio[$NumberOfStock-1]; #We store the ratio associated to this stock. Special case for the last stock $InvestmentValue=1; #Initially the investment value is 1 time the initial capital amount. my $stats=new Statistics::Descriptive::Discrete; #We initialize the module used to calculate the means and standard deviations for $j (1..$NumberOfMonth){ #Loop on the number of months $MonthlyGrowth[$j]=0.0; #By how much did we grow this month. Initially, no growth yet. #We loop on each stock to find its monthly contribution to the yield for $k (0..$NumberOfStock-1){ $MonthlyGrowth[$j]=$MonthlyGrowth[$j]+($Ratio[$k]*((gaussian_rand()*$Stock[$k][2])+$Stock[$k][1])); #We had the growth for this stock to the stock already calculated for the preceding stocks } $stats->add_data($MonthlyGrowth[$j]); #Add the yield for this month so we can later on have the mean and standard deviation for this simulation $InvestmentValue=$InvestmentValue*(1+$MonthlyGrowth[$j]); #Value of the Investment after this month } $Results[$i][$NumberOfStock+1]=$stats->mean(); #Calculate the average monthly growth $Results[$i][$NumberOfStock+2]=$stats->standard_deviation(); #Calculate the standard deviation of the monthly growth $pm->finish; #Finish the thread } $pm->wait_all_children; #We wait until all threads are finished #Printing the results print "Simulation "; for $j (0..$NumberOfStock-1){ print "Ratio$Stock[$j][0] "; } print " Mean StdDev YieldRatio\n"; for $i (0..$NumberOfSimulation-1){ printf "%10d ", $Results[$i][0]; for $j (1..$NumberOfStock){ printf " %6.2f ",$Results[$i][$j]; } if($Results[$i][$NumberOfStock+2]!=0) { printf "%5.4f %5.4f %5.4f\n", $Results[$i][$NumberOfStock+1], $Results[$i][$NumberOfStock+2], ($Results[$i][$NumberOfStock+1]/$Results[$i][$NumberOfStock+2]); } else { printf "%5.4f %5.4f %5.4f\n", $Results[$i][$NumberOfStock+1], $Results[$i][$NumberOfStock+2], 0; } } #Subroutines #Subroutine to generate two numbers normally distributed #From "The Perl Cookbook", recipe 2.10 sub gaussian_rand { my ($u1, $u2); my $w; my ($g1, $g2); do { $u1=2*rand()-1; $u2=2*rand()-1; $w=$u1*$u1+$u2*$u2; } while ($w>=1 || $w==0); #There was an error in the recipe, I corrected it here $w=sqrt(-2*log($w)/$w); $g2=$u1*$w; $g1=$u2*$w; return wantarray ? ($g1,$g2) : $g1; }