Using MKL 9.1_beta made no difference in the prior benchmark, but it does improve speed in an earlier benchmark I posted.
From: http://projects.scipy.org/pipermail/numpy-discussion/2007-January/025673.html ================================================================================ ''' A program that uses Monte Carlo to estimate how often the number of rare events with a Poisson distribution will differ by a given amount. ''' import numpy as n from numpy.random import poisson from time import time lam = 4.0 # mu & var for Poisson distributed rands (they are equal in Poisson) N = 10 #number of times to run the program maxNumEvents = 20 #events larger than this are ignored numPois = 100000 #number of pairs of outcomes to generate freqA = 2 #number of times event A occurred freqB = 6 #number of times event B occurred print "#rands fraction [freqA,freqB] fraction [lam,lam] largest% total[mean,mean]" t0 = time() for g in range(1): for h in range(N): suma = n.zeros((maxNumEvents+1,maxNumEvents+1), int) #possible outcomes array count = poisson(lam, size =(numPois,2)) #generate array of pairs of Poissons for i in range(numPois): #if count[i,0] > maxNumEvents: continue #if count[i,1] > maxNumEvents: continue suma[count[i,0],count[i,1]] += 1 d = n.sum(suma) print d, float(suma[freqA,freqB])/d, float(suma[lam,lam])/d , suma.max(), suma[lam,lam] print 'time', time()-t0 Using the SUSE NumPy rpm: python relative_risk.py #rands fraction [2,6] fraction [lam,lam] largest% total[mean,mean] 100000 0.01539 0.03869 3869 3869 100000 0.01534 0.03766 3907 3766 100000 0.01553 0.03841 3859 3841 100000 0.01496 0.03943 3943 3943 100000 0.01513 0.03829 3856 3829 100000 0.01485 0.03825 3993 3825 100000 0.01545 0.03716 3859 3716 100000 0.01526 0.03909 3919 3909 100000 0.01491 0.03826 3913 3826 100000 0.01478 0.03771 3782 3771 time 2.38847184181 Using the MKL [8.1] NumPy: python relative_risk.py #rands fraction [2,6] fraction [lam,lam] largest% total[mean,mean] 100000 0.01502 0.03764 3895 3764 100000 0.01513 0.03841 3841 3841 100000 0.01511 0.03753 3810 3753 100000 0.01577 0.03766 3873 3766 100000 0.01541 0.0373 3963 3730 100000 0.01586 0.03862 3912 3862 100000 0.01552 0.03785 3870 3785 100000 0.01502 0.03854 3896 3854 100000 0.015 0.03803 3880 3803 100000 0.01515 0.03749 3855 3749 time 2.0455300808 So the rpm version only takes ~17% longer to run this program. I'm surprised that there isn't a larger difference. Perhaps there will be in a different type of program. BTW, the cpu is an Intel e6600 Core 2 Duo overclocked to 3.06 GHz (it will run reliably at 3.24 GHz). ============================================================================ With NumPy built using MKL 9.1_beta the program runs in 1.66 seconds. Correcting for the slightly lower CPU speed used (2.93 GHz), this corresponds to 1.59 seconds. The 8.1 version takes 29% longer (this may be partially/all due to different compiler flags being used), and the 8.1 version used with Python compiled with gcc instead of icc takes 50% longer to run. The icc flags used were: -fast (enables -xP -O3 -ipo -no-prec-div -static) -funroll-loops -fno-alias -parallel I don't know if there are obviously better choices for the Core 2 Duo. I'd like to run a more comprehensive benchmark, say Scimark translated from C to Python/NumPy. http://math.nist.gov/scimark2/download_c.html -rex -- Those who forget the pasta are condemed to reheat it. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
