+++ /dev/null
-#!/usr/bin/env python
-
-# Copyright (c) 2010-2011, 2014. The SimGrid Team.
-# All rights reserved.
-
-# This program is free software; you can redistribute it and/or modify it
-# under the terms of the license (GNU LGPL) which comes with this package.
-
-import sys
-from math import sqrt
-
-
-if len(sys.argv) < 5:
- print("Usage : %s datafile links latency bandwidth [size...]" % sys.argv[0])
- print("where : datafile is a SkaMPI pingpong measurement log file");
- print(" links is the number of links between nodes")
- print(" latency is the nominal latency given in the platform file")
- print(" bandwidth is the nominal bandwidth given in the platform file")
- print(" size are segments limits")
- sys.exit(-1)
-
-##-----------------------------------------
-## avg : return average of a list of values
-## param l list of values
-##-----------------------------------------
-def avg (l):
- sum = 0
- for e in l:
- sum += float(e);
- return sum / len(l)
-
-##-------------------------------------------------
-## cov : covariance
-## param X first data vector (..x_i..)
-## param Y second data vector (..x_i..)
-## = 1/n \Sum_{i=1}^n (x_i - avg(x)) * (y_i - avg(y))
-##--------------------------------------------------
-def cov (X, Y):
- assert len(X) == len(Y)
- n = len(X) # n=len(X)=len(Y)
- avg_X = avg(X)
- avg_Y = avg(Y)
- S_XY = 0.0
- for i in range(n):
- S_XY += (X[i] - avg_X) * (Y[i] - avg_Y)
- return (S_XY / n)
-
-##---------------------------------------------------------------------
-## variance : variance
-## param X data vector ( ..x_i.. )
-## (S_X)^2 = (Sum ( x_i - avg(x) )^2 ) / n
-##---------------------------------------------------------------------
-def variance (X):
- n = len(X)
- avg_X = avg (X)
- S_X2 = 0.0
- for i in range(n):
- S_X2 += (X[i] - avg_X) ** 2
- return (S_X2 / n)
-
-##---------------------------------------------------------------------
-## calibrate : output correction factors, c_lat on latency, c_bw on bw
-## such that bandwidth * c_bw = bw_regr, latency * c_lat = lat_regr
-## where bw_regr and lat_regr are the values approximating experimental
-## observations.
-##
-## param links number of links traversed during ping-pong
-## param latency as specified on command line, in s
-## param bandwidth as specified on command line, in Byte/s
-## param sizes vector of data sizes, in Bytes
-## param timings vector of time taken: timings[i] for sizes[i], in us
-##---------------------------------------------------------------------
-def calibrate (links, latency, bandwidth, sizes, timings):
- assert len(sizes) == len(timings)
- if len(sizes) < 2:
- return None
- # compute linear regression : find an affine form time = a*size+b
- S_XY = cov(sizes, timings)
- S_X2 = variance(sizes)
- a = S_XY / S_X2
- b = avg(timings) - a * avg(sizes)
- # corresponding bandwith, in byte/s (was in byte/us in skampi dat)
- bw_regr = 1e6 / a
- # corresponding latency, in s (was in us in skampi dat)
- lat_regr = b*1e-6
- print("\nregression: {0} * x + {1}".format(a,b))
- print("corr_bw = bw_regr/bandwidth= {0}/{1}={2} lat_regr/(lat_xml*links)={3}/({4}*{5}))".format(bw_regr,bandwidth,bw_regr/bandwidth,lat_regr,latency,links))
- # return linear regression result and corresponding correction factors c_bw,c_lat
- return a,b, bw_regr/bandwidth, lat_regr/(latency*links)
-
-
-##---------------------------------------------------------------------
-## outputs a C formatted conditional return value for factor
-##
-## param lb lower bound
-## param ub upper bound
-## param lb_included boolean to tell if bound is included (<=) or exclude (<)
-## param ub_included boolean to tell if bound is included (<=) or exclude (<)
-##---------------------------------------------------------------------
-def c_code_print (lb,ub, retval, lb_included, ub_included):
- lb_cmp = ub_cmp = "<"
- if lb_included:
- lb_cmp ="<="
- if ub_included:
- ub_cmp ="<="
-
- ub_kib=ub/1024.
- lb_kib=lb/1024.
- print("\t /* case {0:.1f} KiB {1} size {2} {3:.1f} KiB */".format(lb_kib,lb_cmp,ub_cmp,ub_kib))
- print("\t if ({0:d} {1} size && size {2} {3:d}) ".format(lb,lb_cmp,ub_cmp,ub))
- print("\t return({0});" . format(retval))
-
-
-##-----------------------------------------------------------------------------------------------
-## main
-##-----------------------------------------------------------------------------------------------
-links = int(sys.argv[2])
-latency = float(sys.argv[3])
-bandwidth = float(sys.argv[4])
-skampidat = open(sys.argv[1], "r")
-
-## read data from skampi logs.
-timings = []
-sizes = []
-readdata =[]
-for line in skampidat:
- l = line.split();
- if line[0] != '#' and len(l) >= 3: # is it a comment ?
- ## expected format
- ## ---------------
- #count= 8388608 8388608 144916.1 7.6 32 144916.1 143262.0
- #("%s %d %d %f %f %d %f %f\n" % (countlbl, count, countn, time, stddev, iter, mini, maxi)
- readdata.append( (int(l[1]),float(l[3]) / 2) ); # divide by 2 because of ping-pong measured
-
-## These may not be sorted so sort it by message size before processing.
-sorteddata = sorted( readdata, key=lambda pair: pair[0])
-sizes,timings= zip(*sorteddata)
-
-
-## adds message sizes of interest: if values are specified starting from the 6th command line arg
-## and these values are found as message sizes in te log file, add it to the limits list.
-## Each of these value si considered a potential inflexion point between two segments.
-##
-## If no value specified, a single segment is considered from 1st to last message size logged.
-limits = []
-if len(sys.argv) > 5:
- for i in range(5, len(sys.argv)):
- limits += [idx for idx in range(len(sizes)) if sizes[idx] == int(sys.argv[i])]
-limits.append(len(sizes) - 1)
-
-factors = []
-low = 0
-for lim in limits:
- correc = calibrate(links, latency, bandwidth, sizes[low:lim + 1], timings[low:lim + 1])
- if correc:
- # save interval [lb,ub] correction, regression line direction and origin
- # and corresponding correction factors for bw and lat resp.
- (dircoef,origin,factor_bw,factor_lat) = correc
- factors.append( (sizes[low],sizes[lim], dircoef, origin, factor_bw,factor_lat) )
- print("Segment [%d:%d] --Bandwidth factor=%g --Latency factor=%g " % (sizes[low], sizes[lim], factor_bw,factor_lat))
- low = lim + 1
-
-# now computes joining lines between segments
-joinseg=[]
-
-print("\n/**\n *------------------ <copy/paste C code snippet in surf/network.c> ----------------------")
-print(" *\n * produced by: {0}\n *".format(' '.join(sys.argv)))
-print(" *---------------------------------------------------------------------------------------\n **/")
-
-# print correction factor for bandwidth for each segment
-print("static double smpi_bandwidth_factor(double size)\n{")
-for (lb,ub,a,b,factor_bw,factor_lat) in factors:
- c_code_print(lb,ub,factor_bw,True,True)
-
- # save ends and starts of segments
- if lb != sizes[0]:
- joinseg.append( (lb,timings[sizes.index(lb)]) )
- if ub != sizes[-1]:
- joinseg.append( (ub,timings[sizes.index(ub)]) )
-
-# print correction factor for bandwidth between segments
-joinseg.reverse()
-print("\n\t /* ..:: inter-segment corrections ::.. */");
-inx=len(joinseg)-1
-while inx>=1:
- (x0,y0) = joinseg[inx]
- inx = inx -1
- (x1,y1) = joinseg[inx]
- inx = inx -1
- # line eq. is y = (y1-y0)/(x0-x1) * x + (y0 x1 - y1 x0)/(x1-x0)
- a = (y1-y0) / (x1-x0)
- bw_join = 1e6 / a
- factor_join_bw = bw_join / bandwidth
- #print("Joining points (%f,%f) -> (%f,%f) : line dir : a=%g\n" % (x0,y0,x1,y1,a))
- c_code_print(x0,x1,factor_join_bw,False,False)
-
-print("}\n")
-
-# print correction factor for latency for each segment
-print("static double smpi_latency_factor(double size)\n{")
-for (lb,ub,a,b,factor_bw,factor_lat) in factors:
- c_code_print(lb,ub,factor_lat,True,True)
-
-print("\n\t /* ..:: inter-segment corrections ::.. */");
-while joinseg:
- (x0,y0) = joinseg.pop()
- (x1,y1) = joinseg.pop()
- # line eq. is y = (y0-y1)/(x0-x1) * x + (y0 x1 - y1 x0)/(x1-x0)
- #print("(%f,%f) -> (%f,%f)\n" % (x0,y0,x1,y1))
- b = 1e-6 * (y0*x1-y1*x0) / (x1-x0)
- factor_join_lat = b / (latency*links)
- c_code_print(x0,x1,factor_join_lat,False,False)
-
-print("}\n")
-
-print("\n/**\n *------------------ <copy/paste C code snippet in surf/network.c> ----------------------\n **/")
+++ /dev/null
-# SkaMPI pt2pt test
-# on cluster : griffon (nancy) Grid5000. See Grid5000 website for technical data.
-
-#vendor_id : GenuineIntel
-#cpu family : 6
-#model : 23
-#model name : Intel(R) Xeon(R) CPU L5420 @ 2.50GHz
-#stepping : 10
-#cpu MHz : 2493.757
-#cache size : 6144 KB
-
-
-# /home/sgenaud/openmpi.install/bin/mpiexec --mca btl_tcp_if_include eth0 --mca btl_tcp_if_exclude ib0 --mca btl tcp,self -mca orte_base_help_aggregate 0 -mca plm_rsh_agent oarsh -machinefile machinefile -n 2 skampi -i ski_smpi/skampi_pt2pt.ski
-
-# Finished at Fri Jul 9 16:19:24 2010
-# SKaMPI Version 5.0.4 rev. 355
-# Started at Fri Jul 9 16:19:02 2010
-# Total runtime 22 seconds
-# begin result "Pingpong_Send_Recv"
-# duration = 22.49 sec
-# end result "Pingpong_Send_Recv"
-
-count= 1 1 109.2 0.2 32 106.7 107.7
-count= 2 2 109.3 0.3 32 107.6 107.0
-count= 4 4 111.7 0.7 32 107.8 109.7
-count= 8 8 109.0 0.3 32 106.8 107.0
-count= 16 16 109.1 0.4 32 108.0 106.7
-count= 32 32 111.1 0.4 32 110.5 107.4
-count= 64 64 110.2 0.2 32 107.8 108.6
-count= 128 128 118.9 1.2 32 109.9 116.2
-count= 256 256 145.8 0.4 32 138.4 144.9
-count= 512 512 161.7 0.3 32 154.7 161.6
-count= 1024 1024 190.4 0.2 32 182.7 190.4
-count= 1024 1024 193.4 0.3 32 185.9 193.0
-count= 1536 1536 237.3 0.5 32 226.9 237.3
-count= 2048 2048 248.0 0.8 32 238.4 247.9
-count= 2048 2048 249.2 1.0 32 240.1 247.7
-count= 2560 2560 262.9 0.6 32 251.7 262.9
-count= 3072 3072 277.9 0.4 32 272.7 275.0
-count= 3584 3584 285.0 0.5 32 275.0 285.0
-count= 4096 4096 303.0 0.9 32 293.5 298.9
-count= 4096 4096 303.4 0.8 32 297.2 301.6
-count= 4608 4608 310.0 0.8 32 310.0 291.7
-count= 5120 5120 321.4 0.7 32 321.4 291.5
-count= 5632 5632 343.7 0.7 32 343.7 312.0
-count= 6144 6144 320.7 0.5 32 320.7 282.6
-count= 6656 6656 339.4 0.4 32 339.4 296.6
-count= 7168 7168 353.3 0.7 32 353.3 308.5
-count= 7680 7680 386.7 0.8 32 385.3 378.8
-count= 8192 8192 389.1 0.6 32 389.1 373.8
-count= 8192 8192 393.7 0.8 32 391.1 380.4
-count= 16384 16384 627.7 0.7 32 627.7 533.9
-count= 32768 32768 1060.2 0.9 32 1060.2 828.9
-count= 32768 32768 1062.0 1.0 32 1062.0 836.4
-count= 33792 33792 1096.1 0.8 32 1096.1 838.2
-count= 34816 34816 1109.6 0.7 32 1109.6 835.3
-count= 35840 35840 1125.7 1.7 32 1125.7 856.3
-count= 36864 36864 1168.1 1.4 32 1168.1 930.2
-count= 37888 37888 1208.8 0.6 32 1208.8 937.2
-count= 38912 38912 1229.7 1.0 32 1229.7 952.2
-count= 39936 39936 1255.4 1.2 32 1255.4 969.3
-count= 40960 40960 1292.8 0.9 32 1292.8 978.8
-count= 41984 41984 1300.8 1.6 32 1300.8 976.3
-count= 43008 43008 1322.0 1.2 32 1322.0 988.2
-count= 44032 44032 1365.8 1.0 32 1365.8 1064.5
-count= 45056 45056 1400.6 0.8 32 1400.6 1072.9
-count= 46080 46080 1426.9 0.9 32 1426.9 1096.7
-count= 47104 47104 1445.7 0.9 32 1445.7 1101.4
-count= 48128 48128 1489.7 0.9 32 1489.7 1119.3
-count= 49152 49152 1516.2 0.6 32 1516.2 1137.3
-count= 50176 50176 1515.0 0.9 32 1515.0 1124.5
-count= 51200 51200 1558.4 1.4 32 1558.4 1202.6
-count= 52224 52224 1595.4 0.8 32 1595.4 1209.2
-count= 53248 53248 1614.3 1.0 32 1614.3 1219.8
-count= 54272 54272 1647.7 0.8 32 1647.7 1244.3
-count= 55296 55296 1684.2 0.9 32 1684.2 1258.9
-count= 56320 56320 1714.6 0.8 32 1714.6 1281.8
-count= 57344 57344 1710.3 1.4 32 1710.3 1262.5
-count= 58368 58368 1757.1 1.1 32 1757.1 1344.3
-count= 59392 59392 1793.4 1.3 32 1793.4 1350.9
-count= 60416 60416 1817.8 0.7 32 1817.8 1366.0
-count= 61440 61440 1850.5 1.5 32 1850.5 1392.2
-count= 62464 62464 1884.3 1.3 32 1884.3 1398.8
-count= 63488 63488 1896.7 1.2 32 1896.7 1403.7
-count= 64512 64512 1907.1 1.0 32 1907.1 1405.3
-count= 65536 65536 2250.0 1.2 32 2250.0 1648.1
-count= 65536 65536 2253.6 1.8 32 2253.6 1653.3
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-count= 68608 68608 2308.5 1.7 32 2308.5 1685.1
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-count= 73728 73728 2441.9 1.7 32 2441.9 1783.1
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-count= 76800 76800 2490.0 2.1 32 2490.0 1808.3
-count= 77824 77824 2503.4 2.8 32 2503.4 1815.1
-count= 78848 78848 2568.6 2.6 32 2568.6 1864.5
-count= 79872 79872 2618.4 2.2 32 2618.4 1897.0
-count= 80896 80896 2615.7 2.6 32 2615.7 1891.0
-count= 81920 81920 2641.5 2.1 32 2641.5 1919.9
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-count= 83968 83968 2662.6 1.8 32 2662.6 1929.4
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-count= 87040 87040 2784.9 2.0 32 2784.9 2013.4
-count= 88064 88064 2792.3 1.9 32 2792.3 2014.1
-count= 89088 89088 2820.0 1.8 32 2820.0 2032.9
-count= 90112 90112 2833.6 1.7 32 2833.6 2045.1
-count= 91136 91136 2861.4 1.1 32 2861.4 2056.6
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-count= 113664 113664 3401.1 1.4 32 3401.1 2430.6
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-count= 118784 118784 3544.9 1.9 32 3544.9 2527.8
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-count= 120832 120832 3584.4 2.2 32 3584.4 2554.0
-count= 121856 121856 3641.5 2.3 32 3641.5 2593.2
-count= 122880 122880 3662.0 1.7 32 3662.0 2608.6
-count= 123904 123904 3695.2 1.7 32 3695.2 2638.5
-count= 124928 124928 3711.6 1.6 32 3711.6 2643.5
-count= 125952 125952 3737.9 1.3 32 3737.9 2659.9
-count= 126976 126976 3750.0 1.8 32 3750.0 2673.3
-count= 128000 128000 3761.6 2.2 32 3761.6 2678.8
-count= 129024 129024 3822.9 1.8 32 3822.9 2725.9
-count= 130048 130048 3838.5 2.3 32 3838.5 2727.5
-count= 131072 131072 3870.2 1.8 32 3870.2 2750.0
-count= 131072 131072 3873.2 1.8 32 3873.2 2757.4
-count= 262144 262144 7121.5 1.0 32 7121.5 4980.9
-count= 524288 524288 10740.7 4.1 32 10740.7 9458.5
-count= 1048576 1048576 19872.8 4.9 32 19872.8 18373.7
-count= 2097152 2097152 37947.8 63.3 32 37947.8 36227.3
-count= 4194304 4194304 73299.4 7.8 32 73299.4 71898.5
-count= 8388608 8388608 144965.0 15.2 32 144965.0 143205.7
+++ /dev/null
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+++ /dev/null
-#!/usr/bin/env python
-
-# Copyright (c) 2010-2011, 2014. The SimGrid Team.
-# All rights reserved.
-
-# This program is free software; you can redistribute it and/or modify it
-# under the terms of the license (GNU LGPL) which comes with this package.
-
-#---------------------------------------------------------------------------------------------------
-# Example invokation:
-# % ./regress.py griffon_skampi_pt2pt.ski.dat 65536 120832
-#
-#
-# Given two vectors of same length n: message size S(.. s_i ..), and communication time T( .. t_i .. )
-# where t_i is the time associated to a mesage size s_i, computes the segmentation of the vectors
-# in 3 segments such that linear regressions on the 3 segments maximize correlation.
-# The metric for correlation is now the cumulative, log error of the estimated time value given
-# by regression, to the real time value.
-#
-# Call r(X[i:j],Y[i:j]) = ax + b the regression line for data of X,Y between indices i and j
-# Call E[i:j] = E( e_i, .., e_j) the vector of estimates from the regression, where e_i = a*S[i] + b
-# Call mean_logerr( T,T' ) the average log error of paired elements t_i and t'_i, of vectors len n T and T' resp.
-# i.e mean_logerr( T,T' ) = 1/n * Sum_i^n ( exp(| ln(t_i)-ln(t'_i)|) - 1 )
-#
-# The script computes indices k and l, s.t.
-# mean_logerr( r(S[0:k],T[0:k]) , E[0,k] ) +
-# mean_logerr( r(S[k:l],T[k:l]) , E[k,l] ) +
-# mean_logerr( r(S[l:n],T[l:n]) , E[l,n] )
-# is minimum.
-#---------------------------------------------------------------------------------------------------
-
-import sys
-from math import sqrt,log,exp
-
-
-if len(sys.argv) != 2 and len(sys.argv) != 4:
- print("Usage : {} datafile".format(sys.argv[0]))
- print("or : {0} datafile p1 p2".format(sys.argv[0]))
- print("where : p1 < p2 belongs to sizes in datafiles")
- sys.exit(-1)
-
-if len(sys.argv) == 4:
- p1=int(sys.argv[2])
- p2=int(sys.argv[3])
-
-##-----------------------------------------
-## avg : return average of a list of values
-## param l list of values
-##-----------------------------------------
-def avg (l):
- sum=0
- for e in l:
- sum=sum+e;
- return sum/len(l)
-
-##-------------------------------------------------
-## cov : covariance
-## param X first data vector (..x_i..)
-## param Y second data vector (..y_i..)
-## = 1/n \Sum_{i=1}^n (x_i - avg(x)) * (y_i - avg(y))
-##--------------------------------------------------
-def cov(X,Y):
- assert(len(X)==len(Y))
- n=len(X) # n=len(X)=len(Y)
- avg_X = avg( X )
- avg_Y = avg( Y )
- S_XY=0
- for i in range(n):
- S_XY = S_XY + ((X[i]-avg_X)*(Y[i]-avg_Y))
-
- return (S_XY/n)
-
-
-##----------------------------------
-## variance : variance
-## param X data vector ( ..x_i.. )
-## (S_X)^2 = (Sum ( x_i - avg(X) )^2 ) / n
-##----------------------------------
-def variance( X ):
- S_X2 = 0
- n = len( X )
- avg_X = avg ( X )
- for i in range(n):
- S_X2 = S_X2 + ((X[i] - avg_X)**2)
-
- return (S_X2/n)
-
-##----------------------------------
-## mean log_error
-## param X data vector ( ..x_i.. ), length n
-## param Y data vector ( ..y_i.. ), length n
-## return mean( 1/n * Sum_i^n ( exp(| ln(x_i)-ln(y_i)|) - 1 )
-##----------------------------------
-def mean_logerr( X,Y ):
- assert( len(X) == len(Y) )
- E = list(); # the list of errors
- for i in range(len(X)):
- E.append( exp(abs(log(X[i])-log(Y[i])))-1 )
- return (avg( E ))
-
-
-##-----------------------------------------------------------------------------------------------
-## correl_split_weighted_logerr : compute regression on each segment and
-## return the weigthed sum of correlation coefficients
-## param X first data vector (..x_i..)
-## param Y second data vector (..x_i..)
-## param segments list of pairs (i,j) where i refers to the ith value in X, and jth value in X
-## return (C,[(i1,j1,X[i1],X[j1]), (i2,j2,X[i2],X[j2]), ....]
-## where i1,j1 is the first segment, c1 the correlation coef on this segment, n1 the number of values
-## i2,j2 is the second segment, c2 the correlation coef on this segment, n2 the number of values
-## ...
-## and C=c1/n1+c2/n2+...
-##-----------------------------------------------------------------------------------------------
-def correl_split_weighted_logerr( X , Y , segments ):
- # expects segments = [(0,i1-1),(i1-1,i2-1),(i2,len-1)]
- correl = list()
- interv = list() # regr. line coeffs and range
- glob_err=0
- for (start,stop) in segments:
- #if start==stop :
- # return 0
- S_XY= cov( X [start:stop+1], Y [start:stop+1] )
- S_X2 = variance( X [start:stop+1] )
- a = S_XY/S_X2 # regr line coeffs
- b = avg ( Y[start:stop+1] ) - a * avg( X[start:stop+1] )
- # fill a vector (Z) with predicted values from regression
- Z=list()
- for i in range(start,stop+1):
- Z.append( a * X[i] + b )
- # compare real values and computed values
- e = mean_logerr( Y[start:stop+1] , Z )
- correl.append( (e, stop-start+1) ); # store correl. coef + number of values (segment length)
- interv.append( (a,b, X[start],X[stop],e) );
-
- for (e,l) in correl:
- glob_err = glob_err + (e*l/len( X )) # the average log err for this segment (e) is
- # weighted by the number of values of the segment (l) out of the total number of values
-
- #print("-> glob_corr={}\n".format(glob_corr))
- return (glob_err,interv);
-
-##-----------------------------------------------------------------------------------------------
-## correl_split_weighted : compute regression on each segment and
-## return the weigthed sum of correlation coefficients
-## param X first data vector (..x_i..)
-## param Y second data vector (..x_i..)
-## param segments list of pairs (i,j) where i refers to the ith value in X, and jth value in X
-## return (C,[(i1,j1,X[i1],X[j1]), (i2,j2,X[i2],X[j2]), ....]
-## where i1,j1 is the first segment, c1 the correlation coef on this segment, n1 the number of values
-## i2,j2 is the second segment, c2 the correlation coef on this segment, n2 the number of values
-## ...
-## and C=c1/n1+c2/n2+...
-##-----------------------------------------------------------------------------------------------
-def correl_split_weighted( X , Y , segments ):
- # expects segments = [(0,i1-1),(i1-1,i2-1),(i2,len-1)]
- correl = list();
- interv = list(); # regr. line coeffs and range
- glob_corr=0
- sum_nb_val=0
- for (start,stop) in segments:
- sum_nb_val = sum_nb_val + stop - start;
- #if start==stop :
- # return 0
- S_XY= cov( X [start:stop+1], Y [start:stop+1] )
- S_X2 = variance( X [start:stop+1] )
- S_Y2 = variance( Y [start:stop+1] ) # to compute correlation
- if S_X2*S_Y2 == 0:
- return (0,[])
- c = S_XY/(sqrt(S_X2)*sqrt(S_Y2))
- a = S_XY/S_X2 # regr line coeffs
- b= avg ( Y[start:stop+1] ) - a * avg( X[start:stop+1] )
- #print(" range [%d,%d] corr=%f, coeff det=%f [a=%f, b=%f]" % (X[start],X[stop],c,c**2,a, b))
- correl.append( (c, stop-start) ); # store correl. coef + number of values (segment length)
- interv.append( (a,b, X[start],X[stop]) );
-
- for (c,l) in correl:
- glob_corr = glob_corr + (l/sum_nb_val)*c # weighted product of correlation
- #print('-- %f * %f' % (c,l/sum_nb_val))
-
- #print("-> glob_corr={}\n".format(glob_corr))
- return (glob_corr,interv);
-
-
-
-
-##-----------------------------------------------------------------------------------------------
-## correl_split : compute regression on each segment and
-## return the product of correlation coefficient
-## param X first data vector (..x_i..)
-## param Y second data vector (..x_i..)
-## param segments list of pairs (i,j) where i refers to the ith value in X, and jth value in X
-## return (C,[(i1,j1,X[i1],X[j1]), (i2,j2,X[i2],X[j2]), ....]
-## where i1,j1 is the first segment, c1 the correlation coef on this segment,
-## i2,j2 is the second segment, c2 the correlation coef on this segment,
-## ...
-## and C=c1*c2*...
-##-----------------------------------------------------------------------------------------------
-def correl_split( X , Y , segments ):
- # expects segments = [(0,i1-1),(i1-1,i2-1),(i2,len-1)]
- correl = list();
- interv = list(); # regr. line coeffs and range
- glob_corr=1
- for (start,stop) in segments:
- #if start==stop :
- # return 0
- S_XY= cov( X [start:stop+1], Y [start:stop+1] )
- S_X2 = variance( X [start:stop+1] )
- S_Y2 = variance( Y [start:stop+1] ) # to compute correlation
- if S_X2*S_Y2 == 0:
- return (0,[])
- c = S_XY/(sqrt(S_X2)*sqrt(S_Y2))
- a = S_XY/S_X2 # regr line coeffs
- b= avg ( Y[start:stop+1] ) - a * avg( X[start:stop+1] )
- #print(" range [%d,%d] corr=%f, coeff det=%f [a=%f, b=%f]" % (X[start],X[stop],c,c**2,a, b))
- correl.append( (c, stop-start) ); # store correl. coef + number of values (segment length)
- interv.append( (a,b, X[start],X[stop]) );
-
- for (c,l) in correl:
- glob_corr = glob_corr * c # product of correlation coeffs
- return (glob_corr,interv);
-
-
-
-##-----------------------------------------------------------------------------------------------
-## main
-##-----------------------------------------------------------------------------------------------
-sum=0
-nblines=0
-skampidat = open(sys.argv[1], "r")
-
-
-## read data from skampi logs.
-timings = []
-sizes = []
-readdata =[]
-for line in skampidat:
- l = line.split();
- if line[0] != '#' and len(l) >= 3: # is it a comment ?
- ## expected format
- ## ---------------
- #count= 8388608 8388608 144916.1 7.6 32 144916.1 143262.0
- #("%s %d %d %f %f %d %f %f\n" % (countlbl, count, countn, time, stddev, iter, mini, maxi)
- readdata.append( (int(l[1]),float(l[3])) );
- nblines=nblines+1
-
-## These may not be sorted so sort it by message size before processing.
-sorteddata = sorted( readdata, key=lambda pair: pair[0])
-sizes,timings = zip(*sorteddata);
-
-# zip makes tuples; cast to lists for backward compatibility with python2.X
-sizes = list(sizes)
-timings = list(timings)
-
-##----------------------- search for best break points-----------------
-## example
-## p1=2048 -> p1inx=11 delta=3 -> [8;14]
-## 8 : segments[(0,7),(8,13),(13,..)]
-## ....
-## p2=65536 -> p2inx=16 delta=3 -> [13;19]
-
-if len(sys.argv) == 4:
-
- p1inx = sizes.index( p1 );
- p2inx = sizes.index( p2 );
- max_glob_corr = 999990;
- max_p1inx = p1inx
- max_p2inx = p2inx
-
- ## tweak parameters here to extend/reduce search
- search_p1 = 70 # number of values to search +/- around p1
- search_p2 = 70 # number of values to search +/- around p2
- min_seg_size = 3
- if (search_p2 + min_seg_size > len(sizes)): # reduce min segment sizes when points close to data extrema
- min_seg_size = len(sizes)-search_p2
- if (search_p1 - min_seg_size < 0):
- min_seg_size = search_p1
-
- lb1 = max( 1, p1inx-search_p1 )
- ub1 = min( p1inx+search_p1, p2inx);
- lb2 = max( p1inx, p2inx-search_p2) # breakpoint +/- delta
- ub2 = min( p2inx+search_p2, len(sizes)-1);
-
- print("** evaluating over \n");
- print("interv1:\t %d <--- %d ---> %d" % (sizes[lb1],p1,sizes[ub1]))
- print("rank: \t (%d)<---(%d)--->(%d)\n" % (lb1,p1inx,ub1))
- print("interv2:\t\t %d <--- %d ---> %d" % (sizes[lb2],p2,sizes[ub2]))
- print("rank: \t\t(%d)<---(%d)--->(%d)\n" % (lb2,p2inx,ub2))
-
- result = list()
- for i in range(lb1,ub1+1):
- for j in range(lb2,ub2+1):
- if i<j: # segments must not overlap
- if i+1 >=min_seg_size and j-i+1 >= min_seg_size and len(sizes)-1-j >= min_seg_size : # not too small segments
- #print("** i=%d,j=%d" % (i,j))
- segments = [(0,i),(i,j),(j,len(sizes)-1)]
- result.append( correl_split_weighted_logerr( sizes, timings, segments) ) # add pair (metric,interval)
-
- # sort results on ascending metric: ok for logerr. Add "reverse=true" for desc sort if you use a correlation metric
- result = sorted( result, key=lambda pair: pair[0])
-
-
- top_n_sol=5; # tweak to display top best n solution
-
- print("#-------------------- result summary ---------------------------------------------------------------------\n");
-
- for k in range(top_n_sol):
- (err,interval) = result[k]
- print(k)
- print("\n RANK {0}\n-------".format(k))
- print("** overall metric = {0}".format(err))
- for (a,b,i,j,e) in interval:
- print("** OPT: [{0} .. {1}] segment_metric={2} slope: {3} x + {4}".format(i,j,e,a,b))
-
-
- print("\n\n\n")
-
- print("#-------------------- Best Solution : cut here the gnuplot code -----------------------------------------------------------\n");
- preamble='set output "regr.eps"\n\
-set terminal postscript eps color\n\
-set key left\n\
-set xlabel "Each message size in bytes"\n\
-set ylabel "Time in us"\n\
-set logscale x\n\
-set logscale y\n\
-set grid'
-
- print(preamble);
- print('plot "%s" u 3:4:($5) with errorbars title "skampi traces %s",\\' % (sys.argv[1],sys.argv[1]));
- (err,interval) = result[0]
- for (a,b,i,j,e) in interval:
- print('"%s" u (%d<=$3 && $3<=%d? $3:0/0):(%f*($3)+%f) w linespoints title "regress. %s-%s bytes",\\' % (sys.argv[1],i,j,a,b,i,j))
-
- print("#-------------------- /cut here the gnuplot code -----------------------------------------------------------\n");
-
-
-else:
- print('\n** Linear regression on %d values **\n' % (nblines))
- print('\n sizes=',sizes,'\n\n')
- avg_sizes = avg( sizes )
- avg_timings = avg( timings )
- print("avg_timings=%f, avg_sizes=%f, nblines=%d\n" % (avg_timings,avg_sizes,nblines))
-
- S_XY= cov( sizes, timings )
- S_X2 = variance( sizes )
- S_Y2 = variance( timings ) # to compute correlation
-
- a = S_XY/S_X2
- correl = S_XY/(sqrt(S_X2)*sqrt(S_Y2)) # corealation coeff (Bravais-Pearson)
-
-
- b= avg_timings - a * avg_sizes
- print("[S_XY=%f, S_X2=%f]\n[correlation=%f, coeff det=%f]\n[a=%f, b=%f]\n" % (S_XY, S_X2, correl,correl**2,a, b))
-
-
+++ /dev/null
-#!/usr/bin/env python
-
-# Copyright (c) 2011, 2014. The SimGrid Team.
-# All rights reserved.
-
-# This program is free software; you can redistribute it and/or modify it
-# under the terms of the license (GNU LGPL) which comes with this package.
-
-# This script takes the following command line parameters
-# 1) an input file containing 2 columns: message size and 1-way trip time
-# 2) the maximum relative error for a line segment
-# 3) the minimum number of points needed to justify adding a line segment
-# 4) the number of links
-# 5) the latency
-# 6) the bandwidth
-
-import sys
-
-def compute_regression(points):
- N = len(points)
-
- if N < 1:
- return None
-
- if N < 2:
- return (0, points[0][1])
-
- Sx = Sy = Sxx = Syy = Sxy = 0.0
-
- for x, y in points:
- Sx += x
- Sy += y
- Sxx += x*x
- Syy += y*y
- Sxy += x*y
- denom = Sxx * N - Sx * Sx
- # don't return 0 or negative values as a matter of principle...
- m = max(sys.float_info.min, (Sxy * N - Sy * Sx) / denom)
- b = max(sys.float_info.min, (Sxx * Sy - Sx * Sxy) / denom)
- return (m, b)
-
-def compute_error(m, b, x, y):
- yp = m*x+b
- return abs(yp - y) / max(min(yp, y), sys.float_info.min)
-
-def compute_max_error(m, b, points):
- max_error = 0.0
- for x, y in points:
- max_error = max(max_error, compute_error(m, b, x, y))
- return max_error
-
-def get_max_error_point(m, b, points):
- max_error_index = -1
- max_error = 0.0
-
- i = 0
- while i < len(points):
- x, y = points[i]
- error = compute_error(m, b, x, y)
- if error > max_error:
- max_error_index = i
- max_error = error
- i += 1
-
- return (max_error_index, max_error)
-
-infile_name = sys.argv[1]
-error_bound = float(sys.argv[2])
-min_seg_points = int(sys.argv[3])
-links = int(sys.argv[4])
-latency = float(sys.argv[5])
-bandwidth = float(sys.argv[6])
-
-infile = open(infile_name, 'r')
-
-# read datafile
-points = []
-for line in infile:
- fields = line.split()
- points.append((int(fields[0]), int(fields[1])))
-infile.close()
-
-# should sort points by x values
-points.sort()
-
-# break points up into segments
-pointsets = []
-lbi = 0
-while lbi < len(points):
- min_ubi = lbi
- max_ubi = len(points) - 1
- while max_ubi - min_ubi > 1:
- ubi = (min_ubi + max_ubi) / 2
- m, b = compute_regression(points[lbi:ubi+1])
- max_error = compute_max_error(m, b, points[lbi:ubi+1])
- if max_error > error_bound:
- max_ubi = ubi - 1
- else:
- min_ubi = ubi
- ubi = max_ubi
- if min_ubi < max_ubi:
- m, b = compute_regression(points[lbi:max_ubi+1])
- max_error = compute_max_error(m, b, points[lbi:max_ubi+1])
- if max_error > error_bound:
- ubi = min_ubi
- pointsets.append(points[lbi:ubi+1])
- lbi = ubi+1
-
-# try to merge larger segments if possible and compute piecewise regression
-i = 0
-segments = []
-notoutliers = 0
-while i < len(pointsets):
- currpointset = []
- j = i
- while j < len(pointsets):
- newpointset = currpointset + pointsets[j]
- # if joining a small segment, we can delete bad points
- if len(pointsets[j]) < min_seg_points:
- k = 0
- while k < len(pointsets[j]):
- m, b = compute_regression(newpointset)
- max_error_index, max_error = get_max_error_point(m, b, newpointset)
- if max_error <= error_bound:
- break
- del newpointset[max_error_index]
- k += 1
- # only add new pointset if we had to delete fewer than its length
- # points
- if k < len(pointsets[j]):
- i = j
- currpointset = newpointset
- # otherwise, we just see if it works...
- else:
- m, b = compute_regression(newpointset)
- max_error = compute_max_error(m, b, newpointset)
- if max_error > error_bound:
- break
- i = j
- currpointset = newpointset
- j += 1
- i += 1
- # outliers are ignored when constructing the piecewise funciton
- if len(currpointset) < min_seg_points:
- continue
- notoutliers += len(currpointset)
- m, b = compute_regression(currpointset)
- lb = min(x for x, y in currpointset)
- lat_factor = b / (1.0e6 * links * latency)
- bw_factor = 1.0e6 / (m * bandwidth)
- segments.append((lb, m, b, lat_factor, bw_factor))
-
-outliers = len(points) - notoutliers
-segments.sort()
-segments.reverse()
-
-print "/**--------- <copy/paste C code snippet in surf/network.c> -------------"
-print " * produced by:"
-print " *", " ".join(sys.argv)
-print " * outliers:", outliers
-print " * gnuplot: "
-print " plot \"%s\" using 1:2 with lines title \"data\", \\" % (infile_name)
-for lb, m, b, lat_factor, bw_factor in segments:
- print " (x >= %d) ? %g*x+%g : \\" % (lb, m, b)
-print " 1.0 with lines title \"piecewise function\""
-print " *-------------------------------------------------------------------*/"
-print
-print "static double smpi_bandwidth_factor(double size)\n{\n"
-for lb, m, b, lat_factor, bw_factor in segments:
- print " if (size >= %d) return %g;" % (lb, bw_factor)
-print " return 1.0;\n}\n"
-print "static double smpi_latency_factor(double size)\n{\n"
-for lb, m, b, lat_factor, bw_factor in segments:
- print " if (size >= %d) return %g;" % (lb, lat_factor)
-print " return 1.0;\n}\n"
-print "/**--------- <copy/paste C code snippet in surf/network.c> -----------*/"
+++ /dev/null
-/* Copyright (c) 2011, 2014. The SimGrid Team.
- * All rights reserved. */
-
-/* This program is free software; you can redistribute it and/or modify it
- * under the terms of the license (GNU LGPL) which comes with this package. */
-
-/**--------- <copy/paste C code snippet in surf/network.c> -------------
- * produced by:
- * ./regression2.py ./pingpong-in.dat 0.15 30 2 2.4e-5 1.25e8
- * outliers: 66
- * gnuplot:
- plot "./pingpong-in.dat" using 1:2 with lines title "data", \
- (x >= 65472) ? 0.00850436*x+558.894 : \
- (x >= 15424) ? 0.0114635*x+167.446 : \
- (x >= 9376) ? 0.0136219*x+124.464 : \
- (x >= 5776) ? 0.00735707*x+105.022 : \
- (x >= 3484) ? 0.0103235*x+90.2886 : \
- (x >= 1426) ? 0.0131384*x+77.3159 : \
- (x >= 732) ? 0.0233927*x+93.6146 : \
- (x >= 257) ? 0.0236608*x+93.7637 : \
- (x >= 127) ? 0.0246645*x+94.0447 : \
- (x >= 64) ? 0.037963*x+93.0877 : \
- (x >= 0) ? 2.22507e-308*x+98.0633 : \
- 1.0 with lines title "piecewise function"
- *-------------------------------------------------------------------*/
-
-static double smpi_bandwidth_factor(double size)
-{
-
- if (size >= 65472) return 0.940694;
- if (size >= 15424) return 0.697866;
- if (size >= 9376) return 0.58729;
- if (size >= 5776) return 1.08739;
- if (size >= 3484) return 0.77493;
- if (size >= 1426) return 0.608902;
- if (size >= 732) return 0.341987;
- if (size >= 257) return 0.338112;
- if (size >= 127) return 0.324353;
- if (size >= 64) return 0.210731;
- if (size >= 0) return 3.59539e+305;
- return 1.0;
-}
-
-static double smpi_latency_factor(double size)
-{
-
- if (size >= 65472) return 11.6436;
- if (size >= 15424) return 3.48845;
- if (size >= 9376) return 2.59299;
- if (size >= 5776) return 2.18796;
- if (size >= 3484) return 1.88101;
- if (size >= 1426) return 1.61075;
- if (size >= 732) return 1.9503;
- if (size >= 257) return 1.95341;
- if (size >= 127) return 1.95926;
- if (size >= 64) return 1.93933;
- if (size >= 0) return 2.04299;
- return 1.0;
-}
-
-/**--------- <copy/paste C code snippet in surf/network.c> -----------*/
