[1363] | 1 | #!/usr/bin/env python |
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| 2 | |
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| 3 | # Timing of MPI module for Python and estimation of latency and bandwidth |
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| 4 | # |
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| 5 | # Send numerical array in a ring from processor 0 to 1 etc back to 0 |
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| 6 | # Perform timings and compare different sending strategies |
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| 7 | # |
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| 8 | # OMN, OCT 2001 |
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| 9 | |
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| 10 | import time, sys, pypar, Numeric |
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| 11 | |
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| 12 | # The send/recv routines |
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| 13 | # |
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| 14 | import pypar |
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| 15 | |
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| 16 | #from mpiext import send_array, receive_array #most direct methods |
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| 17 | #Not available in final install - use bypass form for now |
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| 18 | |
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| 19 | #-------------------------------------------------------------- |
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| 20 | |
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| 21 | method = 2 # Use |
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| 22 | # 0: automatically allocated buffers |
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| 23 | # 1: user-supplied buffers |
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| 24 | # 2: Use bypass - let pypar use direct mpi_ext call |
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| 25 | # 3: direct call to mpiext (with buffers), |
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| 26 | # only fractionally better than bypass |
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| 27 | |
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| 28 | vanilla = False # Force use of vanilla format (1) |
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| 29 | consistency_check = False # Check correctness |
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| 30 | |
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| 31 | |
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| 32 | #-------------------------------------------------------------- |
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| 33 | # linfit |
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| 34 | # |
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| 35 | def linfit(x, y): |
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| 36 | """Fit a and b to the model y = ax + b. Return a,b,variance |
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| 37 | """ |
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| 38 | |
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| 39 | Sx = Sy = SSoN = SxoN = norm = varest = 0.0 |
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| 40 | N = len(x) |
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| 41 | assert len(y) == N, "x and y must have same length" |
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| 42 | |
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| 43 | for i in range(N): |
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| 44 | #print("x,y = %f, %f\n",x[i],y[i]) |
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| 45 | Sx = Sx + x[i] |
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| 46 | Sy = Sy + y[i] |
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| 47 | |
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| 48 | SxoN = Sx/N |
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| 49 | |
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| 50 | a = 0.0 |
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| 51 | for i in range(N): |
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| 52 | t = x[i] - SxoN |
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| 53 | SSoN = SSoN + t*t |
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| 54 | a = a + t*y[i] |
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| 55 | |
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| 56 | |
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| 57 | a = a/SSoN # a = (N Sxy - SxSy)/(NSxx - Sx^2) */ |
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| 58 | b = (Sy - Sx*a)/N |
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| 59 | |
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| 60 | # Quality - variance estimate \sum_i r_i^2 /(m-n) |
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| 61 | for i in range(N): |
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| 62 | norm = norm + float(x[i])*x[i] |
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| 63 | res = y[i] - a*x[i] - b |
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| 64 | varest = varest + res*res |
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| 65 | |
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| 66 | varest = varest/norm/(N-2) |
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| 67 | return a, b, varest |
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| 68 | |
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| 69 | |
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| 70 | |
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| 71 | |
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| 72 | #-------------------------------------------------------------- |
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| 73 | # Main program |
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| 74 | # |
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| 75 | MAXI = 10 # Number of blocks |
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| 76 | MAXM = 500000 # Largest block |
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| 77 | BLOCK = MAXM/MAXI # Block size |
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| 78 | |
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| 79 | repeats = 10 |
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| 80 | msgid = 0 |
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| 81 | vanilla = 0 #Select vanilla mode (slower but general) |
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| 82 | |
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| 83 | numprocs = pypar.size() |
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| 84 | myid = pypar.rank() |
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| 85 | processor_name = pypar.Get_processor_name() |
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| 86 | |
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| 87 | if myid == 0: |
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| 88 | # Main process - Create message, pass on, verify correctness and log timing |
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| 89 | # |
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| 90 | print "MAXM = %d, number of processors = %d" %(MAXM, numprocs) |
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| 91 | print "Measurements are repeated %d times for reliability" %repeats |
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| 92 | |
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| 93 | if numprocs < 2: |
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| 94 | print "Program needs at least two processors - aborting\n" |
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| 95 | pypar.Abort() |
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| 96 | |
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| 97 | pypar.Barrier() #Synchronize all before timing |
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| 98 | print "I am process %d on %s" %(myid,processor_name) |
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| 99 | |
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| 100 | |
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| 101 | #Initialise data and timings |
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| 102 | # |
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| 103 | from RandomArray import uniform, seed |
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| 104 | seed(17, 53) |
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| 105 | A = uniform(0.0,100.0,MAXM) |
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| 106 | elsize = A.itemsize() |
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| 107 | #print elsize |
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| 108 | |
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| 109 | noelem = [0]*MAXI |
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| 110 | bytes = [0]*MAXI |
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| 111 | avgtime = [0.0]*MAXI |
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| 112 | mintime = [ 1000000.0]*MAXI |
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| 113 | maxtime = [-1000000.0]*MAXI |
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| 114 | |
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| 115 | |
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| 116 | |
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| 117 | |
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| 118 | if myid == 0: |
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| 119 | # Determine timer overhead |
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| 120 | cpuOH = 1.0; |
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| 121 | for k in range(repeats): # Repeat to get reliable timings |
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| 122 | t1 = pypar.Wtime() |
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| 123 | t2 = pypar.Wtime() |
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| 124 | if t2-t1 < cpuOH: cpuOH = t2-t1 |
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| 125 | |
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| 126 | print "Timing overhead is %f seconds.\n" %cpuOH |
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| 127 | |
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| 128 | |
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| 129 | # Pass msg circularly |
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| 130 | for k in range(repeats): |
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| 131 | if myid == 0: |
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| 132 | print "Run %d of %d" %(k+1,repeats) |
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| 133 | |
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| 134 | for i in range(MAXI): |
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| 135 | m=BLOCK*i+1 |
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| 136 | |
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| 137 | noelem[i] = m |
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| 138 | |
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| 139 | pypar.Barrier() # Synchronize |
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| 140 | |
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| 141 | if myid == 0: |
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| 142 | # |
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| 143 | # Main process |
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| 144 | # |
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| 145 | t1 = pypar.Wtime() |
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| 146 | |
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| 147 | if method==0: |
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| 148 | pypar.send(A[:m], destination=1, tag=msgid, vanilla=vanilla) |
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| 149 | C = pypar.receive(numprocs-1, tag=msgid, vanilla=vanilla) |
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| 150 | elif method == 1: |
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| 151 | pypar.send(A[:m], use_buffer=True, destination=1, |
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| 152 | tag=msgid, vanilla=vanilla) |
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| 153 | C = pypar.receive(numprocs-1, buffer=A[:m], tag=msgid, vanilla=vanilla) |
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| 154 | elif method==2: |
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| 155 | pypar.send(A[:m], use_buffer=True, destination=1, |
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| 156 | tag=msgid, vanilla=vanilla, bypass=True) |
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| 157 | C = pypar.receive(numprocs-1, buffer=A[:m], tag=msgid, |
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| 158 | vanilla=vanilla, bypass=True) |
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| 159 | else: |
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| 160 | raise 'Unknown method' |
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| 161 | #send_array(A[:m], 1, msgid) |
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| 162 | #stat = receive_array(A[:m], numprocs-1, msgid) |
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| 163 | #C = A[:m] |
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| 164 | |
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| 165 | t2 = pypar.Wtime() - t1 - cpuOH |
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| 166 | t2 = t2/numprocs |
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| 167 | avgtime[i] = avgtime[i] + t2 |
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| 168 | if t2 < mintime[i]: mintime[i] = t2 |
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| 169 | if t2 > maxtime[i]: maxtime[i] = t2 |
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| 170 | |
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| 171 | # Uncomment to verify integrity of data |
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| 172 | # However, this may affect accuracy of timings for some reason. |
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| 173 | # |
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| 174 | if consistency_check: |
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| 175 | assert Numeric.alltrue(C == A[:m]) |
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| 176 | else: |
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| 177 | # |
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| 178 | # Parallel process - get msg and pass it on |
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| 179 | # |
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| 180 | |
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| 181 | if method==0: |
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| 182 | C = pypar.receive(myid-1, tag=msgid, vanilla=vanilla) |
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| 183 | pypar.send(A[:m], destination=(myid+1)%numprocs, |
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| 184 | tag=msgid, vanilla=vanilla) |
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| 185 | elif method==1: |
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| 186 | C = pypar.receive(myid-1, buffer=A[:m], tag=msgid, vanilla=vanilla) |
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| 187 | pypar.send(A[:m], use_buffer=True, destination=(myid+1)%numprocs, |
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| 188 | tag=msgid, vanilla=vanilla) |
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| 189 | elif method==2: |
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| 190 | # Use pypar bypass |
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| 191 | C = pypar.receive(myid-1, buffer=A[:m], tag=msgid, |
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| 192 | vanilla=vanilla, bypass=True) |
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| 193 | pypar.send(A[:m], use_buffer=True, destination=(myid+1)%numprocs, |
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| 194 | tag=msgid, vanilla=vanilla, bypass=True) |
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| 195 | else: |
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| 196 | raise 'Unknown method' |
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| 197 | # Use direct mpiext call |
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| 198 | #stat = receive_array(A[:m], myid-1, msgid) |
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| 199 | #send_array(A[:m], (myid+1)%numprocs, msgid) |
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| 200 | |
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| 201 | |
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| 202 | # Output stats |
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| 203 | # |
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| 204 | if myid == 0: |
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| 205 | print "Bytes transferred time (micro seconds)" |
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| 206 | print " min avg max " |
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| 207 | print "----------------------------------------------" |
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| 208 | |
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| 209 | for i in range(MAXI): |
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| 210 | avgtime[i] = avgtime[i]/repeats*1.0e6 #Average micro seconds |
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| 211 | mintime[i] = mintime[i]*1.0e6 #Min micro seconds |
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| 212 | maxtime[i] = maxtime[i]*1.0e6 #Min micro seconds |
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| 213 | |
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| 214 | m = noelem[i] |
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| 215 | bytes[i] = elsize*noelem[i] |
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| 216 | |
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| 217 | print "%10d %10d %10d %10d" %(bytes[i], mintime[i], avgtime[i], maxtime[i]) |
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| 218 | |
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| 219 | |
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| 220 | Tbw, Tlat, varest = linfit(bytes, mintime) |
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| 221 | print "\nLinear regression on best timings (t = t_l + t_b * bytes):\n", |
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| 222 | print " t_b = %f\n t_l = %f" %(Tbw,Tlat) |
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| 223 | print " Estimated relative variance = %.9f\n" %varest |
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| 224 | |
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| 225 | print "Estimated bandwith (1/t_b): %.3f Mb/s" %(1.0/Tbw) |
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| 226 | print "Estimated latency: %d micro s" %int(mintime[0]-bytes[0]*Tbw) |
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| 227 | |
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| 228 | |
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| 229 | pypar.Finalize() |
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| 230 | |
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| 231 | |
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| 232 | |
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| 233 | |
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| 234 | |
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| 235 | |
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