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jimmieha
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profilers added
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‎profile_results/pyecm.py.cProfile

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Factoring 10000000000000000000000000000000000000000000000001:
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11
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197
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909091
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5076141624365532994918781726395939035533
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21897 function calls in 0.432 CPU seconds
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Ordered by: standard name
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ncalls tottime percall cumtime percall filename:lineno(function)
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1 0.000 0.000 0.000 0.000 <string>:1(<module>)
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1 0.000 0.000 0.000 0.000 __future__.py:48(<module>)
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1 0.000 0.000 0.000 0.000 __future__.py:74(_Feature)
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7 0.000 0.000 0.000 0.000 __future__.py:75(__init__)
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1 0.000 0.000 0.000 0.000 os.py:743(urandom)
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1 0.002 0.002 0.003 0.003 pyecm.py:1034(get_points)
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3 0.000 0.000 0.013 0.004 pyecm.py:1075(isprime)
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3 0.000 0.000 0.240 0.080 pyecm.py:1137(ecm)
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5 0.007 0.001 0.380 0.076 pyecm.py:1235(factors)
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2 0.000 0.000 0.000 0.000 pyecm.py:1287(is_switch)
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8915 0.130 0.000 0.133 0.000 pyecm.py:131(next_prime)
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2 0.000 0.000 0.000 0.000 pyecm.py:1316(valid_input)
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1 0.000 0.000 0.380 0.380 pyecm.py:1357(command_line)
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1 0.000 0.000 0.380 0.380 pyecm.py:1435(main)
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1 0.015 0.015 0.400 0.400 pyecm.py:15(<module>)
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95 0.000 0.000 0.000 0.000 pyecm.py:182(mpz)
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3 0.001 0.000 0.001 0.000 pyecm.py:188(root)
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3 0.000 0.000 0.001 0.000 pyecm.py:205(sqrt)
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1 0.000 0.000 0.000 0.000 pyecm.py:235(ts)
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8 0.003 0.000 0.008 0.001 pyecm.py:325(add)
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4 0.000 0.000 0.000 0.000 pyecm.py:36(gcd)
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72 0.009 0.000 0.009 0.000 pyecm.py:373(atdn)
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1 0.000 0.000 0.000 0.000 pyecm.py:388(copy)
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3 0.000 0.000 0.003 0.001 pyecm.py:398(could_be_prime)
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208 0.095 0.000 0.204 0.001 pyecm.py:432(double)
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3 0.000 0.000 0.003 0.001 pyecm.py:455(fastprime)
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3 0.000 0.000 0.000 0.000 pyecm.py:519(inv_const)
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2 0.000 0.000 0.000 0.000 pyecm.py:525(naf)
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230 0.054 0.000 0.120 0.001 pyecm.py:539(parallel_invert)
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18 0.000 0.000 0.000 0.000 pyecm.py:558(prod)
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13 0.005 0.000 0.012 0.001 pyecm.py:754(subtract)
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230 0.066 0.000 0.066 0.000 pyecm.py:77(invert)
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1 0.000 0.000 0.000 0.000 pyecm.py:775(congrats)
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3 0.000 0.000 0.240 0.080 pyecm.py:786(sure_factors)
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1 0.000 0.000 0.000 0.000 pyecm.py:827(to_tuple)
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1 0.001 0.001 0.228 0.228 pyecm.py:837(mainloop)
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1 0.000 0.000 0.076 0.076 pyecm.py:994(fast_multiply)
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1 0.004 0.004 0.005 0.005 random.py:40(<module>)
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1 0.000 0.000 0.000 0.000 random.py:643(WichmannHill)
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1 0.000 0.000 0.000 0.000 random.py:71(Random)
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1 0.000 0.000 0.000 0.000 random.py:793(SystemRandom)
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1 0.000 0.000 0.000 0.000 random.py:90(__init__)
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1 0.000 0.000 0.000 0.000 random.py:99(seed)
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1 0.000 0.000 0.000 0.000 {binascii.hexlify}
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1 0.000 0.000 0.000 0.000 {eval}
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1 0.032 0.032 0.432 0.432 {execfile}
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1 0.000 0.000 0.000 0.000 {function seed at 0x100530938}
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404 0.000 0.000 0.000 0.000 {isinstance}
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1321 0.000 0.000 0.000 0.000 {len}
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1 0.000 0.000 0.000 0.000 {math.exp}
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9611 0.003 0.000 0.003 0.000 {math.log}
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3 0.000 0.000 0.000 0.000 {math.sqrt}
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20 0.000 0.000 0.000 0.000 {max}
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440 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
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1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
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1 0.000 0.000 0.000 0.000 {method 'replace' of 'str' objects}
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2 0.000 0.000 0.000 0.000 {min}
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1 0.000 0.000 0.000 0.000 {posix.close}
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1 0.000 0.000 0.000 0.000 {posix.open}
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1 0.000 0.000 0.000 0.000 {posix.read}
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231 0.000 0.000 0.000 0.000 {range}
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‎profile_results/pyecm.py.line_profiler

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Timer unit: 1e-06 s
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File: pyecm.py
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Function: invert at line 76
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Total time: 0.427487 s
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Line # Hits Time Per Hit % Time Line Contents
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==============================================================
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76 @profile
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77 def invert(a, b):
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78 '''Computes the inverse of a modulo b. b must be odd.
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79
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80 Returns the inverse of a (mod b).'''
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81 230 403 1.8 0.1 if a == 0 or b == 0:
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82 return 0
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83
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84 230 249 1.1 0.1 truth = False
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85 230 255 1.1 0.1 if a < 0:
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86 truth = True
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87 a = -a
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88
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89 230 212 0.9 0.0 b_orig = b
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90 230 211 0.9 0.0 alpha = 1
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91 230 222 1.0 0.1 beta = 0
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92
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93 424 569 1.3 0.1 while not a & 1:
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94 194 226 1.2 0.1 if alpha & 1:
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95 112 163 1.5 0.0 alpha += b_orig
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96 194 252 1.3 0.1 alpha >>= 1
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97 194 218 1.1 0.1 a >>= 1
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98
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99 230 248 1.1 0.1 if b > a:
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100 230 239 1.0 0.1 a, b = b, a
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101 230 241 1.0 0.1 alpha, beta = beta, alpha
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102
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103 24834 29747 1.2 7.0 while b != 0 and a != b:
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104 24604 26940 1.1 6.3 a -= b
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105 24604 26910 1.1 6.3 alpha -= beta
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106
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107 73584 86906 1.2 20.3 while not a & 1:
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108 48980 55889 1.1 13.1 if alpha & 1:
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109 25106 28201 1.1 6.6 alpha += b_orig
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110 48980 59761 1.2 14.0 alpha >>= 1
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111 48980 55045 1.1 12.9 a >>= 1
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112
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113 24604 25288 1.0 5.9 if b > a:
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114 13156 13600 1.0 3.2 a,b = b,a
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115 13156 13544 1.0 3.2 alpha, beta = beta, alpha
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116
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117 230 235 1.0 0.1 if a == b:
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118 230 261 1.1 0.1 a -= b
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119 230 258 1.1 0.1 alpha -= beta
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120 230 235 1.0 0.1 a, b = b, a
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121 230 245 1.1 0.1 alpha, beta = beta, alpha
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122
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123 230 268 1.2 0.1 if a != 1:
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124 1 1 1.0 0.0 return 0
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125
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126 229 227 1.0 0.1 if truth:
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127 alpha = b_orig - alpha
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128
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129 229 218 1.0 0.1 return alpha
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File: pyecm.py
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Function: double at line 431
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Total time: 1.27076 s
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Line # Hits Time Per Hit % Time Line Contents
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==============================================================
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431 @profile
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432 def double(p, n):
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433 '''Doubles each point in the input list. Much like the add function, we take advantage of fast inversion.
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434
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435 Returns the doubled list.'''
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436 208 922 4.4 0.1 inv = range(len(p))
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437
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438 19136 19680 1.0 1.5 for i in xrange(len(p)):
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439 18928 26903 1.4 2.1 inv[i] = p[i][1] << 1
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440
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441 208 998041 4798.3 78.5 inv = parallel_invert(inv, n)
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442
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443 208 421 2.0 0.0 if not isinstance(inv, list):
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444 return inv
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445
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446 19136 20290 1.1 1.6 for i in xrange(len(p)):
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447 18928 22958 1.2 1.8 x = p[i][0]
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448 18928 35512 1.9 2.8 m = (x * x) % n
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449 18928 48911 2.6 3.8 m = ((m + m + m + p[i][2]) * inv[i]) % n
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450 18928 44906 2.4 3.5 p[i][0] = (m * m - x - x) % n
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451 18928 51985 2.7 4.1 p[i][1] = (m * (x - p[i][0]) - p[i][1]) % n
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452
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453 208 235 1.1 0.0 return p
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File: pyecm.py
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Function: parallel_invert at line 538
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Total time: 0.971774 s
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Line # Hits Time Per Hit % Time Line Contents
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==============================================================
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538 @profile
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539 def parallel_invert(l, n):
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540 '''Inverts all elements of a list modulo some number, using 3(n-1) modular multiplications and one inversion.
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541
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542 Returns the list with all elements inverted modulo 3(n-1).'''
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543 230 479 2.1 0.0 l_ = l[:]
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544 20930 20740 1.0 2.1 for i in xrange(len(l)-1):
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545 20700 43498 2.1 4.5 l[i+1] = (l[i] * l[i+1]) % n
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546
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547 230 804621 3498.4 82.8 inv = invert(l[-1], n)
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548 230 311 1.4 0.0 if inv == 0:
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549 1 435 435.0 0.0 return gcd(l[-1], n)
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550
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551 20839 21461 1.0 2.2 for i in xrange(len(l)-1, 0, -1):
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552 20610 41223 2.0 4.2 l[i] = (inv * l[i-1]) % n
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553 20610 38521 1.9 4.0 inv = (inv * l_[i]) % n
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554 229 254 1.1 0.0 l[0] = inv
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555
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556 229 231 1.0 0.0 return l
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‎profile_results/pyecm.py.memory_profiler

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test
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909091
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5076141624365532994918781726395939035533
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Filename: pyecm.py
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Line # Mem usage Increment Line Contents
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================================================
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76 @profile
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77 def invert(a, b):
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78 '''Computes the inverse of a modulo b. b must be odd.
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79
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80 11.754 MB 0.000 MB Returns the inverse of a (mod b).'''
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81 28.969 MB 17.215 MB if a == 0 or b == 0:
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82 return 0
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83
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84 28.969 MB 0.000 MB truth = False
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85 28.969 MB 0.000 MB if a < 0:
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86 truth = True
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87 a = -a
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88
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89 28.969 MB 0.000 MB b_orig = b
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90 28.969 MB 0.000 MB alpha = 1
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91 22.930 MB -6.039 MB beta = 0
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92
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93 28.969 MB 6.039 MB while not a & 1:
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94 28.969 MB 0.000 MB if alpha & 1:
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95 24.012 MB -4.957 MB alpha += b_orig
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96 28.969 MB 4.957 MB alpha >>= 1
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97 28.195 MB -0.773 MB a >>= 1
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98
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99 28.969 MB 0.773 MB if b > a:
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100 28.969 MB 0.000 MB a, b = b, a
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101 11.852 MB -17.117 MB alpha, beta = beta, alpha
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102
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103 29.008 MB 17.156 MB while b != 0 and a != b:
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104 29.008 MB 0.000 MB a -= b
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105 18.379 MB -10.629 MB alpha -= beta
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106
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107 29.008 MB 10.629 MB while not a & 1:
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108 29.008 MB 0.000 MB if alpha & 1:
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109 21.496 MB -7.512 MB alpha += b_orig
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110 29.008 MB 7.512 MB alpha >>= 1
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111 29.008 MB 0.000 MB a >>= 1
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112
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113 29.008 MB 0.000 MB if b > a:
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114 29.008 MB 0.000 MB a,b = b,a
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115 29.008 MB 0.000 MB alpha, beta = beta, alpha
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116
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117 29.008 MB 0.000 MB if a == b:
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118 29.008 MB 0.000 MB a -= b
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119 29.008 MB 0.000 MB alpha -= beta
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120 29.008 MB 0.000 MB a, b = b, a
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121 29.008 MB 0.000 MB alpha, beta = beta, alpha
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122
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123 29.008 MB 0.000 MB if a != 1:
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124 29.008 MB 0.000 MB return 0
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125 11.754 MB -17.254 MB
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126 28.945 MB 17.191 MB if truth:
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127 alpha = b_orig - alpha
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128
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129 28.945 MB 0.000 MB return alpha
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Filename: pyecm.py
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Line # Mem usage Increment Line Contents
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================================================
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538 @profile
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539 def parallel_invert(l, n):
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540 '''Inverts all elements of a list modulo some number, using 3(n-1) modular multiplications and one inversion.
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541
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542 11.746 MB 0.000 MB Returns the list with all elements inverted modulo 3(n-1).'''
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543 11.844 MB 0.098 MB l_ = l[:]
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544 28.965 MB 17.121 MB for i in xrange(len(l)-1):
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545 28.969 MB 0.004 MB l[i+1] = (l[i] * l[i+1]) % n
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546
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547 29.008 MB 0.039 MB inv = invert(l[-1], n)
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548 29.008 MB 0.000 MB if inv == 0:
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549 29.008 MB 0.000 MB return gcd(l[-1], n)
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550 11.754 MB -17.254 MB
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551 28.949 MB 17.195 MB for i in xrange(len(l)-1, 0, -1):
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552 28.949 MB 0.000 MB l[i] = (inv * l[i-1]) % n
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553 28.949 MB 0.000 MB inv = (inv * l_[i]) % n
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554 28.949 MB 0.000 MB l[0] = inv
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555
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556 28.949 MB 0.000 MB return l
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Filename: pyecm.py
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Line # Mem usage Increment Line Contents
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================================================
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431 @profile
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432 def double(p, n):
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433 '''Doubles each point in the input list. Much like the add function, we take advantage of fast inversion.
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434
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435 11.770 MB 0.000 MB Returns the doubled list.'''
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436 11.902 MB 0.133 MB inv = range(len(p))
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437
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438 28.887 MB 16.984 MB for i in xrange(len(p)):
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439 28.887 MB 0.000 MB inv[i] = p[i][1] << 1
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440
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441 28.949 MB 0.062 MB inv = parallel_invert(inv, n)
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442
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443 11.973 MB -16.977 MB if not isinstance(inv, list):
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444 return inv
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445
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446 28.961 MB 16.988 MB for i in xrange(len(p)):
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447 28.961 MB 0.000 MB x = p[i][0]
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448 28.961 MB 0.000 MB m = (x * x) % n
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449 28.961 MB 0.000 MB m = ((m + m + m + p[i][2]) * inv[i]) % n
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450 28.961 MB 0.000 MB p[i][0] = (m * m - x - x) % n
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451 28.961 MB 0.000 MB p[i][1] = (m * (x - p[i][0]) - p[i][1]) % n
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452
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453 28.961 MB 0.000 MB return p
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‎profile_results/pyecm.py.plop

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Factoring 10000000000000000000000000000000000000000000000001:
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11
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909091
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5076141624365532994918781726395939035533
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profile output saved to /tmp/plop.out
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overhead was 2.93319289749e-05 per sample (0.00293319289749%)

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