Time Profiling · Apache SINGA
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EditTime Profiling
SINGA 支持对图中缓冲的每个运算符进行时间分析。为了利用时间分析功能，我们首先调
用device.SetVerbosity方法来设置时间分析器的 verbosity，然后调
用device.PrintTimeProfiling来打印出时间分析的结果。
设置时间分析 Verbosity
要使用时间分析功能，我们需要设置 verbosity。有三个级别的 verbosity。在默认
值verbosity == 0的情况下，它不会进行任何时间分析。当我们设置verbosity == 1时
，它将对前向和后向传播时间进行分析。当verbosity == 2时，它将对图中每个缓冲操作
所花费的时间进行分析。
以下是设置时间分析功能的示例代码：
# create a device
from singa import device
dev = device.create_cuda_gpu()
# set the verbosity
verbosity = 2
dev.SetVerbosity(verbosity)
# optional: skip the first 5 iterations when profiling the time
dev.SetSkipIteration(5)
那么，当我们在程序的最后完成训练后，我们就可以通过调
用device.PrintTimeProfiling方法来打印时间分析结果。
dev.PrintTimeProfiling()
不同 Verbosity 的输出示例
我们可以运行 ResNet
的示例，
看看不同的 verbosity 设置的输出。
verbosity == 1
Time Profiling:
Forward Propagation Time : 0.0409127 sec
Backward Propagation Time : 0.114813 sec
verbosity == 2
Time Profiling:
OP_ID0. SetValue : 1.73722e-05 sec
OP_ID1. cudnnConvForward : 0.000612724 sec
OP_ID2. GpuBatchNormForwardTraining : 0.000559449 sec
OP_ID3. ReLU : 0.000375004 sec
OP_ID4. GpuPoolingForward : 0.000240041 sec
OP_ID5. SetValue : 3.4176e-06 sec
OP_ID6. cudnnConvForward : 0.000115619 sec
OP_ID7. GpuBatchNormForwardTraining : 0.000150415 sec
OP_ID8. ReLU : 9.95494e-05 sec
OP_ID9. SetValue : 3.22432e-06 sec
OP_ID10. cudnnConvForward : 0.000648668 sec
OP_ID11. GpuBatchNormForwardTraining : 0.000149793 sec
OP_ID12. ReLU : 9.92118e-05 sec
OP_ID13. SetValue : 3.37728e-06 sec
OP_ID14. cudnnConvForward : 0.000400953 sec
OP_ID15. GpuBatchNormForwardTraining : 0.000572181 sec
OP_ID16. SetValue : 3.21312e-06 sec
OP_ID17. cudnnConvForward : 0.000398698 sec
OP_ID18. GpuBatchNormForwardTraining : 0.00056836 sec
OP_ID19. Add : 0.000542246 sec
OP_ID20. ReLU : 0.000372783 sec
OP_ID21. SetValue : 3.25312e-06 sec
OP_ID22. cudnnConvForward : 0.000260731 sec
OP_ID23. GpuBatchNormForwardTraining : 0.000149041 sec
OP_ID24. ReLU : 9.9072e-05 sec
OP_ID25. SetValue : 3.10592e-06 sec
OP_ID26. cudnnConvForward : 0.000637481 sec
OP_ID27. GpuBatchNormForwardTraining : 0.000152577 sec
OP_ID28. ReLU : 9.90518e-05 sec
OP_ID29. SetValue : 3.28224e-06 sec
OP_ID30. cudnnConvForward : 0.000404586 sec
OP_ID31. GpuBatchNormForwardTraining : 0.000569679 sec
OP_ID32. Add : 0.000542291 sec
OP_ID33. ReLU : 0.00037211 sec
OP_ID34. SetValue : 3.13696e-06 sec
OP_ID35. cudnnConvForward : 0.000261219 sec
OP_ID36. GpuBatchNormForwardTraining : 0.000148281 sec
OP_ID37. ReLU : 9.89299e-05 sec
OP_ID38. SetValue : 3.25216e-06 sec
OP_ID39. cudnnConvForward : 0.000633644 sec
OP_ID40. GpuBatchNormForwardTraining : 0.000150711 sec
OP_ID41. ReLU : 9.84902e-05 sec
OP_ID42. SetValue : 3.18176e-06 sec
OP_ID43. cudnnConvForward : 0.000402752 sec
OP_ID44. GpuBatchNormForwardTraining : 0.000571523 sec
OP_ID45. Add : 0.000542435 sec
OP_ID46. ReLU : 0.000372539 sec
OP_ID47. SetValue : 3.24672e-06 sec
OP_ID48. cudnnConvForward : 0.000493054 sec
OP_ID49. GpuBatchNormForwardTraining : 0.000293142 sec
OP_ID50. ReLU : 0.000190047 sec
OP_ID51. SetValue : 3.14784e-06 sec
OP_ID52. cudnnConvForward : 0.00148837 sec
OP_ID53. GpuBatchNormForwardTraining : 8.34794e-05 sec
OP_ID54. ReLU : 5.23254e-05 sec
OP_ID55. SetValue : 3.40096e-06 sec
OP_ID56. cudnnConvForward : 0.000292971 sec
OP_ID57. GpuBatchNormForwardTraining : 0.00029174 sec
OP_ID58. SetValue : 3.3248e-06 sec
OP_ID59. cudnnConvForward : 0.000590154 sec
OP_ID60. GpuBatchNormForwardTraining : 0.000294149 sec
OP_ID61. Add : 0.000275119 sec
OP_ID62. ReLU : 0.000189268 sec
OP_ID63. SetValue : 3.2704e-06 sec
OP_ID64. cudnnConvForward : 0.000341232 sec
OP_ID65. GpuBatchNormForwardTraining : 8.3304e-05 sec
OP_ID66. ReLU : 5.23667e-05 sec
OP_ID67. SetValue : 3.19936e-06 sec
OP_ID68. cudnnConvForward : 0.000542484 sec
OP_ID69. GpuBatchNormForwardTraining : 8.60537e-05 sec
OP_ID70. ReLU : 5.2479e-05 sec
OP_ID71. SetValue : 3.41824e-06 sec
OP_ID72. cudnnConvForward : 0.000291295 sec
OP_ID73. GpuBatchNormForwardTraining : 0.000292795 sec
OP_ID74. Add : 0.000274438 sec
OP_ID75. ReLU : 0.000189689 sec
OP_ID76. SetValue : 3.21984e-06 sec
OP_ID77. cudnnConvForward : 0.000338776 sec
OP_ID78. GpuBatchNormForwardTraining : 8.484e-05 sec
OP_ID79. ReLU : 5.29408e-05 sec
OP_ID80. SetValue : 3.18208e-06 sec
OP_ID81. cudnnConvForward : 0.000545542 sec
OP_ID82. GpuBatchNormForwardTraining : 8.40976e-05 sec
OP_ID83. ReLU : 5.2256e-05 sec
OP_ID84. SetValue : 3.36256e-06 sec
OP_ID85. cudnnConvForward : 0.000293003 sec
OP_ID86. GpuBatchNormForwardTraining : 0.0002989 sec
OP_ID87. Add : 0.000275041 sec
OP_ID88. ReLU : 0.000189867 sec
OP_ID89. SetValue : 3.1184e-06 sec
OP_ID90. cudnnConvForward : 0.000340417 sec
OP_ID91. GpuBatchNormForwardTraining : 8.39395e-05 sec
OP_ID92. ReLU : 5.26544e-05 sec
OP_ID93. SetValue : 3.2336e-06 sec
OP_ID94. cudnnConvForward : 0.000539787 sec
OP_ID95. GpuBatchNormForwardTraining : 8.2753e-05 sec
OP_ID96. ReLU : 4.86758e-05 sec
OP_ID97. SetValue : 3.24384e-06 sec
OP_ID98. cudnnConvForward : 0.000287108 sec
OP_ID99. GpuBatchNormForwardTraining : 0.000293127 sec
OP_ID100. Add : 0.000269478 sec
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Last updated on 9/27/2020
← Distributed TrainingHalf Precision →
设置时间分析 Verbosity
不同 Verbosity 的输出示例
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