Easy benchmarking of all public open-source implementations of convnets
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README.md

README.md

convnet-benchmarks

Easy benchmarking of all public open-source implementations of convnets. A summary is provided in the section below.

Machine: 6-core Intel Core i7-5930K CPU @ 3.50GHz + NVIDIA Titan X + Ubuntu 14.04 x86_64

##Imagenet Winners Benchmarking I pick some popular imagenet models, and I clock the time for a full forward + backward pass. I average my times over 10 runs. I ignored dropout and softmax layers.

AlexNet (One Weird Trick paper) - Input 128x3x224x224

Library Class Time (ms) forward (ms) backward (ms)
NervanaSys-16 ConvLayer 97 30 67
NervanaSys-32 ConvLayer 109 31 78
fbfft SpatialConvolutionCuFFT 136 45 91
cudaconvnet2* ConvLayer 177 42 135
CuDNN (R2) * cudnn.SpatialConvolution 231 70 161
Caffe (native) ConvolutionLayer 324 121 203
Torch-7 (native) SpatialConvolutionMM 342 132 210

Overfeat [fast] - Input 128x3x231x231

Library Class Time (ms) forward (ms) backward (ms)
NervanaSys-16 ConvLayer 364 119 245
NervanaSys-32 ConvLayer 410 126 284
fbfft SpatialConvolutionCuFFT 407 139 268
cudaconvnet2* ConvLayer 723 176 547
CuDNN (R2) * cudnn.SpatialConvolution 810 234 576
Caffe ConvolutionLayer 823 355 468
Torch-7 (native) SpatialConvolutionMM 878 379 499

OxfordNet [Model-A] - Input 64x3x224x224

Library Class Time (ms) forward (ms) backward (ms)
NervanaSys-16 ConvLayer 530 166 364
NervanaSys-32 ConvLayer 629 173 456
fbfft SpatialConvolutionCuFFT 1092 355 737
cudaconvnet2* ConvLayer 1229 408 821
CuDNN (R2) * cudnn.SpatialConvolution 1099 342 757
Caffe ConvolutionLayer 1068 323 745
Torch-7 (native) SpatialConvolutionMM 1105 350 755

Layer-wise Benchmarking

###Spatial Convolution layer (3D input 3D output, densely connected)

forward + backprop (wrt input and weights)
Original Library Class/Function Benchmarked Time (ms) forward (ms) backward (ms)
fbfft SpatialConvolutionCuFFT 256 101 155
cuda-convnet2 * ConvLayer 977 201 776
cuda-convnet** pylearn2.cuda_convnet 1077 312 765
CuDNN R2 * cudnn.SpatialConvolution 1019 269 750
Theano CorrMM 1225 407 818
Caffe ConvolutionLayer 1231 396 835
Torch-7 SpatialConvolutionMM 1265 418 877
DeepCL ConvolutionLayer 6280 2648 3632
cherry-picking**** best per layer 235 79 155

This table is NOT UPDATED For TITAN-X. These numbers below were on Titan Black and are here only for informational and legacy purposes.

Original Library Class/Function Benchmarked Time (ms) forward (ms) backward (ms)
Theano (experimental)*** conv2d_fft 1178 304 874
Torch-7 nn.SpatialConvolutionBHWD 1892 581 1311
ccv ccv_convnet_layer 809+bw 809
Theano (legacy) conv2d 70774 3833 66941
  • * indicates that the library was tested with Torch bindings of the specific kernels.
  • ** indicates that the library was tested with Pylearn2 bindings.
  • *** This is an experimental module which used FFT to calculate convolutions. It uses a lot of memory according to @benanne
  • **** The last row shows results obtainable when choosing the best-performing library for each layer.
  • L1 - Input: 128x128 Batch-size 128, Feature maps: 3->96, Kernel Size: 11x11, Stride: 1x1
  • L2 - Input: 64x64 Batch-size 128, Feature maps: 64->128, Kernel Size: 9x9, Stride: 1x1
  • L3 - Input: 32x32 Batch-size 128, Feature maps: 128->128, Kernel Size: 9x9, Stride: 1x1
  • L4 - Input: 16x16 Batch-size 128, Feature maps: 128->128, Kernel Size: 7x7, Stride: 1x1
  • L5 - Input: 13x13 Batch-size 128, Feature maps: 384->384, Kernel Size: 3x3, Stride: 1x1
  • The table is ranked according to the total time forward+backward calls for layers (L1 + L2 + L3 + L4 + L5)

#####Breakdown

forward

Columns L1, L2, L3, L4, L5, Total are times in milliseconds

Original Library Class/Function Benchmarked L1 L2 L3 L4 L5 Total
fbfft SpatialConvolutionCuFFT 57 27 6 2 9 101
cuda-convnet2 * ConvLayer 36 113 40 4 8 201
cuda-convnet** pylearn2.cuda_convnet 38 183 68 7 16 312
CuDNN R2 cudnn.SpatialConvolution 56 143 53 6 11 269
Theano CorrMM 91 143 121 24 28 407
Caffe ConvolutionLayer<Dtype> 93 136 116 24 27 396
Torch-7 nn.SpatialConvolutionMM 94 149 123 24 28 418
DeepCL ConvolutionLayer 738 1241 518 47 104 2648
cherry-picking**** best per layer 36 27 6 2 8 79
backward (gradInput + gradWeight)

Columns L1, L2, L3, L4, L5, Total are times in milliseconds

Original Library Class/Function Benchmarked L1 L2 L3 L4 L5 Total
fbfft SpatialConvolutionCuFFT 76 45 12 4 18 155
cuda-convnet2 * ConvLayer 103 467 162 15 29 776
cuda-convnet** pylearn2.cuda_convnet 136 433 147 15 34 765
CuDNN R2 cudnn.SpatialConvolution 139 401 159 19 32 750
Theano CorrMM 179 405 174 29 31 818
Caffe ConvolutionLayer<Dtype> 200 405 172 28 30 835
Torch-7 nn.SpatialConvolutionMM 206 432 178 29 32 877
DeepCL ConvolutionLayer 484 2144 747 59 198 3632
cherry-picking**** best per layer 76 45 12 4 18 155