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aisensiy/pytorch-gpu-benchmark

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MIT

About

Comparison of learning and inference speed of different gpu with various cnn models in pytorch

  • 1080TI
  • TITAN V
  • 2080TI

Specification

Graphics Card Name NVIDIA GeForce GTX 1080 Ti NVIDIA GeForce RTX 2080 Ti NVIDIA TITAN V
Process 16nm 12nm 12nm
Die Size 471mm² 754mm² 815mm²
Transistors 11,800 million 18,600 million 21,100 million
CUDA Cores 3584 Cores 4352 Cores 5120 Cores
Tensor Cores None 544 Cores 640 Cores
Clock(base) 1481 MHz 1350 MHz 1200 MHz
FP16 (half) performance 177.2 GFLOPS 26,895 GFLOPS 29,798 GFLOPS
FP32 (float) performance 11,340 GFLOPS 13,448 GFLOPS 14,899 GFLOPS
FP64 (double) performance 354.4 GFLOPS 420.2 GFLOPS 7,450 GFLOPS
Memory 11GB GDDR5X 11 GB GDDR6 12 GB HBM2
Memory Speed 11Gbps 14.00 Gbps 1.7Gbps HBM2
Memory Interface 352-bit 352-bit 3072-bit
Memory Bandwidth 484 GB/s 616 GB/s 653GB/s
Price $699 US $1,199 US $2,999 US
Release Date Mar 10th, 2017 Sep 20th, 2018 Dec 7th, 2017

reference site

  1. Single & multi GPU with batch size 12: compare training and inference speed of **SequeezeNet, VGG-16, VGG-19, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet169, DenseNet201, DenseNet161 mobilenet mnasnet ... **

  2. Experiments are performed on three types of datatype. single precision, double precision, half precision

  3. making plot(plotly)

Usage

./test.sh

Results

requirement

  • torchvision
  • torch>=1.0.0
  • pandas
  • plotly
  • cufflinks
  • psutil

Environment

  • Pytorch version 1.4
  • Number of GPUs on current device 4
  • CUDA version = 10.0
  • CUDNN version= 7601

Change Log

  • 2020/01/17
    • Edit coding style and some bug
    • Change plot method
    • Add results of various model experiments(only 2080ti)
  • 2019/01/09
    • PR Update typo (thank for johmathe)
    • Add requirements.txt
    • Add result figures
    • Add ('TkAgg') for cli
    • Addition Muilt GPUS (DGX-station)

New Result

Comparison single 2080ti gpu
Total(train)
Half Single Double
Total(inference)
Half Single Double
densenet(train)
Half Single Double
densenet(inference)
Half Single Double
vgg(train)
Half Single Double
vgg(inference)
Half Single Double
squeezenet(train)
Half Single Double
squeezenet(inference)
Half Single Double
shufflenetv2(train)
Half Single Double
shufflenetv2(inference)
Half Single Double
resnet(train)
Half Single Double
resnet(inference)
Half Single Double
mobilenet(train)
Half Single Double
mobilenet(inference)
Half Single Double
mnasnet(train)
Half Single Double
mnasnet(inference)
Half Single Double



Comparison multi gpu ( using 2 2080ti gpus)
Total(train)
Half Single Double
Total(inference)
Half Single Double
densenet(train)
Half Single Double
densenet(inference)
Half Single Double
vgg(train)
Half Single Double
vgg(inference)
Half Single Double
squeezenet(train)
Half Single Double
squeezenet(inference)
Half Single Double
shufflenetv2(train)
Half Single Double
shufflenetv2(inference)
Half Single Double
resnet(train)
Half Single Double
resnet(inference)
Half Single Double
mobilenet(train)
Half Single Double
mobilenet(inference)
Half Single Double
mnasnet(train)
Half Single Double
mnasnet(inference)
Half Single Double



Comparison multi gpu ( using 3 2080ti gpus)
Total(train)
Half Single Double
Total(inference)
Half Single Double
densenet(train)
Half Single Double
densenet(inference)
Half Single Double
vgg(train)
Half Single Double
vgg(inference)
Half Single Double
squeezenet(train)
Half Single Double
squeezenet(inference)
Half Single Double
shufflenetv2(train)
Half Single Double
shufflenetv2(inference)
Half Single Double
resnet(train)
Half Single Double
resnet(inference)
Half Single Double
mobilenet(train)
Half Single Double
mobilenet(inference)
Half Single Double
mnasnet(train)
Half Single Double
mnasnet(inference)
Half Single Double



Comparison multi gpu ( using 4 2080ti gpus)
Total(train)
Half Single Double
Total(inference)
Half Single Double
densenet(train)
Half Single Double
densenet(inference)
Half Single Double
vgg(train)
Half Single Double
vgg(inference)
Half Single Double
squeezenet(train)
Half Single Double
squeezenet(inference)
Half Single Double
shufflenetv2(train)
Half Single Double
shufflenetv2(inference)
Half Single Double
resnet(train)
Half Single Double
resnet(inference)
Half Single Double
mobilenet(train)
Half Single Double
mobilenet(inference)
Half Single Double
mnasnet(train)
Half Single Double
mnasnet(inference)
Half Single Double

Comparison between networks (single GPU)

Each network is fed with 12 images with 224x224x3 dimensions. For training, time durations of 20 passes of forward and backward are averaged. For inference, time durations of 20 passes of forward are averaged. 5 warm up steps are performed that do not calculate towards the final result.

I conducted the experiment using two rtx 2080ti.

Mode gpu precision densenet121 densenet161 densenet169 densenet201 resnet101 resnet152 resnet18 resnet34 resnet50 squeezenet1_0 squeezenet1_1 vgg16 vgg16_bn vgg19 vgg19_bn
Training TITAN V single 56.17 ms 120.7 ms 72.59 ms 93.35 ms 84.59 ms 119.5 ms 16.69 ms 28.27 ms 50.54 ms 15.30 ms 9.857 ms 72.85 ms 80.95 ms 85.55 ms 94.42 ms
Inference TITAN V single 17.49 ms 39.33 ms 23.63 ms 30.93 ms 23.96 ms 34.22 ms 4.827 ms 8.428 ms 14.27 ms 4.565 ms 2.765 ms 22.94 ms 25.41 ms 27.55 ms 30.28 ms
Training TITAN V double 139.8 ms 387.4 ms 175.9 ms 224.5 ms 509.9 ms 720.0 ms 94.21 ms 194.6 ms 271.7 ms 68.38 ms 31.18 ms 1463. ms 1484. ms 1993. ms 2016. ms
Inference TITAN V double 47.68 ms 170.5 ms 60.73 ms 78.43 ms 317.7 ms 448.6 ms 60.26 ms 129.9 ms 159.8 ms 42.37 ms 11.95 ms 1261. ms 1266. ms 1745. ms 1751. ms
Training TITAN V half 43.79 ms 75.16 ms 57.53 ms 70.88 ms 47.82 ms 67.43 ms 10.48 ms 17.19 ms 29.08 ms 13.15 ms 9.390 ms 36.03 ms 46.84 ms 41.16 ms 52.65 ms
Inference TITAN V half 11.87 ms 22.88 ms 16.04 ms 20.70 ms 12.80 ms 18.11 ms 3.085 ms 5.116 ms 7.608 ms 3.694 ms 2.329 ms 10.96 ms 13.26 ms 12.72 ms 15.17 ms
Training 1080ti single 77.18 ms 164.0 ms 99.66 ms 127.6 ms 112.8 ms 158.7 ms 22.48 ms 36.80 ms 68.87 ms 20.56 ms 13.29 ms 101.8 ms 114.1 ms 119.9 ms 133.2 ms
Inference 1080ti single 23.53 ms 51.53 ms 31.82 ms 41.73 ms 33.02 ms 47.02 ms 6.426 ms 10.97 ms 20.17 ms 7.174 ms 4.370 ms 33.73 ms 37.25 ms 39.95 ms 44.12 ms
Training 1080ti double 779.5 ms 2522. ms 940.4 ms 1196. ms 2410. ms 3546. ms 463.3 ms 969.9 ms 1216. ms 259.9 ms 131.5 ms 4227. ms 4271. ms 5475. ms 5522. ms
Inference 1080ti double 47.68 ms 275.2 ms 1157. ms 328.6 ms 414.9 ms 1080. ms 1589. ms 181.1 ms 390.8 ms 529.6 ms 110.9 ms 49.96 ms 2094. ms 2103. ms 2775. ms
Training 1080ti half 43.79 ms 70.00 ms 148.4 ms 89.43 ms 113.6 ms 151.0 ms 219.5 ms 21.00 ms 34.84 ms 76.24 ms 19.60 ms 13.18 ms 91.60 ms 105.9 ms 108.1 ms
Inference 1080ti half 18.62 ms 42.26 ms 25.27 ms 33.01 ms 27.49 ms 38.88 ms 5.645 ms 9.765 ms 16.26 ms 5.869 ms 3.576 ms 30.69 ms 33.22 ms 36.71 ms 39.51 ms
Mode gpu precision resnet18 resnet34 resnet50 resnet101 resnet152 densenet121 densenet169 densenet201 densenet161 squeezenet1_0 squeezenet1_1 vgg16 vgg16_bn vgg19_bn vgg19
Training RTX 2080ti(1) single 16.36 ms 28.44 ms 49.63 ms 81.40 ms 115.1 ms 57.69 ms 75.18 ms 91.69 ms 112.7 ms 14.49 ms 9.108 ms 75.86 ms 85.42 ms 98.43 ms 88.05 ms
Inference RTX 2080ti(1) single 4.894 ms 8.624 ms 14.65 ms 24.57 ms 35.15 ms 16.70 ms 21.94 ms 28.89 ms 34.64 ms 4.704 ms 2.765 ms 23.70 ms 26.25 ms 30.82 ms 28.03 ms
Training RTX 2080ti(1) double 367.9 ms 755.4 ms 939.9 ms 1844. ms 2702. ms 593.5 ms 724.3 ms 921.3 ms 1916. ms 187.8 ms 94.99 ms 3251. ms 3277. ms 4265. ms 4238. ms
Inference RTX 2080ti(1) double 165.0 ms 328.5 ms 436.4 ms 831.0 ms 1196. ms 213.8 ms 266.0 ms 339.5 ms 910.7 ms 82.71 ms 35.79 ms 1702. ms 1708. ms 2280. ms 2274. ms
Training RTX 2080ti(1) half 13.17 ms 22.25 ms 35.46 ms 57.50 ms 81.38 ms 51.11 ms 66.88 ms 80.20 ms 88.37 ms 17.87 ms 35.75 ms 53.16 ms 63.06 ms 72.75 ms 61.95 ms
Inference RTX 2080ti(1) half 3.423 ms 5.662 ms 9.035 ms 14.51 ms 20.52 ms 13.47 ms 17.54 ms 22.51 ms 27.10 ms 4.280 ms 2.397 ms 16.14 ms 18.14 ms 19.76 ms 17.89 ms
Training RTX 2080ti(2) single 16.92 ms 29.51 ms 51.46 ms 84.90 ms 120.0 ms 58.13 ms 75.96 ms 92.47 ms 117.6 ms 14.95 ms 9.255 ms 78.95 ms 88.71 ms 102.3 ms 91.67 ms
Inference RTX 2080ti(2) single 5.107 ms 8.976 ms 15.18 ms 25.60 ms 36.60 ms 17.02 ms 22.40 ms 29.46 ms 36.72 ms 4.852 ms 2.786 ms 24.76 ms 27.25 ms 32.05 ms 29.27 ms
Training RTX 2080ti(2) double 381.9 ms 781.5 ms 971.6 ms 1900. ms 2777. ms 610.6 ms 744.7 ms 948.1 ms 1974. ms 191.9 ms 97.27 ms 3317. ms 3350. ms 4357. ms 4329. ms
Inference RTX 2080ti(2) double 171.8 ms 341.7 ms 449.5 ms 849.5 ms 1231. ms 221.1 ms 275.2 ms 352.5 ms 938.9 ms 83.66 ms 36.48 ms 1715. ms 1721. ms 2294. ms 2289. ms
Training RTX 2080ti(2) half 13.57 ms 22.97 ms 36.55 ms 59.10 ms 83.81 ms 51.74 ms 68.35 ms 81.21 ms 89.46 ms 15.75 ms 35.46 ms 55.28 ms 65.43 ms 75.75 ms 64.62 ms
Inference RTX 2080ti(2) half 3.520 ms 5.837 ms 9.272 ms 14.93 ms 21.13 ms 13.38 ms 18.71 ms 22.40 ms
26.82 ms 4.446 ms 2.406 ms 16.29 ms 17.91 ms 20.90 ms 19.14 ms



The result below is the result from the previous code.



TitanV Total

TitanV inference
densenet resnet
vgg squeezenet



TitanV Training

densenet resnet
vgg squeezenet



1080ti Total

1080ti inference
densenet resnet
vgg squeezenet



1080ti Training

densenet resnet
vgg squeezenet



rtx2080ti Total

rtx2080ti inference
densenet resnet
vgg squeezenet



rtx2080ti Training

densenet resnet
vgg squeezenet



Device comparison(Training)

VGG
half single double
resnet
half single double
densenet
half single double
squeezenet
half single double





Device comparison(inference)

VGG
half single double
resnet
half single double
densenet
half single double
squeezenet
half single double

DGX STATION SPEC

Spec NVIDIA DGX Station
GPUs 4 x Tesla V100
TFLOPS (GPU FP16) 480
GPU Memory 64 GB total system
CPU 20-Core Intel Xeon E5-2698 v4 2.2 GHz
NVIDIA CUDA Cores 20,480
NVIDIA Tensor Cores 2,560
Maximum Power Requirements 1,500 W
System Memory 256 GB DDR4 LRDIMM
Storage 4 (data: 3 and OS: 1) x 1.92 TB SSD RAID 0
Network Dual 10 GbE, 4 IB EDR
Display 3X DisplayPort, 4K resolution
Acoustics < 35 dB
Software Ubuntu Linux Host OSDGX Recommended GPU DriverCUDA Toolkit
System Weight 88 lbs / 40 kg
System Dimensions 518 D x 256 W x 639 H (mm)
Operating Temperature Range 10 – 30 °C

result

batchs gpus times
half 16 1 15.6316900253296
half 16 2 25.2950036525726
half 16 3 32.5298488140106
half 16 4 39.5952260494232
half 32 1 28.9202857017517
half 32 2 26.9314527511597
half 32 3 32.6970362663269
half 32 4 40.0277709960938
half 64 1 54.6519541740418
half 64 2 36.9417870044708
half 64 3 35.1460886001587
half 64 4 39.9034130573273
half 128 1 105.689181089401
half 128 2 62.5697267055512
half 128 3 50.5970776081085
half 128 4 45.686126947403
single 16 1 15.7001733779907
single 16 2 25.2602100372314
single 16 3 32.5334632396698
single 16 4 39.9562275409698
single 32 1 29.0114963054657
single 32 2 26.9594860076904
single 32 3 32.7185535430908
single 32 4 39.8312091827393
single 64 1 54.7226464748383
single 64 2 38.2881510257721
single 64 3 35.2633249759674
single 64 4 40.4890751838684
single 128 1 105.767976045609
single 128 2 62.6480567455292
single 128 3 50.3757321834564
single 128 4 45.5866599082947
double 16 1 15.703741312027
double 16 2 25.3219473361969
double 16 3 33.0831336975098
double 16 4 40.441951751709
double 32 1 29.0125107765198
double 32 2 27.3240101337433
double 32 3 33.0090951919556
double 32 4 40.2768909931183
double 64 1 54.7836709022522
double 64 2 36.7958390712738
double 64 3 35.0011682510376
double 64 4 39.9146497249603
double 128 1 105.872387886047
double 128 2 62.9272031784058
double 128 3 48.4100317955017
double 128 4 45.5989670753479
MIT License Copyright (c) 2018 Jaehun Ryu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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