Abstract
This Best Practices For Using cuDNN 3D Convolutions guide covers various 3D convolution and deconvolution guidelines. This document also provides guidelines for setting the cuDNN library parameters to enhance the performance for 3D convolutions in the cuDNN 8.2.1. Specifically, these guidelines are focused on settings such as filter sizes, padding and dilation settings.
For previously released cuDNN documentation, see cuDNN Archives.
These guidelines are applicable to 3D convolution and deconvolution functions starting in NVIDIA® CUDA® Deep Neural Network library™ (cuDNN) v7.6.3.
This document provides guidelines for setting the cuDNN library parameters to enhance the performance of 3D convolutions. Specifically, these guidelines are focused on settings such as filter sizes, padding and dilation settings. Additionally, an application-specific use-case, namely, medical imaging, is presented to demonstrate the performance enhancement of 3D convolutions with these recommended settings. Specifically, these guidelines are applicable to the following functions and their associated data types:
For more information, see the cuDNN Developer Guide and cuDNN API.
To optimize your performance in your model, ensure you meet the following general guidelines:
- Layout
-
The layout is in NCHW format.
- Filter size
-
The filter size is
Tx1x1,Tx2x2,Tx3x3,Tx5x5, whereTis a positive integer. There are additional limits for the value ofTinwgradand strideddgrad. - Stride
-
Arbitrary for forward and backward filter;
dgrad/deconv: 1x1x1 or 2x2x2 with 2x2x2 filter. - Dilation
-
The dilation is 1x1x1.
- Platform
-
The platform is Volta, Turing, and Ampere with input/output channels divisible by 8.
- Batch/image size
-
cuDNN will fallback to non-Tensor Core kernel if it determines that the workspace required is larger than 256MB of GPU memory. The workspace required depends on many factors. For the Tensor Core kernels, the workspace size generally scales linearly with output tensor size. Therefore, this can be mitigated by using smaller image sizes or mini-batch sizes.
2.1. Recommended Settings In cuDNN While Performing 3D Convolutions
The following tables show the specific improvements that were made in each release.
2.1.1. cuDNN 8.x.x Recommended Settings
Recommended settings while performing 3D convolutions for cuDNN 8.x.x.
| 8.0.3 - 8.2.1 | 8.0.0 and 8.0.1 Preview - 8.0.2 | ||
|---|---|---|---|
| Platform | NVIDIA Ampere GPU architecture NVIDIA Turing GPU architecture NVIDIA Volta GPU architecture |
||
| Convolution (3D or 2D) | 3D and 2D | ||
Convolution or deconvolution (fprop, dgrad, or wgrad) |
|
||
| Grouped convolution | Yes or No | Yes | |
| Group size | C_per_group == K_per_group == {4,8,16,32,64,128,256} |
C_per_group == K_per_group == {4,8,16,32} |
|
Data layout format (NHWC/NCHW)1 |
NDHWC | ||
| Input/output precision (FP16, FP32, or FP64) | FP16 and FP322 | ||
| Accumulator (compute) precision (FP16, FP32, or FP64) | FP32 | ||
| Filter (kernel) sizes | No limitation | ||
| Padding | No limitation | ||
| Image sizes | 2GB limitation for a tensor | ||
| Number of channels | C | 0 mod 8 |
|
| K | 0 mod 8 |
||
| Convolution mode | Cross-correlation and convolution | ||
| Strides | dgrad: 1x1x1 or 2x2x2 |
||
| Dilation | No limitation | ||
| Data pointer alignment | All data pointers are 16-bytes aligned. | ||
2.1.2. cuDNN 7.6.x Recommended Settings
Recommended settings while performing 3D convolutions for cuDNN 7.6.x.
| 7.6.5 | 7.6.4 | 7.6.2 | 7.6.1 | |||
|---|---|---|---|---|---|---|
| Platform | Turing Volta |
Volta | ||||
| Convolution (3D or 2D) | 3D and 2D | 3D | ||||
Convolution or deconvolution (fprop, dgrad, or wgrad) |
|
|
|
|||
| Grouped convolution | Yes or No | Yes | No | |||
| Group size | C_per_group == K_per_group == {4,8,16,32} |
NA | ||||
Data layout format (NHWC/NCHW)3 |
NCDHW | NCDHW4 | ||||
| Input/output precision (FP16, FP32, or FP64) | FP16 | FP16 or FP32 | FP165 or FP326 | |||
| Accumulator (compute) precision (FP16, FP32, or FP64) | FP32 | Better to be the same with input/output precision. | FP32 | |||
| Filter (kernel) sizes | 2x2x2 T7x1x1 Tx2x2 Tx3x3 Tx5x5 |
1x1x1 2x2x2 3x3x3 5x5x5 Tx1x1 Tx2x2 Tx3x3 Tx5x5 Tx1x1 Tx2x2 Tx3x3 Tx5x5 |
||||
| Padding | No limitation | Filter // 28 | ||||
| Image sizes | 256 MB WS limit | No limitation | 256 MB WS limit | |||
| Number of channels | C | Arbitrary | 0 mod 8 | |||
| K | Arbitrary | 0 mod 8 | ||||
| Convolution mode | Cross-correlation for dgrad; otherwise, both modes |
No limitation Cross-correlation |
||||
| Strides | 1x1x1 and 2x2x2 strides for dgrad |
2x2x2 Arbitrary stride |
1x1x1 | |||
| Dilation | 1x1x1 | |||||
The following table shows the average speed-up of unique cuDNN 3D convolution calls for each network on V100 and A100 GPUs that satisfies the conditions in Best Practices For Medical Imaging. The end-to-end training performance will depend on a number of factors, such as framework overhead, kernel run time, and model architecture type.
3.1. Average Speedup Of Unique cuDNN 3D Convolutions API Calls
3.1.1. cuDNN 8.x.x Average Speedup
cuDNN version 8.2.1 compared to 7.6.5
| Model | Batchsize | A100 8.2.1 vs V100 7.6.5 | V100 8.2.1 vs V100 7.6.5 | ||
|---|---|---|---|---|---|
| FP16 | FP32 | FP16 | FP32 | ||
| V-Net (3D-Image segmentation) | 2 | 2.5x | 7.7x | 2.2x | 2.5x |
| 8 | 3.7x | 6.4x | 2.6x | 1.7x | |
| 16 | 4.5x | 7.5x | 2.7x | 2.1x | |
| 32 | 6.5x | 5.7x | 3.6x | 1.6x | |
| 3D-UNet (3D-Image Segmentation) | 2 | 8.3x | 7.3x | 3.8x | 1.5x |
| 4 | 12.7x | 6.4x | 5.8x | 1.5x | |
cuDNN version 8.2.0 compared to 7.6.5
| Model | Batchsize | A100 8.2.0 vs V100 7.6.5 | V100 8.2.0 vs V100 7.6.5 | ||
|---|---|---|---|---|---|
| FP16 | FP32 | FP16 | FP32 | ||
| V-Net (3D-Image segmentation) | 2 | 2.3x | 7.3x | 2.2x | 2.5x |
| 8 | 3.4x | 5.9x | 2.4x | 1.8x | |
| 16 | 4.1x | 6.8x | 2.5x | 2.1x | |
| 32 | 5.8x | 5.1x | 3.3x | 1.6x | |
| 3D-UNet (3D-Image Segmentation) | 2 | 6.8x | 5.9x | 3.4x | 1.5x |
| 4 | 10.5x | 2.6x | 5.1x | 1.6x | |
cuDNN version 8.1.1 compared to 7.6.5
| Model | Batchsize | A100 8.1.1 vs V100 7.6.5 | V100 8.1.1 vs V100 7.6.5 | ||
|---|---|---|---|---|---|
| FP16 | FP32 | FP16 | FP32 | ||
| V-Net (3D-Image segmentation) | 2 | 2.3x | 6.8x | 2.1x | 2.4x |
| 8 | 3.2x | 5.1x | 2.3x | 1.8x | |
| 16 | 3.8x | 5.9x | 2.3x | 2.1x | |
| 32 | 5.4x | 4.4x | 3.1x | 1.6x | |
| 3D-UNet (3D-Image Segmentation) | 2 | 7.2x | 6.3x | 3.4x | 1.5x |
| 4 | 11x | 2.6x | 4.9x | 1.6x | |
cuDNN version 8.1.0 compared to 7.6.5
| Model | Batchsize | A100 8.1.0 vs V100 7.6.5 | V100 8.1.0 vs V100 7.6.5 | ||
|---|---|---|---|---|---|
| FP16 | FP32 | FP16 | FP32 | ||
| V-Net (3D-Image segmentation) | 2 | 2.4x | 7.3x | 2.2x | 2.4x |
| 8 | 3.4x | 5.3x | 2.3x | 1.8x | |
| 16 | 3.9x | 6x | 2.3x | 2.1x | |
| 32 | 5.5x | 4.4x | 3.1x | 1.6x | |
| 3D-UNet (3D-Image Segmentation) | 2 | 7.3x | 6.4x | 3.5x | 1.5x |
| 4 | 11.2x | 2.6x | 5x | 1.6x | |
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NHWC/NCHW corresponds to NDHWC/NCDHW in 3D convolution.
CUDNN_TENSOROP_MATH_ALLOW_CONVERSION pre-Ampere. Default TF32 math in Ampere.
NHWC/NCHW corresponds to NDHWC/NCDHW in 3D convolution.
CUDNN_TENSOROP_MATH
CUDNN_TENSOROP_MATH_ALLOW_CONVERSION
padding = filter // 2