Perf reduction due to munmap with dataloader pinning thread ?
Labels: module: dataloader, triaged

### 🐛 Describe the bug

As shown below, I am observing 10+ ms bubbles in the GPU streams, and it not every step will apper, and i add nvtx to https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/pin_memory.py#L39 ,Through analysis OS runtime libraries on nsight ,  i found that this line will lead to long munmap operation, so i doubt the bubble problem is caused by this

and Although I am not quite sure that the gap is caused by munmap, I observed the timeline of all gaps, and the corresponding CPU stack are all long munmap

relerated issue:
https://github.com/pytorch/pytorch/issues/77139

models : 
centerpoint https://github.com/tianweiy/CenterPoint 
c3d  https://github.com/xinge008/Cylinder3D

![image](https://user-images.githubusercontent.com/33319780/209288215-f2e85847-e039-4e8c-bb2d-9e6089f57329.png)



### Versions

Collecting environment information...
PyTorch version: 1.13.0
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.23.1
Libc version: glibc-2.31

Python version: 3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:35)  [GCC 10.4.0] (64-bit runtime)
Python platform: Linux-4.14.0_1-0-0-44-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB

Nvidia driver version: 470.82.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==1.13.0
[pip3] torch-scatter==2.0.9
[pip3] torchaudio==0.13.0
[pip3] torchvision==0.14.0
[conda] blas                      2.116                       mkl    conda-forge
[conda] blas-devel                3.9.0            16_linux64_mkl    conda-forge
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] liblapacke                3.9.0            16_linux64_mkl    conda-forge
[conda] mkl                       2022.1.0           h84fe81f_915    conda-forge
[conda] mkl-devel                 2022.1.0           ha770c72_916    conda-forge
[conda] mkl-include               2022.1.0           h84fe81f_915    conda-forge
[conda] numpy                     1.23.5           py38h7042d01_0    conda-forge
[conda] pytorch                   1.13.0          py3.8_cuda11.7_cudnn8.5.0_0    pytorch
[conda] pytorch-cuda              11.7                 h67b0de4_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch-scatter             2.0.9                    pypi_0    pypi
[conda] torchaudio                0.13.0               py38_cu117    pytorch
[conda] torchvision               0.14.0               py38_cu117    pytorch

cc @SsnL @VitalyFedyunin @ejguan @NivekT