core dump when request 2 or more gpus with Tesla T4

### 1. Issue or feature description
It's ok when request 1 gpu in yaml. But when request more than 1, the output of nvidia-smi is below:
The output of nvidia-smi in host machine is ok.

In another machine with GeForce RTX 2070 SUPER ,it's all right when request 2 gpus.
but when I run application locally , it abort due to :
```
[4pdvGPU ERROR (pid:697 thread=140106827071488 context.c:189)]: cuCtxGetDevice Not Found. tid=140106827071488 ctx=0x239601906000:0x23960041a000
home/limengxuan/work/libcuda_override/src/cuda/context.c:189: cuCtxGetDevice: Assertion `0' failed.
```

### 2. Steps to reproduce the issue
ubuntu1~20.04 + microk8s + Tesla T4 GPU + 510driver
### 3. Information to [attach](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests/) (optional if deemed irrelevant)

Common error checking:
- [ ] The output of `nvidia-smi -a` on your host
- [ ] Your docker configuration file (e.g: `/etc/docker/daemon.json`)
-{
"default-runtime": "nvidia",
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
}
}

Additional information that might help better understand your environment and reproduce the bug:
- [ ] Any relevant kernel output lines from `dmesg`
```
nvidia-smi[2260220]: segfault at 0 ip 00007fde46d051ce sp 00007ffe1ae4c9e8 error 4 in libc-2.31.so[7fde46b9d000+178000]
[89993.700532] Code: fd d7 c9 0f bc d1 c5 fe 7f 27 c5 fe 7f 6f 20 c5 fe 7f 77 40 c5 fe 7f 7f 60 49 83 c0 1f 49 29 d0 48 8d 7c 17 61 e9 c2 04 00 00 fe 6f 1e c5 fe 6f 56 20 c5 fd 74 cb c5 fd d7 d1 49 83 f8 21 0f
[90182.697502] nvidia-smi[2265941]: segfault at 0 ip 00007f241971c1ce sp 00007fffff703d08 error 4 in libc-2.31.so[7f24195b4000+178000]
[90182.697509] Code: fd d7 c9 0f bc d1 c5 fe 7f 27 c5 fe 7f 6f 20 c5 fe 7f 77 40 c5 fe 7f 7f 60 49 83 c0 1f 49 29 d0 48 8d 7c 17 61 e9 c2 04 00 00 fe 6f 1e c5 fe 6f 56 20 c5 fd 74 cb c5 fd d7 d1 49 83 f8 21 0f

```

The issue involves a core dump when requesting multiple Tesla T4 GPUs via the k8s-vgpu-scheduler. Start by examining the device plugin code handling multi-GPU allocation, particularly around CUDA context management. Check logs and the provided dmesg output for segmentation faults in nvidia-smi. Reproduce the environment with Ubuntu 20.04, microk8s, Tesla T4, and driver 510. Look at the nvidia-container-runtime configuration and how the scheduler maps multiple GPUs to pods.

Written by the indexing model from the issue text.