nsight-for-remote-gpu-server
所属分类：GPU/显卡
开发工具：Cuda
文件大小：0KB
下载次数：0
上传日期：2018-08-09 10:53:21
上 传 者：
sh-1993
说明： nsight，一个用于CUDA编程的eclipse IDE，为远程gpu服务器设置
(nsight, an eclipse IDE for CUDA programming, set up for remote gpu server)
文件列表:
Dockerfile (872, 2018-08-09)
images/ (0, 2018-08-09)
images/nsight_1.png (271529, 2018-08-09)
images/nsight_10.png (235056, 2018-08-09)
images/nsight_11.png (168088, 2018-08-09)
images/nsight_2.png (214366, 2018-08-09)
images/nsight_3.png (183419, 2018-08-09)
images/nsight_4_2.png (185890, 2018-08-09)
images/nsight_5.png (285946, 2018-08-09)
images/nsight_6.png (165950, 2018-08-09)
images/nsight_7.png (185657, 2018-08-09)
images/nsight_8.png (182924, 2018-08-09)
images/nsight_9.png (270590, 2018-08-09)
src/ (0, 2018-08-09)
src/file2.cu (723, 2018-08-09)
src/file2.h (194, 2018-08-09)
src/mainfile.cu (388, 2018-08-09)
# Nsight: run CUDA code on remote gpu server
*nsight, an eclipse IDE for CUDA programming*
### Steps for configuring the execution on remote gpu server:
---
#### Insallation:
1. nvidia cuda-toolkit can be installed from [here](https://developer.nvidia.com/cuda-downloads). Not necessary to have GPU device on local.
2. set the system path for nvcc compiler.
3. run `nvcc -V` on you local machine to get the version.
4. run the `nsight` on you local, it will open the nsight ide in GUI.
5. ssh into remote the server and pull the docker image on remote server using: `docker pull kayush206/ssh-docker`
6. run the docker image on remote server using: `nvidia-docker run -d -p
:22 kayush206/ssh-docker`. replace the `` with the available port of remote(i.e `54321`).
#### Configuration:
1. Create a `CUDA C/C++ Project`:
2. Choose the project location on local as default and project type as `Empty Project` and click on `Next`.
2. Under the `Basic settings`, tick the `Generate PTX code` and `Generate GPU code` as `5.0` boxes and **untick the `2.0` boxes** (not supported with cuda-9.0) and click on `Next`.
3. Under `Target Systems`,click on `manage` and then click on `add` to add the remote connect details. replace the:
* `` by gpu host address,
* `` by port to which container is mapped(i.e. `54321`),
* set `User name` to `root`,
* set `Label` to `gpu-container`
and click on `Finish`.
4. Under `Target Systems`, Add the project location and Toolkit details for remote(`gpu-container`) And selcet the `x86_64` as CPU Architecture in `Local System` and `gpu-container`.
5. Copy all the `*.cu` and `*.h` files from the `src` to project.
6. Click on the `Run Configuration` from `Run` for configuring project in order to execute it on remote gpu server.
7. Right click on `C/C++ Remote Application` and choose `New`:
8. Click on the `Remote` tab and in `Remote Connection` choose `gpu-container`. And verify that `Remote toolkit` is exactly same as `/usr/local/cuda/bin`.
Edit the `Remote executable` by clicking `Run remote executable` to **`/root/nsight-workspace/hello_project/Debug/hello_project`** after done with editing, click back to `Upload local executable`.
9. In order to synchronize your project files between local and remote, select the `Set Active` to `gpu-container`.
And then, whenever you want to manually sync projecct files, click on `Sync Active Now`.
10. In order to build the project, choose `Clean...` and then `Build All` from `Project` tab.
11. click on the run button (green circle). if everything goes fine you will be able to see the output as details for you gpu devices.
#### References:
1. [Executing CUDA C++ Application on a remote machine using Nsight Eclipse](https://medium.com/@rajeshkumar/executing-cuda-c-application-on-a-remote-machine-using-nsight-eclipse-fb8364029625)
近期下载者：
相关文件：
收藏者：