https://github.com/eedalong/ece408

Code base and slides for ECE408：Applied Parallel Programming On GPU.
https://github.com/eedalong/ece408

ece408 gpu-programming machine-learning parallel-programming

Last synced: 12 months ago
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Code base and slides for ECE408：Applied Parallel Programming On GPU.

• Host: GitHub
• URL: https://github.com/eedalong/ece408
• Owner: eedalong
• Created: 2021-04-17T11:56:21.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2021-07-02T06:51:36.000Z (over 4 years ago)
• Last Synced: 2025-04-13T01:06:23.285Z (12 months ago)
• Topics: ece408, gpu-programming, machine-learning, parallel-programming
• Language: C++
• Homepage: http://ece408.hwu-server2.crhc.illinois.edu/_layouts/15/start.aspx#/SitePages/Home.aspx
• Size: 35.6 MB
• Stars: 122
• Watchers: 1
• Forks: 34
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
## Code Project For Course ECE408

This is code project for ECE408(Applied Parallel Programming). And you can use this project as a good start for your own lab implementation. Here's how you finish your own project. [You can get all slices here](http://ece408.hwu-server2.crhc.illinois.edu/_layouts/15/start.aspx#/SitePages/Home.aspx).

### 1. Finish your own code based on template code

`templates/mpx_template.cu` are template codes and you need to full fill these code files. For me, I add these implementations in `mpx_implement.cu`.

### 2. Add your code to CmakeLists.txt

Once you finish your new implementation code, you need to add it to `CmakeLists.txt` so it can be compiled.

    

    // mpx_implement.cu is your implementation code file name.

    CUDA_ADD_EXECUTABLE(MPX_Implement mpx_implement.cu ${wbhdr} ${wbsrc})

**NOTICE:** In `CmakeLists.txt`, this line set GPU arch and code generation configuration. Change it if your GPU arch is not `sm_86`.

![](imgs/archs.png)

### 2. Compile & Run

    mkdir build

    cd build

    cmake ..

    make 

Executable files will be generated in under `build/`, and any time when you update your implementation you just need to rerun `make` under `build/`. But if you update your `CmakeLists.txt`, you should run `cmake ..` first and then run `make` to compile all codes.

## Test Dataset

datasets for code test are under `test_data/`, you can use them to test your implementation with

     

     ./program -e  -i , -o  -t 

The `` and `` are the input and output files provided in the dataset. The `` is the location you’d like to place the output from your program. The`` is the output file type: `vector`, `matrix`, or `image`. If an MP does not expect an input or output, then pass none as the parameter.

For example, if you want test mp1_implementatiton, you can run

    

    #suppose you are in ./build directory

    ./MP1_Implement -e ../test_data/mp01/0/output.raw -i ../test_data/mp01/0/input0.raw,../test_data/mp01/0/input1.raw -o ../test_data/mp01/0/res.raw -t vector 

And if you see logs like this, then congratulations, you have passed the test!

![](imgs/correct_res.png)

## Generate your own dataset

You can generate your own dataset if you want more test. For example, you can use `MP2_Dataset` to generate dataset.

    MP2_Dataset -d /data/directory/ -ra 100 -ca 128 -rb 128 -cb 256