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# System prepended metadata

title: CSCI 699

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CSCI 699
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Test approaches
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1. C++ infunction timing. Calculate the actual time used when performing the calculation.
    - might be ignoring other factors
2. Js wrap timer,(or by using CL tools), calculate the time used for the whole program.
    - might be affected by memory allocation and releasement time.
3. 
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Tasks tobe tested
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1. AI/ML related tasks
    - Tensorflow lite compilation to wasm
        - TFlite is compile in bazel, which is super complex and is not modifiable.
        - It is the performance testing of the whole library.
    - Pytorch model -> torch script -> ONNX -> C++ runtime
        - Uses libtorch, and torch script to compile translate the model.
        - Hard to explain the actual implementation optimization or reasoning of the performance.
        - Same issue, performance testing of the whole pytorch library. (There are some prebuilt version)
    - ONNX model -> ONNC backend -> C++ runtime
        - Much lower level, takes time to look into the implementation of the source code.
        - Relatively small project, prob. is not super optimized, but not really effecting the mass usage.


