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.