• mpiPi.h: Some important global information is located in the mpiPi_t structure.

• mpiPi.c: The main operating logic of mpiP.

• make-wrappers.py: Add customized operations after intercepting MPI.

• mpiP-wrappers.c: Wrapped MPI generated by make-wrappers.py. Do not modify it directly.

• trace2symbol: Remove some redundant information.

• order: Reorder events.

Train.py: Data preprocessing (processing each categorical feature field into global index for embedding table, numerical feature discretization), model training.

Replay.py: Predict and simulate MPI event sequence.

Combine.py: Organize and concatenate multiple .order files.

MPI_define.py: Define MPI-related information.

Model_LSTM.py: LSTM model.

Trace_Dataset.py: Dataset class for processing trace data.

TraceStat.py: Record the statistical information in the trace, including the amount of data sent and received between processes, computation time and communication time, etc.

1. Compile mpiP and dependent libraries to generate libmpiP.so.

2. Compile the target application, add -lmpiP after -lmpi. An alternative way is runtime instrument via export LD_PRELOAD=[path to mpiP]/libmpiP.so

3. Compile trace2symbol and order programs in dwarfcode.

4. Run the target application, generate np trace files [0:np].trace, and put them into the trace_data folder.

5. Run trace2symbol 0.trace 0.symbol np, generate [0:np].symbol

6. Run order 0.symbol 0.order np, generate [0:np].order

7. Run Combine.py, generate train_dataset.csv

8. Run Train.py

9. Run Replay.py