Comments (1)
tbennun
commented on May 24, 2024
The compilation to LLVM IR is project-specific, due to makefiles and compiler compatibility. The way we generated Linux kernel LLVM IR files was very similar to yours - we switched the compiler to clang, and when building the kernel we logged all commands (verbose mode). Following that, we swapped every -o <file.o> with -S -emit-llvm -o <file.ll> in clang build commands.
I do not exactly know why you are able to generate more LLVM-IR files, perhaps the script you found is better at detecting build commands, or (which is more likely IMO) your kernel configuration included additional kernel modules that we did not compile.
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Related Issues (20)
- train_inst2vec.py fails on specific file during vocabulary building HOT 11
- [train_task_classifyapp.py] GPU memory grows unlimitedly HOT 9
- loss and acc have large differences even though train and valid are set totally same HOT 4
- test
- Asm inline call handling HOT 1
- question about predict value p at train_task_classifyapp.py line 417 HOT 2
- Train task classifyapp on same data as for training the embedding HOT 2
- ValueError: GraphDef cannot be larger than 2GB. HOT 1
- Bug of Regular Express matching Report For Preprocessing LLVM IR HOT 1
- 'MultiDiGraph' object has no attribute 'node' HOT 3
- Confusion in inst2vec_preprocess.py when reading code HOT 6
- The original source code of the datasets HOT 3
- Expected combineable dataset HOT 1
- for classifyapp, vocubalary dictionary is not present. HOT 1
- How to get the embedding result of inst2vec ? HOT 3
- the links to all the datasets did not work. HOT 3
- dictionary_pickle not available HOT 3
- [inst2vec_evaluate.py] IndexError : list index out of range in analogies HOT 1
- The link to the dataset is not working HOT 2
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