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Profiling CLE, pyelftools, and pefile #231
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Here are some preliminary findings:
I profiled PE loading back in summer 2017 and found that the same thing applied to pefile as it does to pyelftools - the hot functions are all struct parsing and this is already highly optimized. The big difference between our use of pefile vs pyelftools is that we use pefile as much more of a monolity, whereas we use pyelftools as a parsing toolkit. It might be possible to remove some unnecessary parsing if we look more carefully into how to use pefile efficiently. |
Are you using load_debug_info=True? If so, are you using the latest pyelftools master? Recently a PR added a cache for DIU I believe, which sped up DWARF loading for me a lot. I was thinking of monkeypatching the struct loading code in pyelftools in CLE using a C-backed implementation. What do you think? |
all of my tests were with load_debug_info=False. I think your idea could maybe work but we would need to read the entire file into memory first and I don't really know how we would keep track of that. |
also which level of abstraction were you thinking of monkeypatching pyelftools at? I can't seem to find a level in between "redo the whole gigantic mess" and "so small I don't think it would help anything" |
I'm thinking of moving |
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One of the timeout binaries that we definitely want to be able to load: asterisk.zip |
Loading binaries is taking longer and longer since recent updates in CLE, pyelftools, and pefile. Profiling them is the first step to make things faster.
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