4 ms·

Which one?

chrisseaton 9y ago
I would imagine Nuitka is slower, because it cannot apply dynamic optimisations which are the key technique for making a language like Python fast, but more predictable and regular as the dynamic optimisations that PyPy uses are vulnerable to performance cliffs.
- radarsat1 9y ago
  Are there any profile-guided optimisation methods for Python? Like, something that saves information from PyPy's dynamic optimisations on first run and uses them later for an AOT approach?
- stevedonovan 9y ago
  Yes, compare to LuaJIT. On a good day, it can bite C's shiny metal ass, but consistent and reliable application performance requires intimate knowledge of those performance cliffs.
  - Sean1708 9y ago
    > it can bite C's shiny metal ass Can it actually? I've certainly seen benchmark's where a JITed program is comparable/marginally faster than a C program, but I've never seen one where it outright trounces C.
    - xapata 9y ago
      Yes. A jit compiler can compile based on both code and data, and can compile all the dependencies. In contrast an aot compiler must only use the code and links to other pre-compiled binaries. Further, a jit can compile to specific hardware. An aot must anticipate what hardware is possible. If you aot compile to several possible hardware configs, then you'll bloat the binary.
      - MaxBarraclough 9y ago
        > In contrast an aot compiler must only use the code Not since the development of "profile-guided optimisation", no.
        xapata 9y ago
        Ok, then you've got the code and the profile. The actual data and hardware it happens to be using is still better information.
        MaxBarraclough 9y ago
        > The actual data and hardware it happens to be using is still better information. That's certainly what HotSpot's promoters want us to believe, but I'm not convinced there's a lot of extra value in having detailed data on the current run, compared to 'static' profile-guided optimisation. I'm not sure if there's been any serious study of this question. As to the specific hardware: this might matter for things like SIMD, yes, but often won't make any appreciable difference. Some Linux distros have the user compile almost everything locally, but aren't all that much faster for it.
      - Sean1708 9y ago
        Which is great in theory, but what was intending to ask was are there any actual examples of JITed programs which beat C by a large margin? Because, as I say, I've only ever seen ones which are comparable to C.
        xapata 9y ago
        Trouble is, you don't tend to write a big program in two languages the exact same way. The rewrite nearly always wins because of algorithm improvements and cleaning cruft. So it's hard to benchmark. If two small programs are about the same speed in C vs JITlang, then the theory says the JITted one should take over as the program gets bigger.

Which one?

I would imagine Nuitka is slower, because it cannot apply dynamic optimisations which are the key technique for making a language like Python fast, but more predictable and regular as the dynamic optimisations that PyPy uses are vulnerable to performance cliffs.

radarsat1 9y ago
Are there any profile-guided optimisation methods for Python? Like, something that saves information from PyPy's dynamic optimisations on first run and uses them later for an AOT approach?
stevedonovan 9y ago
Yes, compare to LuaJIT. On a good day, it can bite C's shiny metal ass, but consistent and reliable application performance requires intimate knowledge of those performance cliffs.
- Sean1708 9y ago
  > it can bite C's shiny metal ass Can it actually? I've certainly seen benchmark's where a JITed program is comparable/marginally faster than a C program, but I've never seen one where it outright trounces C.
  - xapata 9y ago
    Yes. A jit compiler can compile based on both code and data, and can compile all the dependencies. In contrast an aot compiler must only use the code and links to other pre-compiled binaries. Further, a jit can compile to specific hardware. An aot must anticipate what hardware is possible. If you aot compile to several possible hardware configs, then you'll bloat the binary.
    - MaxBarraclough 9y ago
      > In contrast an aot compiler must only use the code Not since the development of "profile-guided optimisation", no.
      - xapata 9y ago
        Ok, then you've got the code and the profile. The actual data and hardware it happens to be using is still better information.
        MaxBarraclough 9y ago
        > The actual data and hardware it happens to be using is still better information. That's certainly what HotSpot's promoters want us to believe, but I'm not convinced there's a lot of extra value in having detailed data on the current run, compared to 'static' profile-guided optimisation. I'm not sure if there's been any serious study of this question. As to the specific hardware: this might matter for things like SIMD, yes, but often won't make any appreciable difference. Some Linux distros have the user compile almost everything locally, but aren't all that much faster for it.
    - Sean1708 9y ago
      Which is great in theory, but what was intending to ask was are there any actual examples of JITed programs which beat C by a large margin? Because, as I say, I've only ever seen ones which are comparable to C.
      - xapata 9y ago
        Trouble is, you don't tend to write a big program in two languages the exact same way. The rewrite nearly always wins because of algorithm improvements and cleaning cruft. So it's hard to benchmark. If two small programs are about the same speed in C vs JITlang, then the theory says the JITted one should take over as the program gets bigger.