4 ms·

No citation needed as long as you understand both languages' runtime stack. Python is an interpreted language that runs on top of a virtual machine (usually CP

by wting 14y ago

No citation needed as long as you understand both languages' runtime stack. Python is an interpreted language that runs on top of a virtual machine (usually CPython). Types are determined at run time. .py -> .pyo* (bytecode) -> VM -> OS On the other hand, Go (and other C languages) have a different runtime stack: .go -> binary* -> OS Between source and binary are a bunch of compiler steps[0], but * is where both binaries are executed. Statically compiled languages do not have to determine types at runtime and therefore are able to optimize code paths much better. Still don't believe me? Here's a Fibonacci calculation micro benchmark[1] I ran: ╭─ting@noa /tmp/fib ‹python-2.7.3› ‹ruby-1.9.3› ╰─➤ time ./go 1346269 real 0.02s user 0.01s sys 0.00s ╭─ting@noa /tmp/fib ‹python-2.7.3› ‹ruby-1.9.3› ╰─➤ time python3 ./fib.py 1346269 real 0.61s user 0.60s sys 0.00s Sidenote: Java runs on a virtual machine (JVM) but it's performance comes very close to C languages due to static typing, JIT compilation, and heavy investment in the JVM from many companies. [0]: For C, compilation goes through these steps: hello.c (preprocessor) hello.tmp (compiler) hello.s (assembler) hello.o (linker) hello <-- binary to run [1]: https://gist.github.com/wting/77c9742fa1169179235f https://gist.github.com/wting/77c9742fa1169179235f

dilap 14y ago
Dynamically-typed languages can be fast when run with a jit -- for example, Lua+luajit is close to Go in your microbenchmark. On my computer, for n=40, Go is 0m2.156s and luajit is 0m3.199s.
- wting 14y ago
  "Fast" and "close" are subjective terms. That's a 50% increase for a few thousand function calls. PyPy uses JIT to improve Python run time speeds but it's still magnitudes slower than statically typed languages. I've upped n to 40 and rerun with the following languages: C: 0.38s Java: 0.55s Go: 0.90s Rust: 1.29s LuaJit: 2.19s Haskell: 8.97s PyPy: 10.06s Lua: 22.87s Ruby: 22.13s Python2: 43.88s Python3: 66.28s All code is available in the previous mentioned gist: https://gist.github.com/wting/77c9742fa1169179235f https://gist.github.com/wting/77c9742fa1169179235f
  - cygwin98 14y ago
    Can't imagine Ruby is twice as fast as Python 2 and three times as fast as Python 3 right now. Can you share your code in a gist?
  - smosher 14y ago
    I'm curious what times you get with an iterative version, or at least using a LUT. As-is, this mostly benchmarks the stack (admittedly that is an interesting datapoint.)
  - dilap 14y ago
    Thanks for the extra results. Obviously, a single micro-benchmark will only take you so far (and something like the Computer Language Shootout gets you farther -- it's a shame and mystifying (to me) that that site no longer has results for LuaJIT...). But anyway, in my (limited) experimentations with LuaJIT, it's often been within a factor of 2x-3x of speed of C, which to me is pretty fast, and typical of many statically-typed, compiled languages.
  - dilap 14y ago
    I don't know if anyone will ever read this thread again :), but just in case, the the current front-page post on Julia provides another nice example of a fast, dynamically-typed, JITed language (within 1-2x of C, from their own set of benchmarks).
pjmlp 14y ago
Someone just mixed languages with implementation. C interpreter -> http://root.cern.ch/drupal/content/cint http://root.cern.ch/drupal/content/cint Java compiler to native code -> http://www.excelsior-usa.com/jet.html http://www.excelsior-usa.com/jet.html
- wting 14y ago
  Yes, if you want to get pedantic about it languages are separate from implementation. For example there's CPython, Cython, PyPy, Jython, IronPython. However, reality is that most languages' ecosystems and performances are tightly connected to one or two implementations.
  - pjmlp 14y ago
    I tend to get pedantic about it given my background in compiler design. I find sad that young generations mix languages with implementations and get wrong concepts that a certain language can only be implemented in a specific way.
    - burntsushi 14y ago
      > I find sad that young generations mix languages with implementations and get wrong concepts that a certain language can only be implemented in a specific way. Yes, but the parent that you responded to wasn't really susceptible to this. It's quite natural to speak of the "performance properties of Language X" as if you were to say, "the performance properties of the most widely used implementation of Language X." English doesn't lend itself well to precision. Therefore, people rely on the ability of others to use contextual clues to infer assumptions. It's pretty clear in this case what point the parent was trying to convey. And I'm not sure what youth has to do with any of this.
      - pjmlp 14y ago
        > And I'm not sure what youth has to do with any of this. I am already old enough to have coded Z80 assembly back in the day and I see this mix of languages and implementations mostly around youth wannabe programmers.

by wting 14y ago

dilap 14y ago

Dynamically-typed languages can be fast when run with a jit -- for example, Lua+luajit is close to Go in your microbenchmark. On my computer, for n=40, Go is 0m2.156s and luajit is 0m3.199s.

wting 14y ago
"Fast" and "close" are subjective terms. That's a 50% increase for a few thousand function calls. PyPy uses JIT to improve Python run time speeds but it's still magnitudes slower than statically typed languages. I've upped n to 40 and rerun with the following languages: C: 0.38s Java: 0.55s Go: 0.90s Rust: 1.29s LuaJit: 2.19s Haskell: 8.97s PyPy: 10.06s Lua: 22.87s Ruby: 22.13s Python2: 43.88s Python3: 66.28s All code is available in the previous mentioned gist: https://gist.github.com/wting/77c9742fa1169179235f https://gist.github.com/wting/77c9742fa1169179235f
- cygwin98 14y ago
  Can't imagine Ruby is twice as fast as Python 2 and three times as fast as Python 3 right now. Can you share your code in a gist?
- smosher 14y ago
  I'm curious what times you get with an iterative version, or at least using a LUT. As-is, this mostly benchmarks the stack (admittedly that is an interesting datapoint.)
- dilap 14y ago
  Thanks for the extra results. Obviously, a single micro-benchmark will only take you so far (and something like the Computer Language Shootout gets you farther -- it's a shame and mystifying (to me) that that site no longer has results for LuaJIT...). But anyway, in my (limited) experimentations with LuaJIT, it's often been within a factor of 2x-3x of speed of C, which to me is pretty fast, and typical of many statically-typed, compiled languages.
- dilap 14y ago
  I don't know if anyone will ever read this thread again :), but just in case, the the current front-page post on Julia provides another nice example of a fast, dynamically-typed, JITed language (within 1-2x of C, from their own set of benchmarks).

pjmlp 14y ago

Someone just mixed languages with implementation. C interpreter -> http://root.cern.ch/drupal/content/cint http://root.cern.ch/drupal/content/cint Java compiler to native code -> http://www.excelsior-usa.com/jet.html http://www.excelsior-usa.com/jet.html

wting 14y ago
Yes, if you want to get pedantic about it languages are separate from implementation. For example there's CPython, Cython, PyPy, Jython, IronPython. However, reality is that most languages' ecosystems and performances are tightly connected to one or two implementations.
- pjmlp 14y ago
  I tend to get pedantic about it given my background in compiler design. I find sad that young generations mix languages with implementations and get wrong concepts that a certain language can only be implemented in a specific way.
  - burntsushi 14y ago
    > I find sad that young generations mix languages with implementations and get wrong concepts that a certain language can only be implemented in a specific way. Yes, but the parent that you responded to wasn't really susceptible to this. It's quite natural to speak of the "performance properties of Language X" as if you were to say, "the performance properties of the most widely used implementation of Language X." English doesn't lend itself well to precision. Therefore, people rely on the ability of others to use contextual clues to infer assumptions. It's pretty clear in this case what point the parent was trying to convey. And I'm not sure what youth has to do with any of this.
    - pjmlp 14y ago
      > And I'm not sure what youth has to do with any of this. I am already old enough to have coded Z80 assembly back in the day and I see this mix of languages and implementations mostly around youth wannabe programmers.