5 ms·

But then writing something in C is not really difficult isnt it? (The real difficult part is the algorithm not the implementation in a language)

adamnemecek 6y ago
That really depends but on average it will be more difficult and there will be a lot of boiler plate.
stabbles 6y ago
I think you're overestimating how many Python developers are willing to write and compile C. Debugging gets more difficult across two languages. Shipping binaries with a Python package correctly can be a hassle too (what about -march=native performance).
- marmaduke 6y ago
  Numba compiles Python to LLVM as a backend, why is this even a discussion?
  - krastanov 6y ago
    Because numba (and jax), although being wonderful pieces of engineering, work on a fairly limited subset of python, do not permit the same level of introspection as python, and do not play nice with other python libraries. Julia does not make that sacrifice.
    - marmaduke 6y ago
      > do not play nice with other python libraries. Julia hasn't been around long enough to build ecosystem with multiple commerical giants building competing products. > Julia does not make that sacrifice. Sure, Julia sacrifices your sanity on the alter of stack traces, method resolution, time to first plot, among others. > fairly limited subset of python Fantastic comment, always brought up, except that this same subset of Python (really how to use CPU efficiently) is about the same that anyone writing about Julia performance is preaching.
      - DNF2 6y ago
        I beg your pardon. What parts of Julia are not supported by its jit compiler?
      - krastanov 6y ago
        I think a really good example of how Julian tools are composable and work with the whole of the language, while that is not the case with Pythonic tools, is autodifferentiation. The many different julian autodiff libraries have little trouble performing the autodiff through special functions, weird data structures, and 3rd party libraries. In python, none of the autodiff tools play nice with scipy or with mpmath or with sympy (or qutip, which is something I need). This is not about commercial competitors either - all of the libraries I mentioned are non-commercial. Julia provides the interoperability between such libraries, including custom datatypes, frequently with fast zero-cost abstractions.
        marmaduke 6y ago
        I agree but think that Julia had the benefit of watching Python and MATLAB suck at auto diff and do better as a community. That said PyTorch and Jax are great. > fast zero-cost abstraction Until you get a compiler error and the stack trace takes up a few screens of text. Who maintains that abstraction by the way? Nothing is free.
      - amkkma 6y ago
        >Fantastic comment, always brought up, except that this same >subset of Python (really how to use CPU efficiently) is ?about the same that anyone writing about Julia performance is >preaching. This is completely and demonstrably false. Julia allows fast programming with macros, multiple dispatch, abstract types and more.
        marmaduke 6y ago
        All those features have a cost that, in a performance oriented setting, require restricted the code to a subset of Julia that matches what the hardware is fast at.
    - datanecdote 6y ago
      For Jax I believe this is false. Jax is composable. In fact it’s a core design goal. Jax arrays implement the Numpy API. I routinely drop Jax arrays into other python libraries designed for Numpy. It works quite well. It’s not effortless 100% of the time but no library interop is (including Julia multiple dispatch). I can introspect Jax. Until you wrap your function with jit(foo) it’s as introspectable as any other Python code, at least if I’m understanding what you mean by introspection. Jax has implemented most of the Numpy functions, certainly most of the ones anyone needs to use on a regular basis. I rarely find anything missing. And if it is, I can write it myself, in python, and have it work seamlessly with the rest of Jax (autodiff, jit, etc)
      - oxinabox 6y ago
        Jax is awesome. But supporting most of numpy isn't enough. Because numpy isn't composable. You want to add banded-block-banded matrices to numpy? Then you need to fork numpy (or in this case fork jax); this is a package in julia and it works with everything. You want add names to your array dimentions like PyTorch recently did, then like PyTorch you need to fork numpy; again this is package in julia. You want to do both? You have to merge those two forks into each other. In julia this isn't even a package this is just using the aformentioned two packages. You want to work with Units or track Measurment error (or both?). Basically same story. Except better in some ways worse in others. Better because you don't have to fork numpy, it is extensible enough to allow that. Packages exist that use that etendability for exactly that. Worse because those are scalar types, why are you even having to write code to deal with array support at all. Agian 2 julia packages and they don't even mention arrays internally. The problem's not Jax. The problem is numpy. Or rather the problem is this level of composability is really hard most of the time in most languages (including the python + C combo. Especially so even). Its true that this is not always trivial 100$% of the time with julia's multiple dispatch. but it is truer there than anywhere else i have seen.
  - stabbles 6y ago
    A subset of Python for one, and debugging stays awkward with gdb. Makes me think of this little pun: https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L461-L474 https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L...
    - marmaduke 6y ago
      The subset of Python compatible with Julia's whole performance story (mostly, structs are the missing piece), yes sure.
      - DNF2 6y ago
        You mean the type system and multiple dispatch? I guess those are fairly minor points, yes.
        DNF2 6y ago
        One performance feature I find really nice in Julia, is that you can write your own function (cost function, function to integrate, whatever) that you can pass into a solver/integrator/etc. Then it will be inlined and compiled into that external library. Does this work with numba? Are your jit-compiled functions compiled into external modules?
      - amkkma 6y ago
        Even if structs worked, they wouldn't be part of a good type system, don't work with abstract interfaces/inheritance,traits, multiple dispatch, fast union types, zero allocation immutables or any myriad of other things
  - sgtnoodle 6y ago
    Numba isn't as slam dunk of a solution as it seems, at least when your code needs to run on something besides x86. I recently witnessed someone getting numba based code running on an ARM based system, and they fought llvm package and performance issues for weeks. The code in question was generally working and seemed pretty good on x86, but took up multiple CPU cores at 100% on the ARM platform. Doing a straight forward port of the algorithm to C++ and wrapping it with pybind11 resulted in the same algorithm using 14% of one CPU core. Perhaps the numba implementation could have been made to work eventually, but it seemed pretty difficult to debug due to all the layers involved.
- deleted 6y ago
  [deleted]
- edsac_xyzw 6y ago
  That is not too hard if one uses Pybind11 C++ library, which allows creating native code Python modules (shared libraries) in a simple and declarative manner. Other library that can be used with Pybind11 is Eigen for linear algebra that can perform loop fusion in numerical computation, for instance, it can add multiple vectors of same size in a single loop.
StefanKarpinski 6y ago
I'm a pretty competent C programmer, but doing even fairly simple matrix programming in C is fiendishly hard not to screw up. When I was porting this code [1] to this [2], it took an absurdly long time to iron out all the bugs. And just look at the difference in verbosity and difficulty. Worst of all, C has no guard rails: it's so easy to think you have it right, only to realize much later (or not at all, frankly) that you're computing complete nonsense. [1] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3b604c47aa0da767cc97bbea9f64151/perf.jl#L128-L148 https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3... [2] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3b604c47aa0da767cc97bbea9f64151/perf.c#L135-L209 https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3...
- andi999 6y ago
  Thanks for the example. Could you also tell how much the speedup of this led to?
  - StefanKarpinski 6y ago
    That C code is 39% faster than the Julia version, so faster but not massively so. You can optimize the Julia code to be as fast as the C code by using in-place operations, but it's probably not worth if for such a small speedup unless the code is a real bottleneck. By comparison, the Python version (using NumPy, of course) is 25x slower than C, which is a whole different situation. Most the speed advantage of the C here is due to reusing the same memory over and over, which you can do pretty easily in Julia as well. Here's the Julia code using in-place operations and it's still quite a bit more readable than the C version: https://gist.github.com/StefanKarpinski/e57f5a36b7890b261a0d42db4eb1324c https://gist.github.com/StefanKarpinski/e57f5a36b7890b261a0d.... I timed this and it's the same speed as the C version. When developing this, it follows the same general outline as the C version, but you have several benefits: (1) You can use asserts to compare to the easy version; (2) There are niceties like bounds checks and array indexing. In C you can't do (1) because there is on easy version to compare with. And doing the array index computations in C is kind of a nightmare—it's so easy to accidentally screw them up.

adamnemecek 6y ago

That really depends but on average it will be more difficult and there will be a lot of boiler plate.

stabbles 6y ago

I think you're overestimating how many Python developers are willing to write and compile C. Debugging gets more difficult across two languages. Shipping binaries with a Python package correctly can be a hassle too (what about -march=native performance).

marmaduke 6y ago
Numba compiles Python to LLVM as a backend, why is this even a discussion?
- krastanov 6y ago
  Because numba (and jax), although being wonderful pieces of engineering, work on a fairly limited subset of python, do not permit the same level of introspection as python, and do not play nice with other python libraries. Julia does not make that sacrifice.
  - marmaduke 6y ago
    > do not play nice with other python libraries. Julia hasn't been around long enough to build ecosystem with multiple commerical giants building competing products. > Julia does not make that sacrifice. Sure, Julia sacrifices your sanity on the alter of stack traces, method resolution, time to first plot, among others. > fairly limited subset of python Fantastic comment, always brought up, except that this same subset of Python (really how to use CPU efficiently) is about the same that anyone writing about Julia performance is preaching.
    - DNF2 6y ago
      I beg your pardon. What parts of Julia are not supported by its jit compiler?
    - krastanov 6y ago
      I think a really good example of how Julian tools are composable and work with the whole of the language, while that is not the case with Pythonic tools, is autodifferentiation. The many different julian autodiff libraries have little trouble performing the autodiff through special functions, weird data structures, and 3rd party libraries. In python, none of the autodiff tools play nice with scipy or with mpmath or with sympy (or qutip, which is something I need). This is not about commercial competitors either - all of the libraries I mentioned are non-commercial. Julia provides the interoperability between such libraries, including custom datatypes, frequently with fast zero-cost abstractions.
      - marmaduke 6y ago
        I agree but think that Julia had the benefit of watching Python and MATLAB suck at auto diff and do better as a community. That said PyTorch and Jax are great. > fast zero-cost abstraction Until you get a compiler error and the stack trace takes up a few screens of text. Who maintains that abstraction by the way? Nothing is free.
    - amkkma 6y ago
      >Fantastic comment, always brought up, except that this same >subset of Python (really how to use CPU efficiently) is ?about the same that anyone writing about Julia performance is >preaching. This is completely and demonstrably false. Julia allows fast programming with macros, multiple dispatch, abstract types and more.
      - marmaduke 6y ago
        All those features have a cost that, in a performance oriented setting, require restricted the code to a subset of Julia that matches what the hardware is fast at.
  - datanecdote 6y ago
    For Jax I believe this is false. Jax is composable. In fact it’s a core design goal. Jax arrays implement the Numpy API. I routinely drop Jax arrays into other python libraries designed for Numpy. It works quite well. It’s not effortless 100% of the time but no library interop is (including Julia multiple dispatch). I can introspect Jax. Until you wrap your function with jit(foo) it’s as introspectable as any other Python code, at least if I’m understanding what you mean by introspection. Jax has implemented most of the Numpy functions, certainly most of the ones anyone needs to use on a regular basis. I rarely find anything missing. And if it is, I can write it myself, in python, and have it work seamlessly with the rest of Jax (autodiff, jit, etc)
    - oxinabox 6y ago
      Jax is awesome. But supporting most of numpy isn't enough. Because numpy isn't composable. You want to add banded-block-banded matrices to numpy? Then you need to fork numpy (or in this case fork jax); this is a package in julia and it works with everything. You want add names to your array dimentions like PyTorch recently did, then like PyTorch you need to fork numpy; again this is package in julia. You want to do both? You have to merge those two forks into each other. In julia this isn't even a package this is just using the aformentioned two packages. You want to work with Units or track Measurment error (or both?). Basically same story. Except better in some ways worse in others. Better because you don't have to fork numpy, it is extensible enough to allow that. Packages exist that use that etendability for exactly that. Worse because those are scalar types, why are you even having to write code to deal with array support at all. Agian 2 julia packages and they don't even mention arrays internally. The problem's not Jax. The problem is numpy. Or rather the problem is this level of composability is really hard most of the time in most languages (including the python + C combo. Especially so even). Its true that this is not always trivial 100$% of the time with julia's multiple dispatch. but it is truer there than anywhere else i have seen.
- stabbles 6y ago
  A subset of Python for one, and debugging stays awkward with gdb. Makes me think of this little pun: https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L461-L474 https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L...
  - marmaduke 6y ago
    The subset of Python compatible with Julia's whole performance story (mostly, structs are the missing piece), yes sure.
    - DNF2 6y ago
      You mean the type system and multiple dispatch? I guess those are fairly minor points, yes.
      - DNF2 6y ago
        One performance feature I find really nice in Julia, is that you can write your own function (cost function, function to integrate, whatever) that you can pass into a solver/integrator/etc. Then it will be inlined and compiled into that external library. Does this work with numba? Are your jit-compiled functions compiled into external modules?
    - amkkma 6y ago
      Even if structs worked, they wouldn't be part of a good type system, don't work with abstract interfaces/inheritance,traits, multiple dispatch, fast union types, zero allocation immutables or any myriad of other things
- sgtnoodle 6y ago
  Numba isn't as slam dunk of a solution as it seems, at least when your code needs to run on something besides x86. I recently witnessed someone getting numba based code running on an ARM based system, and they fought llvm package and performance issues for weeks. The code in question was generally working and seemed pretty good on x86, but took up multiple CPU cores at 100% on the ARM platform. Doing a straight forward port of the algorithm to C++ and wrapping it with pybind11 resulted in the same algorithm using 14% of one CPU core. Perhaps the numba implementation could have been made to work eventually, but it seemed pretty difficult to debug due to all the layers involved.
deleted 6y ago
[deleted]
edsac_xyzw 6y ago
That is not too hard if one uses Pybind11 C++ library, which allows creating native code Python modules (shared libraries) in a simple and declarative manner. Other library that can be used with Pybind11 is Eigen for linear algebra that can perform loop fusion in numerical computation, for instance, it can add multiple vectors of same size in a single loop.

StefanKarpinski 6y ago

I'm a pretty competent C programmer, but doing even fairly simple matrix programming in C is fiendishly hard not to screw up. When I was porting this code [1] to this [2], it took an absurdly long time to iron out all the bugs. And just look at the difference in verbosity and difficulty. Worst of all, C has no guard rails: it's so easy to think you have it right, only to realize much later (or not at all, frankly) that you're computing complete nonsense. [1] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3b604c47aa0da767cc97bbea9f64151/perf.jl#L128-L148 https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3... [2] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3b604c47aa0da767cc97bbea9f64151/perf.c#L135-L209 https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3...

andi999 6y ago
Thanks for the example. Could you also tell how much the speedup of this led to?
- StefanKarpinski 6y ago
  That C code is 39% faster than the Julia version, so faster but not massively so. You can optimize the Julia code to be as fast as the C code by using in-place operations, but it's probably not worth if for such a small speedup unless the code is a real bottleneck. By comparison, the Python version (using NumPy, of course) is 25x slower than C, which is a whole different situation. Most the speed advantage of the C here is due to reusing the same memory over and over, which you can do pretty easily in Julia as well. Here's the Julia code using in-place operations and it's still quite a bit more readable than the C version: https://gist.github.com/StefanKarpinski/e57f5a36b7890b261a0d42db4eb1324c https://gist.github.com/StefanKarpinski/e57f5a36b7890b261a0d.... I timed this and it's the same speed as the C version. When developing this, it follows the same general outline as the C version, but you have several benefits: (1) You can use asserts to compare to the easy version; (2) There are niceties like bounds checks and array indexing. In C you can't do (1) because there is on easy version to compare with. And doing the array index computations in C is kind of a nightmare—it's so easy to accidentally screw them up.