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Unless I'm misunderstanding, this seems to be writing python extensions in Julia not C. So quite a misleading title. Also why should I pick this over cython, p

Unless I'm misunderstanding, this seems to be writing python extensions in Julia not C. So quite a misleading title. Also why should I pick this over cython, pythran or numba? with those I don't need to learn how to optimise another language (and no just writing Julia code does not necessarily get you large speed ups and certainly not the speed of C in many cases). I really wish the Julia community would stop overselling the language.

adgjlsfhk1 4y ago
Do you have any source on well written julia being any slower than well written C?
- bigdict 4y ago
  https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/julia-gcc.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
  - webshit2 4y ago
    It says right there: "Always look at the source code" fannkuch-redux, n-body, spectral-norm, reverse-complement: the fastest C wins by a large margin because it's an unreadable mess of vector intrinsics. If you really wanted to, you can do that in Julia too (and it might read better). Julia looks pretty even with C in the ones that were faithfully translated.
- jakobnissen 4y ago
  Don't be unreasonable. Numerical Julia code is just as fast, precisely because it's easy to design efficient high-level APIs and let the compiler optimise it. Try timing Julia's IO, or it's string processing, or it's hash tables, or its array allocation or it's GC - you know, nearly everything other that mathematical operations on floats or ints. Nearly everything leaves performance on the table. Sure Julia is fast, but not on a C level in these areas. There is still lots of room for optimisation, though.
  - jkrubin 4y ago
    What’s your point?
    - anewhnaccount2 4y ago
      The point is that these things (string processing/hash tables) are typically done using custom/tuned code in C and so would be faster than the generic, and not particularly carefully tuned Julia implementations. However, given this library seems to be about passing NumPy arrays to/from Julia, the application is almost certainly to expose numerical stuff from Julia to Python. Basically, the use case is similar to Numba, but it works by producing a C-extension from a Julia system image (if I understand correctly). The main advantage over something like Numba would be access to the Julia ecosystem.
    - jakobnissen 4y ago
      The point is that when adgjlsfhk1 doubts that Julia is slower than C, he does so from a background where "data" is a synonym for arrays of numbers, and "computing" means processing these arrays. Julia shines here, precisely because the people developing Julia have a similar focus. In contrast, if your performance bottlenecks are IO, parsing and hash table operations, then Julia's performance will be in par with Python and get absolutely crushed by Rust (I have less experience with C, but would imagine Rust and C are about equally fast). The major difference with Python is that Julia's implementation of these things _could_ be fast in a way that Python's can't. They just aren't.
      - adgjlsfhk1 4y ago
        on par with python is way off. Julia in these areas definitely isn't fully optimized, but the performance will be much closer to something like Java for these cases. a dict in Julia will still have 1 pointer indirect compared to the python one where all your objects have 16 bytes overhead and an extra pointer indirection.
        jakobnissen 4y ago
- cycomanic 4y ago
  I did a comparison of Julia vs numpy, cython and pythran [1] some time ago, for a typical dsp routine we use in our work, and Julia was quite a bit slower than the alternatives. Now I'm by no means a Julia expert so I might have missed an optimisation opportunity (although I posted this and nobody could point to something obvious) , however the whole advertisement behind Julia is that one gets essentially C speed with the ease of Python. That's actually my main gripe with Julia, the community advertises it as something that it isn't. I think otherwise some really cool stuff is happening in Julia Land. [1] https://jochenschroeder.com/blog/articles/DSP_with_Python2/ https://jochenschroeder.com/blog/articles/DSP_with_Python2/
  - meta2meta 4y ago
    You should run Julia code directly to benchmark instead of calling it in Python through pyjulia.
    - cycomanic 4y ago
      Actually I benchmarked inside Julia as well and didn't see a difference so left it like this.
  - salty_biscuits 4y ago
    I only had a quick look, but I would guess the slicing is what makes it slower than you would expect. Julia copies on an array slice, but you can use the @view macro or do some loops with a preallocated array to make it faster.
    - cycomanic 4y ago
      Thanks I'll have a look and post and update sometime later.
  - webshit2 4y ago
    wxy[:,:,k] += mu*conj(err[i,k])*X This should probably be wxy[:,:,k] .+= mu*conj(err[i,k]).*X so allocations are avoided. This doubled the speed for me, although I don't what size inputs are realistic. The @benchmark and @profile macros are good for this stuff.
pizza 4y ago
This is why it's a C extension: https://github.com/Suzhou-Tongyuan/jnumpy/blob/main/TyPython/src/C.jl https://github.com/Suzhou-Tongyuan/jnumpy/blob/main/TyPython... The title is correct because it is a Julia numpy interface. Inside it seems is another project, TyPython, that provides an efficient Julia-Python-Numpy bridge, via C extensions.
- anewhnaccount2 4y ago
  Agreed that the title is technically accurate. Julia should also be in the title, but this is possibly an oversight. It seems a bit unnecessary for the GP to assume that this is a bad faith attempt to mislead and insert Julia where it is not welcome rather than a potentially useful extra tool for writing high performance code. Note that the author/poster is probably not a native English speaker.
- cycomanic 4y ago
  I would argue that it might technically be correct (and admittedly that's the best type of correct) in the strict sense, it is also misleading. If I read that I'm writing C extensions in minutes I at least expect that the extension compiles to C (which the Julia code doesn't).
  - crabbygrabby 4y ago
    Definitely misleading given all the bloat and baggage adding Julia as a dependency to a project can have. It's the heaviest glue ever.
anewhnaccount2 4y ago
> Also why should I pick this over cython, pythran or numba? One scenario would be that there is code already written in Julia, which you would like to expose to a broader audience. Although overall Julia's ecosystem is smaller than Python's, there are some niches within which it is highly developed. Library code accessible from the alternatives you listed is mainly limited to existing Python and C code. See the demo with `ParallelKMeans.jl`, which wraps a high performance Julia library performing K-means clustering, which could be a useful addition to the Python ecosystem. Another scenario is that you want to write some custom code which makes use of the Julia ecosystem and then expose it to Python.
oefrha 4y ago
Title says “C extension” which is accurate, not “extension written in C”. You can write C extensions for CPython in non-C languages with a bridge, e.g. PyO3 for Rust, but the extension ultimately has to use the C API and ABI.
crabbygrabby 4y ago
I wish they would stop overselling too. There are so many problems with trying to adopt it for real projects it isn't even funny... Good for researchers sometimes, but even then... It's got issues. Pretty toxic community too.
- dunefox 4y ago
  > Pretty toxic community too. Quite the opposite according to my own experience.
- Sukera 4y ago
  > There are so many problems with trying to adopt it for real projects it isn't even funny... Good for researchers sometimes, but even then... It's got issues. Can you expand on that, so that it's possible to improve on them?
  - JZerf 4y ago
    It seems to use a lot of memory which can be unacceptable for many people and can become impractical if you plan on running several programs simultaneously. For several of the problems at https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/julia.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/... on the Computer Language Benchmarks Games, it shows the Julia programs as using several times more memory than similar Java programs. It's even worse when comparing to C, C++, Rust, etc...
    - ChrisRackauckas 4y ago
      Remember that for those benchmarks, C, C++, and Rust compile the binaries and then run the benchmark, while Julia compiles as part of the benchmark. The memory usage is dramatically decreased if you do a workflow similar to the other languages using something like PackageCompiler.jl to build a binary that is benchmarked. If you treat the other languages as "JIT" and include those factors in the benchmarking process, it's a lot closer.
      - 4y ago

adgjlsfhk1 4y ago

Do you have any source on well written julia being any slower than well written C?

bigdict 4y ago
https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/julia-gcc.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
- webshit2 4y ago
  It says right there: "Always look at the source code" fannkuch-redux, n-body, spectral-norm, reverse-complement: the fastest C wins by a large margin because it's an unreadable mess of vector intrinsics. If you really wanted to, you can do that in Julia too (and it might read better). Julia looks pretty even with C in the ones that were faithfully translated.
jakobnissen 4y ago
Don't be unreasonable. Numerical Julia code is just as fast, precisely because it's easy to design efficient high-level APIs and let the compiler optimise it. Try timing Julia's IO, or it's string processing, or it's hash tables, or its array allocation or it's GC - you know, nearly everything other that mathematical operations on floats or ints. Nearly everything leaves performance on the table. Sure Julia is fast, but not on a C level in these areas. There is still lots of room for optimisation, though.
- jkrubin 4y ago
  What’s your point?
  - anewhnaccount2 4y ago
    The point is that these things (string processing/hash tables) are typically done using custom/tuned code in C and so would be faster than the generic, and not particularly carefully tuned Julia implementations. However, given this library seems to be about passing NumPy arrays to/from Julia, the application is almost certainly to expose numerical stuff from Julia to Python. Basically, the use case is similar to Numba, but it works by producing a C-extension from a Julia system image (if I understand correctly). The main advantage over something like Numba would be access to the Julia ecosystem.
  - jakobnissen 4y ago
    The point is that when adgjlsfhk1 doubts that Julia is slower than C, he does so from a background where "data" is a synonym for arrays of numbers, and "computing" means processing these arrays. Julia shines here, precisely because the people developing Julia have a similar focus. In contrast, if your performance bottlenecks are IO, parsing and hash table operations, then Julia's performance will be in par with Python and get absolutely crushed by Rust (I have less experience with C, but would imagine Rust and C are about equally fast). The major difference with Python is that Julia's implementation of these things _could_ be fast in a way that Python's can't. They just aren't.
    - adgjlsfhk1 4y ago
      on par with python is way off. Julia in these areas definitely isn't fully optimized, but the performance will be much closer to something like Java for these cases. a dict in Julia will still have 1 pointer indirect compared to the python one where all your objects have 16 bytes overhead and an extra pointer indirection.
      - jakobnissen 4y ago
cycomanic 4y ago
I did a comparison of Julia vs numpy, cython and pythran [1] some time ago, for a typical dsp routine we use in our work, and Julia was quite a bit slower than the alternatives. Now I'm by no means a Julia expert so I might have missed an optimisation opportunity (although I posted this and nobody could point to something obvious) , however the whole advertisement behind Julia is that one gets essentially C speed with the ease of Python. That's actually my main gripe with Julia, the community advertises it as something that it isn't. I think otherwise some really cool stuff is happening in Julia Land. [1] https://jochenschroeder.com/blog/articles/DSP_with_Python2/ https://jochenschroeder.com/blog/articles/DSP_with_Python2/
- meta2meta 4y ago
  You should run Julia code directly to benchmark instead of calling it in Python through pyjulia.
  - cycomanic 4y ago
    Actually I benchmarked inside Julia as well and didn't see a difference so left it like this.
- salty_biscuits 4y ago
  I only had a quick look, but I would guess the slicing is what makes it slower than you would expect. Julia copies on an array slice, but you can use the @view macro or do some loops with a preallocated array to make it faster.
  - cycomanic 4y ago
    Thanks I'll have a look and post and update sometime later.
- webshit2 4y ago
  wxy[:,:,k] += mu*conj(err[i,k])*X This should probably be wxy[:,:,k] .+= mu*conj(err[i,k]).*X so allocations are avoided. This doubled the speed for me, although I don't what size inputs are realistic. The @benchmark and @profile macros are good for this stuff.

pizza 4y ago

This is why it's a C extension: https://github.com/Suzhou-Tongyuan/jnumpy/blob/main/TyPython/src/C.jl https://github.com/Suzhou-Tongyuan/jnumpy/blob/main/TyPython... The title is correct because it is a Julia numpy interface. Inside it seems is another project, TyPython, that provides an efficient Julia-Python-Numpy bridge, via C extensions.

anewhnaccount2 4y ago
Agreed that the title is technically accurate. Julia should also be in the title, but this is possibly an oversight. It seems a bit unnecessary for the GP to assume that this is a bad faith attempt to mislead and insert Julia where it is not welcome rather than a potentially useful extra tool for writing high performance code. Note that the author/poster is probably not a native English speaker.
cycomanic 4y ago
I would argue that it might technically be correct (and admittedly that's the best type of correct) in the strict sense, it is also misleading. If I read that I'm writing C extensions in minutes I at least expect that the extension compiles to C (which the Julia code doesn't).
- crabbygrabby 4y ago
  Definitely misleading given all the bloat and baggage adding Julia as a dependency to a project can have. It's the heaviest glue ever.

anewhnaccount2 4y ago

> Also why should I pick this over cython, pythran or numba? One scenario would be that there is code already written in Julia, which you would like to expose to a broader audience. Although overall Julia's ecosystem is smaller than Python's, there are some niches within which it is highly developed. Library code accessible from the alternatives you listed is mainly limited to existing Python and C code. See the demo with `ParallelKMeans.jl`, which wraps a high performance Julia library performing K-means clustering, which could be a useful addition to the Python ecosystem. Another scenario is that you want to write some custom code which makes use of the Julia ecosystem and then expose it to Python.

oefrha 4y ago

Title says “C extension” which is accurate, not “extension written in C”. You can write C extensions for CPython in non-C languages with a bridge, e.g. PyO3 for Rust, but the extension ultimately has to use the C API and ABI.

crabbygrabby 4y ago

I wish they would stop overselling too. There are so many problems with trying to adopt it for real projects it isn't even funny... Good for researchers sometimes, but even then... It's got issues. Pretty toxic community too.

dunefox 4y ago
> Pretty toxic community too. Quite the opposite according to my own experience.
Sukera 4y ago
> There are so many problems with trying to adopt it for real projects it isn't even funny... Good for researchers sometimes, but even then... It's got issues. Can you expand on that, so that it's possible to improve on them?
- JZerf 4y ago
  It seems to use a lot of memory which can be unacceptable for many people and can become impractical if you plan on running several programs simultaneously. For several of the problems at https://benchmarksgame-team.pages.debian.net/benchmarksgame/fastest/julia.html https://benchmarksgame-team.pages.debian.net/benchmarksgame/... on the Computer Language Benchmarks Games, it shows the Julia programs as using several times more memory than similar Java programs. It's even worse when comparing to C, C++, Rust, etc...
  - ChrisRackauckas 4y ago
    Remember that for those benchmarks, C, C++, and Rust compile the binaries and then run the benchmark, while Julia compiles as part of the benchmark. The memory usage is dramatically decreased if you do a workflow similar to the other languages using something like PackageCompiler.jl to build a binary that is benchmarked. If you treat the other languages as "JIT" and include those factors in the benchmarking process, it's a lot closer.
    - 4y ago