11–20 of 64 posts
Punchline: rewrote the code to look almost identical to C++, hand-held the compiler by adding @-marks to disable safety checks, forced SIMD codegen and fastmath on. End result: code that is uglier and still much slower than C++. Kind of a shame.
As a quick anecdote, in our take-home interview exercise, we usually receive answers in C++ or Julia, and the two fastest answers have been in Julia.
Earlier quoted context omitted.
> code that is uglier and still much slower than C++. Oh such a shame indeed! They didn’t even manage to produce better looking code at least?? Julia was looking great in 2019 but it was very buggy still so I stopped looking. Had hopes that by now it would be a good choice over C++ and Rust with similar performance.
There's simply no way it'd ever have similar performance to those. It's not possible. I have always seen it as a potential alternative to Java, and definitely better than Python. My experience working in it professionally was that it was... fine. But the GC in it was not good under load and not competitive with Java's.
Punchline: rewrote the code to look almost identical to C++, hand-held the compiler by adding @-marks to disable safety checks, forced SIMD codegen and fastmath on. End result: code that is uglier and still much slower than C++. Kind of a shame.
Punchline: rewrote the code to look almost identical to C++, hand-held the compiler by adding @-marks to disable safety checks, forced SIMD codegen and fastmath on. End result: code that is uglier and still much slower than C++. Kind of a shame.
This is 7 years old. Julia is a totally different language by now. As a quick anecdote, in our take-home interview exercise, we usually receive answers in C++ or Julia, and the two fastest answers have been in Julia.
Yeah, I actually totally forgot to check the date...
Punchline: rewrote the code to look almost identical to C++, hand-held the compiler by adding @-marks to disable safety checks, forced SIMD codegen and fastmath on. End result: code that is uglier and still much slower than C++. Kind of a shame.
This is 7 years old. Julia is a totally different language by now. As a quick anecdote, in our take-home interview exercise, we usually receive answers in C++ or Julia, and the two fastest answers have been in Julia.
Of course it also depends on what additional libaries you are using, especially when it comes to parallel/GPU programming in C++, but easy to believe that Julia out of the box makes it easy to write high performance parallel software.
Earlier quoted context omitted.
There's simply no way it'd ever have similar performance to those. It's not possible. I have always seen it as a potential alternative to Java, and definitely better than Python. My experience working in it professionally was that it was... fine. But the GC in it was not good under load and not competitive with Java's.
How hard was it to maintain a large Julia code base rather then say an OOP or Rust one? It has an interesting paradigm. I feel like it could get really messy
But interfaces are informal. Not using a monorepo say makes it harder to be sure if your broke downstream or not (via downstream’s unit tests).
But freedom from Rust’s orphan rule etc means you can decompose large code into fragments easily, while getting almost Zig-style specialisation yet the ease of use of python (for consumers). I would say this takes a fair bit of skill to wield safely/in a maintainable fashion though, and many packages (including my own) are not extremely mature.
Very interesting post and I think this exposes the limitations of the Julia compiler. Note that an old version of the compiler is used (1.0.3 from 2019). One could say that we can almost replicate the semantic of a C++ program, but writing in Julia. For example we can remove bounds checks in arrays or remove hidden memory allocations. But the goal of a language for numerical computing is capturing the mathematical fo…
I think the best compromise would be to get the best of two words. By default perform bound checks, but have a compiler flag which skips it. Might broke many programs written with default behaviour in mind, but allow perform additional optimizations.
Punchline: rewrote the code to look almost identical to C++, hand-held the compiler by adding @-marks to disable safety checks, forced SIMD codegen and fastmath on. End result: code that is uglier and still much slower than C++. Kind of a shame.
I don't get the appeal. It's like a. OSS Matlab but all contributions are used directly so the language developers can make money for a parent company? Most OSS languages aren't run that way. Seems kind of scammy
It’s nothing like Google-the-ad-company influencing Chrome. The company consumes Julia for products to sell, rather. Maybe this affects the ordering of features landing, but… meh.
Punchline: rewrote the code to look almost identical to C++, hand-held the compiler by adding @-marks to disable safety checks, forced SIMD codegen and fastmath on. End result: code that is uglier and still much slower than C++. Kind of a shame.
I don't get the appeal. It's like a. OSS Matlab but all contributions are used directly so the language developers can make money for a parent company? Most OSS languages aren't run that way. Seems kind of scammy
Earlier quoted context omitted.
> code that is uglier and still much slower than C++. Oh such a shame indeed! They didn’t even manage to produce better looking code at least?? Julia was looking great in 2019 but it was very buggy still so I stopped looking. Had hopes that by now it would be a good choice over C++ and Rust with similar performance.
There's simply no way it'd ever have similar performance to those. It's not possible. I have always seen it as a potential alternative to Java, and definitely better than Python. My experience working in it professionally was that it was... fine. But the GC in it was not good under load and not competitive with Java's.
The key to performance with the GC in Julia is not allocating, but it has gotten substantially better since 2019.