Octavian.jl
Flutter
Octavian.jl | Flutter | |
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17 | 1,203 | |
222 | 161,805 | |
0.0% | 0.5% | |
3.9 | 10.0 | |
24 days ago | 6 days ago | |
Julia | Dart | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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Octavian.jl
- Yann Lecun: ML would have advanced if other lang had been adopted versus Python
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Julia 1.8 has been released
For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
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Why Julia matrix multiplication so slow in this test?
Note that a performance-optimized Julia implementation is on par or even outperform the specialized high-performance BLAS libraries, see https://github.com/JuliaLinearAlgebra/Octavian.jl .
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Multiple dispatch: Common Lisp vs Julia
If you look at the thread for your first reference, there were a large number of performance improvements suggested that resulted in a 30x speedup when combined. I'm not sure what you're looking at for your second link, but Julia is faster than Lisp in n-body, spectral norm, mandelbrot, pidigits, regex, fasta, k-nucleotide, and reverse compliment benchmarks. (8 out of 10). For Julia going faster than C/Fortran, I would direct you to https://github.com/JuliaLinearAlgebra/Octavian.jl which is a julia program that beats MKL and openblas for matrix multiplication (which is one of the most heavily optimized algorithms in the world).
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Why Fortran is easy to learn
> But in the end, it's FORTRAN all the way down. Even in Julia.
That's not true. None of the Julia differential equation solver stack is calling into Fortran anymore. We have our own BLAS tools that outperform OpenBLAS and MKL in the instances we use it for (mostly LU-factorization) and those are all written in pure Julia. See https://github.com/YingboMa/RecursiveFactorization.jl, https://github.com/JuliaSIMD/TriangularSolve.jl, and https://github.com/JuliaLinearAlgebra/Octavian.jl. And this is one part of the DiffEq performance story. The performance of this of course is all validated on https://github.com/SciML/SciMLBenchmarks.jl
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
Well IMO it can definitely be rewritten in Julia, and to an easier degree than python since Julia allows hooking into the compiler pipeline at many areas of the stack. It's lispy an built from the ground up for codegen, with libraries like (https://github.com/JuliaSymbolics/Metatheory.jl) that provide high level pattern matching with e-graphs. The question is whether it's worth your time to learn Julia to do so.
You could also do it at the LLVM level: https://github.com/JuliaComputingOSS/llvm-cbe
For interesting takes on that, you can see https://github.com/JuliaLinearAlgebra/Octavian.jl which relies on loopvectorization.jl to do transforms on Julia AST beyond what LLVM does. Because of that, Octavian.jl beats openblas on many linalg benchmarks
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Python behind the scenes #13: the GIL and its effects on Python multithreading
The initial results are that libraries like LoopVectorization can already generate optimal micro-kernels, and is competitive with MKL (for square matrix-matrix multiplication) up to around size 512. With help on macro-kernel side from Octavian, Julia is able to outperform MKL for sizes up to to 1000 or so (and is about 20% slower for bigger sizes). https://github.com/JuliaLinearAlgebra/Octavian.jl.
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From Julia to Rust
> The biggest reason is because some function of the high level language is incompatible with the application domain. Like garbage collection in hot or real-time code or proprietary compilers for processors. Julia does not solve these problems.
The presence of garbage collection in julia is not a problem at all for hot, high performance code. There's nothing stopping you from manually managing your memory in julia.
The easiest way would be to just preallocate your buffers and hold onto them so they don't get collected. Octavian.jl is a BLAS library written in julia that's faster than OpenBLAS and MKL for small matrices and saturates to the same speed for very large matrices [1]. These are some of the hottest loops possible!
For true, hard-real time, yes julia is not a good choice but it's perfectly fine for soft realtime.
[1] https://github.com/JuliaLinearAlgebra/Octavian.jl/issues/24#...
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Julia 1.6 addresses latency issues
If you want performance benchmarks vs Fortran, https://benchmarks.sciml.ai/html/MultiLanguage/wrapper_packa... has benchmarks with Julia out-performing highly optimized Fortran DiffEq solvers, and https://github.com/JuliaLinearAlgebra/Octavian.jl shows that pure Julia BLAS implementations can compete with MKL and openBLAS, which are among the most heavily optimized pieces of code ever written. Furthermore, Julia has been used on some of the world's fastest super-computers (in the performance critical bits), which as far as I know isn't true of Swift/Kotlin/C#.
Expressiveness is hard to judge objectively, but in my opinion at least, Multiple Dispatch is a massive win for writing composable, re-usable code, and there really isn't anything that compares on that front to Julia.
- Octavian.jl – BLAS-like Julia procedures for CPU
Flutter
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Show HN: Shorebird 1.0, Flutter Code Push
[3]: https://github.com/flutter/flutter/tree/master/packages/flut...
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3D and 2D: Testing out my cross-platform graphics engine
Thanks - that link does not appear to be open access, anyways I don't think I've seen it. I'm familiar with Flutter at a high-level (Kevin Moore gave a great talk on it at Wasm I/O), and I think other than requiring users to work in Dart, it is probably one of the most powerful ways to do cross-platform UI today.
Worth noting that their original GPU backend was Skia, and now they are retooling around Flutter GPU (Impeller)[0], which is kind of designed similarly as an abstract rendering interface over platform-specific GPU APIs.
[0]https://github.com/flutter/flutter/wiki/Flutter-GPU
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Python dev considering Electron vs. Kivy for desktop app UI
If you are considering Electron/React then I would suggest adding Flutter to your list of technologies to consider. It uses Dart (a language similar to C#) and has a lot going for it… relatively quick to get up to speed with, fantastic developer experience (e.g., hot reload, great IDE support, good development tools) and very strong cross-platform support: it generates native iOS, Android, MacOS, Windows and Linux executables. Check it out: https://flutter.dev/
- Lançamento do App Edudu
- Android 12+: Changing wallpaper or dark theme breaks Flutter and Jetpack Apps
- Android 12: Changing wallpaper or dark theme breaks Flutter and Jetpack Compose
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React Native and Flutter: A Developer's Dilemma
You can find the React Native documentation here and Flutter Documentation here.
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Ente: Open-Source, E2E Encrypted, Google Photos Alternative
[1]https://github.com/flutter/flutter/issues/55092#issuecomment...
- Reusing state logic is either too verbose or too difficult #51752
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React Labs: What We've Been Working On – February 2024 – React Compiler
> There is actually a great issue thread on the Flutter GitHub that explains exactly why other solutions do not work correctly when compared to hooks [0]
Interesting. I assume you are referring to this comment in particular -> https://github.com/flutter/flutter/issues/51752#issuecomment... ?
What are some alternatives?
OpenBLAS - OpenBLAS is an optimized BLAS library based on GotoBLAS2 1.13 BSD version.
Introducing .NET Multi-platform App UI (MAUI) - .NET MAUI is the .NET Multi-platform App UI, a framework for building native device applications spanning mobile, tablet, and desktop.
Symbolics.jl - Symbolic programming for the next generation of numerical software
flet - Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.
owl - Owl - OCaml Scientific Computing @ https://ocaml.xyz
WPF - WPF is a .NET Core UI framework for building Windows desktop applications.
Verilog.jl - Verilog for Julia
Uno Platform - Build Mobile, Desktop and WebAssembly apps with C# and XAML. Today. Open source and professionally supported.
Automa.jl - A julia code generator for regular expressions
kivy - Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
Quasar Framework - Quasar Framework - Build high-performance VueJS user interfaces in record time