JET.jl
Octavian.jl
JET.jl | Octavian.jl | |
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13 | 17 | |
694 | 222 | |
- | 0.0% | |
9.0 | 3.9 | |
4 days ago | about 2 months ago | |
Julia | Julia | |
MIT License | GNU General Public License v3.0 or later |
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JET.jl
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Prospects of utilising Rust in scientific computation?
An informative discussion on julia forum. Have you tried using https://github.com/aviatesk/JET.jl to minimize type instabilities?
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Julia v1.9.0 has been released
For instance, https://github.com/aviatesk/JET.jl is still in its relative infancy, but it's played a big role in detecting quite a few potential bugs that had never been reported to use by users or caught in our testing infrastructure. There's also been a lot developments like interfaces to RR the time travelling debugger https://rr-project.org/ which helps us better understand and catch some very hard to debug non-deterministic bugs.
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Julia Computing Raises $24M Series A
Have you seen Shuhei Tadowaki's work on JET.jl (?)
If you're curious: https://github.com/aviatesk/JET.jl
This may seem more about performance (than IDE development) but Shuhei is one of the driving contributors behind developing the capabilities to use compiler capabilities for IDE integration -- and indeed JET.jl contains the kernel of a number of these capabilities.
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I Hate Programming Language Advocacy (2000)
This is sort of being done right now, as dynamic languages have begun to adopt gradual typing... at least Python and Julia, that I know of.
If something like [JET.jl](https://github.com/aviatesk/JET.jl) become ubiquitous in Julia, one could add a function that pointed out all the places in the code where types are not fully inferred by the compiler.
It'll never be quite the same level of safety as a static language, however.
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From Julia to Rust
- Pattern matching (sometimes you don't want the overhead of a method lookup)
[1]: https://github.com/aviatesk/JET.jl
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Julia is the best language to extend Python for scientific computing
You can use the `@code_warntype` macro to check for type stability, which is very helpful for detecting such performance pitfalls on single function level. In the future, https://github.com/aviatesk/JET.jl may give a more powerful way to do it.
- Jet.jl: experimental type checker for Julia
- Jet.jl: A WIP compile time type checker for Julia
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
What are some alternatives?
julia - The Julia Programming Language
OpenBLAS - OpenBLAS is an optimized BLAS library based on GotoBLAS2 1.13 BSD version.
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
Symbolics.jl - Symbolic programming for the next generation of numerical software
Metatheory.jl - Makes Julia reason with equations. General purpose metaprogramming, symbolic computation and algebraic equational reasoning library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
owl - Owl - OCaml Scientific Computing @ https://ocaml.xyz
StaticArrays.jl - Statically sized arrays for Julia
Verilog.jl - Verilog for Julia
HTTP.jl - HTTP for Julia
Automa.jl - A julia code generator for regular expressions
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)