StaticCompiler.jl
OpenBLAS
StaticCompiler.jl | OpenBLAS | |
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16 | 22 | |
474 | 5,983 | |
- | 1.6% | |
6.9 | 9.8 | |
about 1 month ago | 1 day ago | |
Julia | C | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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StaticCompiler.jl
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Potential of the Julia programming language for high energy physics computing
Yes, julia can be called from other languages rather easily, Julia functions can be exposed and called with a C-like ABI [1], and then there's also various packages for languages like Python [2] or R [3] to call Julia code.
With PackageCompiler.jl [4] you can even make AOT compiled standalone binaries, though these are rather large. They've shrunk a fair amount in recent releases, but they're still a lot of low hanging fruit to make the compiled binaries smaller, and some manual work you can do like removing LLVM and filtering stdlibs when they're not needed.
Work is also happening on a more stable / mature system that acts like StaticCompiler.jl [5] except provided by the base language and people who are more experienced in the compiler (i.e. not a janky prototype)
[1] https://docs.julialang.org/en/v1/manual/embedding/
[2] https://pypi.org/project/juliacall/
[3] https://www.rdocumentation.org/packages/JuliaCall/
[4] https://github.com/JuliaLang/PackageCompiler.jl
[5] https://github.com/tshort/StaticCompiler.jl
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Julia App Deployment
PackageCompiler, but it' s a fat runtime and not cross compile. A thin runtime is currently not possible without sacrifices for feature as https://github.com/tshort/StaticCompiler.jl.
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JuLox: What I Learned Building a Lox Interpreter in Julia
https://github.com/tshort/StaticCompiler.jl/issues/59 Would working on this feasible?
- Making Python 100x faster with less than 100 lines of Rust
- What's Julia's biggest weakness?
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Size of a "hello world" application
I just read the project's documentation at https://github.com/tshort/StaticCompiler.jl. It does produce a "hello world" application that is only 8.4k in size đź‘Ť. I do like that it can work on Mac OS. Hopefully Windows support will come soon.
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Why Julia 2.0 isn’t coming anytime soon (and why that is a good thing)
See https://github.com/tshort/StaticCompiler.jl
- My Experiences with Julia
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Julia for health physics/radiation detection
You're probably dancing around the edges of what [PackageCompiler.jl](https://github.com/JuliaLang/PackageCompiler.jl) is capable of targeting. There are a few new capabilities coming online, namely [separating codegen from runtime](https://github.com/JuliaLang/julia/pull/41936) and [compiling small static binaries](https://github.com/tshort/StaticCompiler.jl), but you're likely to hit some snags on the bleeding edge.
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We Use Julia, 10 Years Later
using StaticCompiler # `] add https://github.com/tshort/StaticCompiler.jl` to get latest master
OpenBLAS
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LLaMA Now Goes Faster on CPUs
The Fortran implementation is just a reference implementation. The goal of reference BLAS [0] is to provide relatively simple and easy to understand implementations which demonstrate the interface and are intended to give correct results to test against. Perhaps an exceptional Fortran compiler which doesn't yet exist could generate code which rivals hand (or automatically) tuned optimized BLAS libraries like OpenBLAS [1], MKL [2], ATLAS [3], and those based on BLIS [4], but in practice this is not observed.
Justine observed that the threading model for LLaMA makes it impractical to integrate one of these optimized BLAS libraries, so she wrote her own hand-tuned implementations following the same principles they use.
[0] https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...
[1] https://github.com/OpenMathLib/OpenBLAS
[2] https://www.intel.com/content/www/us/en/developer/tools/onea...
[3] https://en.wikipedia.org/wiki/Automatically_Tuned_Linear_Alg...
[4]https://en.wikipedia.org/wiki/BLIS_(software)
- Assume I'm an idiot - oogabooga LLaMa.cpp??!
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Learn x86-64 assembly by writing a GUI from scratch
Yeah. I'm going to be helping to work on expanding CI for OpenBlas and have been diving into this stuff lately. See the discussion in this closed OpenBlas issue gh-1968 [0] for instance. OpenBlas's Skylake kernels do rely on intrinsics [1] for compilers that support them, but there's a wide range of architectures to support, and when hand-tuned assembly kernels work better, that's what are used. For example, [2].
[0] https://github.com/xianyi/OpenBLAS/issues/1968
[1] https://github.com/xianyi/OpenBLAS/blob/develop/kernel/x86_6...
[2] https://github.com/xianyi/OpenBLAS/blob/23693f09a26ffd8b60eb...
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AI’s compute fragmentation: what matrix multiplication teaches us
We'll have to wait until part 2 to see what they are actually proposing, but they are trying to solve a real problem. To get a sense of things check out the handwritten assembly kernels in OpenBlas [0]. Note the level of granularity. There are micro-optimized implementations for specific chipsets.
If progress in ML will be aided by a proliferation of hyper-specialized hardware, then there really is a scalability issue around developing optimized matmul routines for each specialized chip. To be able to develop a custom ASIC for a particular application and then easily generate the necessary matrix libraries without having to write hand-crafted assembly for each specific case seems like it could be very powerful.
[0] https://github.com/xianyi/OpenBLAS/tree/develop/kernel
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Trying downloading BCML
libraries mkl_rt not found in ['C:\python\lib', 'C:\', 'C:\python\libs'] ``` Install this and try again. Might need to reboot, never know with Windows https://www.openblas.net/
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The Bitter Truth: Python 3.11 vs Cython vs C++ Performance for Simulations
There isn't any fortran code in the repo there itself but numpy itself can be linked with several numeric libraries. If you look through the wheels for numpy available on pypi, all the latest ones are packaged with OpenBLAS which uses Fortran quite a bit: https://github.com/xianyi/OpenBLAS
- Optimizing compilers reload vector constants needlessly
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Just a quick question, can a programming language be as fast as C++ and efficient with as simple syntax like Python?
Sure - write functions in another language, export C bindings, and then call those functions from Python. An example is NumPy - a lot of its linear algebra functions are implemented in C and Fortran.
- OpenBLAS - optimized BLAS library based on GotoBLAS2 1.13 BSD version
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How to include external libraries?
Read the official docs yet?
What are some alternatives?
julia - The Julia Programming Language
Eigen
PackageCompiler.jl - Compile your Julia Package
GLM - OpenGL Mathematics (GLM)
acados - Fast and embedded solvers for nonlinear optimal control
cblas - Netlib's C BLAS wrapper: http://www.netlib.org/blas/#_cblas
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
blaze
oneAPI.jl - Julia support for the oneAPI programming toolkit.
Boost.Multiprecision - Boost.Multiprecision
LoopVectorization.jl - Macro(s) for vectorizing loops.
ceres-solver - A large scale non-linear optimization library