OpenBLAS VS llvm-cbe

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)

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...

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

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/

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

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.

Read the official docs yet?

So how does the LLVM C backend work then?

https://github.com/JuliaHubOSS/llvm-cbe

One alternative worth mentioning, though, would be the LLVM C Backend maintained by the Julia community.

If you drop "easily" and "human" (/s) from your requirements list, then the C backend for LLVM might work. Then you can choose any programming language you want that has LLVM 10-compatible frontend.

If you really must have something that compiles in C (e.g. for a platform where you only have a C compiler) there's an LLVM backend that outputs C code: https://github.com/JuliaComputingOSS/llvm-cbe

You can convert llvm bitcode to C and then use C compiler, there is such project https://github.com/JuliaComputingOSS/llvm-cbe .

I'm really surprised that the LLVM C backends have continually been resurrected then abandoned over the years. It's a good solution to this sort of thing and would enable a lot of cool stuff like Rust to weird embedded platforms. The most recent one is the Julia backend: https://github.com/JuliaComputingOSS/llvm-cbe

Check this project out: https://github.com/JuliaComputingOSS/llvm-cbe.

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

You can try your luck with the "resurrected" C backend: https://github.com/JuliaComputingOSS/llvm-cbe

I don't understand why I see so many requests for LLVM-based languages to change around their backend or IR, that seems to be a huge amount of work for comparatively little benefit. The correct thing to do there is to just add support for those to LLVM.