TAtastyminerals2·Jul 22, 2020Submission
Do you know about OpenBLAS alternative Mir GLAS?
8 points · Open discussionlibmir
mir-glas
D2016–2020
libmir / mir-glas
mir-glas
[Experimental] LLVM-accelerated Generic Linear Algebra Subprograms
4Distinct people
5Mentions
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102GitHub stars
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[Experimental] LLVM-accelerated Generic Linear Algebra Subprograms
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Observed mentions: 2016-12-19 08:09:45 UTC to 2020-07-22 05:58:58 UTC.
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
5 sourcesYou could use mir glas[1], which has similar performance[2], but works on (pretty much) any architecture.
1: https://github.com/libmir/mir-glas
2: http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...
In Intel hit with 32 lawsuits over security flawsThanks for you insight.
I agree that it is not easy to 'parameterise the metal', but it is certainly doable in D[1], in C++ (guessed from the std::thread) good lord no: D's is orders of magnitude ahead of C+'s meta-programming. Writing different versions of a kernel is a poor mans parameterisation ;)
LDC, the LLVM D Compiler, will be getting a dynamic re-optimisation, which I hope to get to play nicely with DCompute. Well PTX, because SPIR-V doesn't yet have a jit backend. Dynamic re-optimisation from what I understand is freeze some variables as constants and rerun the optimisation passes. This is as opposed to recompilation with complete restructuring of the kernel. Not quite the same but for things like loop counts and branch "prediction" this should help a lot.
w.r.t USE_HOST_PTR/COPY_HOST_PTR, that's not a part of the kernel paramete
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risation, that's part of the host and is easily adjustable. Yes you need to figure our which one to use, but that's just part of tuning.
> What I'm saying is - I no longer believe in easy to use abstractions for these purposes.
I hope to be able to show you otherwise :)
[1](https://github.com/libmir/mir-glas#porting-to-a-new-target)
I am the author of DCompute, a compiler/library/runtime framework for abstracting OpenCL/CUDA for D. You can write kernels already, although the API automation is still a work in progress.
I'm hoping that this should level the field a bit, because let's face it, people use CUDA for two reasons: the OpenCL driver API sucks; and the utility libraries (cuDNN et al) for CUDA. Possibly driver quality as well.
By having an API thats not horrible to use, that advantage is gone. The utility libraries will be more of a challenge to undermine, but since it targets CUDA natively there is no disadvantage to users of nvidia's hardware, but there is no advantage to others, yet (see GLAS[1] for what is possible with relative ease). Using D as the kernel language will also bring significant advantages over C/C++, static reflection, sane templates and co
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mpile time code generation to name a few.
You can find it at https://github.com/libmir/dcompute.
If you have any question, please ask!
[1] https://github.com/libmir/mir-glas
> Why re-write what has already been done?
Because we can do better. For example, look at Mir GLAS, written in D.
https://github.com/libmir/mir-glas
In The Crystal Programming Language