blis
vectorflow
blis | vectorflow | |
---|---|---|
17 | 12 | |
2,091 | 1,290 | |
3.5% | 0.2% | |
7.0 | 0.0 | |
7 days ago | 10 months ago | |
C | D | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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blis
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Faer-rs: Linear algebra foundation for the Rust programming language
BLIS is an interesting new direction in that regard: https://github.com/flame/blis
>The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels.
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Optimize sgemm on RISC-V platform
There is a recent update to the blis alternative to BLAS that includes a number of RISC-V performance optimizations.
https://github.com/flame/blis/pull/737
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BLIS: Portable basis for high-performance BLAS-like linear algebra libs
https://github.com/flame/blis/blob/master/docs/Performance.m...
It seems that the selling point is that BLIS does multi-core quite well. I am especially impressed that it does as well as the highly optimized Intel's MKL on Intel's CPUs.
I do not see the selling point of BLIS-specific APIs, though. The whole point of having an open BLAS API standard is that numerical libraries should be drop-in replaceable, so when a new library (such as BLIS here) comes along, one could just re-link the library and reap the performance gain immediately.
What is interesting is that numerical algebra work, by nature, is mostly embarrassingly parallel, so it should not be too difficult to write multi-core implementations. And yet, BLIS here performs so much better than some other industry-leading implementations on multi-core configurations. So the question is not why BLIS does so well; the question is why some other implementations do so poorly.
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Benchmarking 20 programming languages on N-queens and matrix multiplication
First we can use Laser, which was my initial BLAS experiment in 2019. At the time in particular, OpenBLAS didn't properly use the AVX512 VPUs. (See thread in BLIS https://github.com/flame/blis/issues/352 ), It has made progress since then, still, on my current laptop perf is in the same range
Reproduction:
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The Art of High Performance Computing
https://github.com/flame/blis/
Field et al, recent winners of the James H. Wilkinson Prize for Numerical Software.
Field and Goto both worked with Robert van de Geijn. Lots of TACC interaction in that broader team.
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[D] Which BLAS library to choose for apple silicon?
BLIS is fine too~ https://github.com/flame/blis
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Column Vectors vs. Row Vectors
Here's BLIS's object API:
https://github.com/flame/blis/blob/master/docs/BLISObjectAPI...
Searching "object" in BLIS's README (https://github.com/flame/blis) to see what they think of it:
"Objects are relatively lightweight structs and passed by address, which helps tame function calling overhead."
"This is API abstracts away properties of vectors and matrices within obj_t structs that can be queried with accessor functions. Many developers and experts prefer this API over the typed API."
In my opinion, this API is a strict improvement over BLAS. I do not think there is any reason to prefer the old BLAS-style API over an improvement like this.
Regarding your own experience, it's great that using BLAS proved to be a valuable learning experience for you. But your argument that the BLAS API is somehow uniquely helpful in terms of learning how to program numerical algorithms efficiently, or that it will help you avoid performance problems, is not true. It is possible to replace the BLAS API with a more modern and intuitive API with the same benefits. To be clear, the benefits here are direct memory management and control of striding and matrix layout, which create opportunities for optimization. There is nothing unique about BLAS in this regard---it's possible to learn these lessons using any of the other listed options if you're paying attention and being systematic.
- BLIS: Portable software framework for high-performance linear algebra
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Small Neural networks in Julia 5x faster than PyTorch
The article asks "Which Micro-optimizations matter for BLAS3?", implying small dimensions, but doesn't actually tell me. The problem is well-studied, depending on what you consider "small". The most important thing is to avoid the packing step below an appropriate threshold. Implementations include libxsmm, blasfeo, and the "sup" version in blis (with papers on libxsmm and blasfeo). Eigen might also be relevant.
https://libxsmm.readthedocs.io/
https://blasfeo.syscop.de/
https://github.com/flame/blis
- Eigen: A C++ template library for linear algebra
vectorflow
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Programming languages endorsed for server-side use at Meta
>> Mozilla (of course)
Mozilla is a c++ and javascript shop. What do they ship in Rust? How much of Firefox is written in rust for example?
>> Microsoft, Meta, Google/Acrobat, Amazon
Large firms have lots of devs and consequently lots of toy projects. Is their usage of rust more significant than their use of D? I mean Meta was churning out projects in D a while back (warp, flint, etc) and looked like it might be going all in at one point (they even hired one of the leads on D lang).
>> That's practically all of FAANG
Who were we missing? Netflix, they’ve dabbled with D too: https://github.com/Netflix/vectorflow
Don’t misunderstand my point - it’s not that D is more popular than rust, it’s that rust is not used for real work in any significant capacity yet.
Where’s the big project written in rust? Servo and the rust compiler are the only two large rust projects on github.
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Cloud TPU VMs are generally available
Thanks Zak, already applied.
Just wondering does TPU VM support Vectorflow?
https://github.com/Netflix/vectorflow
- Vectorflow is a minimalist neural network library optimized for sparse data and single machine environments open sourced by Netflix (r/MachineLearning)
- [P] Vectorflow is a minimalist neural network library optimized for sparse data and single machine environments open sourced by Netflix
- Vectorflow is a minimalist neural network library optimized for sparse data and single machine environments open sourced by Netflix
- Vectorflow: Minimalist neural network library faster than TensorFlow in D
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Small Neural networks in Julia 5x faster than PyTorch
A library I designed a few years ago (https://github.com/Netflix/vectorflow) is also much faster than pytorch/tensorflow in these cases.
In "small" or "very sparse" setups, you're memory bound, not compute bound. TF and Pytorch are bad at that because they assume memory movements are worth it and do very little in-place operations.
Different tools for different jobs.
What are some alternatives?
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
sundials - Official development repository for SUNDIALS - a SUite of Nonlinear and DIfferential/ALgebraic equation Solvers. Pull requests are welcome for bug fixes and minor changes.
dcompute - DCompute: Native execution of D on GPUs and other Accelerators
DirectXMath - DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
xtensor - C++ tensors with broadcasting and lazy computing
LeNetTorch - PyTorch implementation of LeNet for fitting MNIST for benchmarking.
how-to-optimize-gemm
juliaup - Julia installer and version multiplexer
ugrep - ugrep 5.1: A more powerful, ultra fast, user-friendly, compatible grep. Includes a TUI, Google-like Boolean search with AND/OR/NOT, fuzzy search, hexdumps, searches (nested) archives (zip, 7z, tar, pax, cpio), compressed files (gz, Z, bz2, lzma, xz, lz4, zstd, brotli), pdfs, docs, and more