neanderthal
OpenBLAS
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neanderthal | OpenBLAS | |
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5 | 22 | |
1,043 | 5,974 | |
0.2% | 2.7% | |
7.0 | 9.8 | |
about 1 month ago | 3 days ago | |
Clojure | C | |
Eclipse Public License 1.0 | BSD 3-clause "New" or "Revised" License |
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neanderthal
- AI’s compute fragmentation: what matrix multiplication teaches us
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Having trouble setting up Neanderthal.
There is the official Hello World https://github.com/uncomplicate/neanderthal/tree/master/examples/hello-world
- Da li u Srbiji , generalno prostoru balkana , ima "Ozbiljnih" Open source kreatora?
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Anybody using Common Lisp or clojure for data science
Did you have any occasion to evaluate neanderthal during your research? People seem to prefer it over core.matrix because it focus on primitive speed and sticking to BLAS idioms (as well as offering a decent api for working with GPU backends via cuda and opencl). I am curious to see if you did and found anything lacking there. I have a project on the backburner to try and target neanderthal for local search stuff, expressing problems in a high-level API that can then be baked into some numerically-friendly representation for efficient execution. It's often easier (trivial) to express solution representations, neighborhood functions, and objectives/constraints in a general purpose language, of which none of the things we like (sparse data structures, dynamically allocated stuff) are amenable to the contiguous memory, primitive numeric model that the hardware wants.
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I want to quit my data analyst job and learn and become a Clojure developer
Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.
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?
dtype-next - A Clojure library designed to aid in the implementation of high performance algorithms and systems.
Eigen
libpython-clj - Python bindings for Clojure
GLM - OpenGL Mathematics (GLM)
deep-diamond - A fast Clojure Tensor & Deep Learning library
cblas - Netlib's C BLAS wrapper: http://www.netlib.org/blas/#_cblas
numcl-benchmarks - benchmarks against numpy, julia
blaze
magicl - Matrix Algebra proGrams In Common Lisp.
Boost.Multiprecision - Boost.Multiprecision
qvm - The high-performance and featureful Quil simulator.
ceres-solver - A large scale non-linear optimization library