cutlass
OpenBLAS
cutlass | OpenBLAS | |
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16 | 22 | |
4,563 | 5,974 | |
3.6% | 1.5% | |
8.7 | 9.8 | |
5 days ago | 8 days ago | |
C++ | C | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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cutlass
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Optimization Techniques for GPU Programming [pdf]
I would recommend the course from Oxford (https://people.maths.ox.ac.uk/gilesm/cuda/). Also explore the tutorial section of cutlass (https://github.com/NVIDIA/cutlass/blob/main/media/docs/cute/...) if you want to learn more about high performance gemm.
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Want to understand INT8 better
The latter (and I guess you were asking about this one) is designed to accelerate NN inference in reduced precision. It is possible to use Tensor Cores for you own purposes, mainly through CUTLASS. But because Tensor Cores are designed to execute matrix multiplications, it can be hard to adapt your problem to them. The performance with them is insane (IIRC 32x the performance of the INT32 pipeline), but only for matrix multiplication…
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How do I deal with tensor core and cuda core with different precision?
If you want to learn about controlling Tensor Cores, the main way is through the CUTLASS library, that wraps the complexity of Tensor Cores into higher level abstractions. You can also look for mma/wmma instructions in the PTX specification, or for the WMMA API in CUDA.
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AI’s compute fragmentation: what matrix multiplication teaches us
> we used tensor cores and managed to get back fp32 accuracy with 3 rounds of the things
Hey are you referring to 3xTF32 (https://github.com/NVIDIA/cutlass/tree/master/examples/28_am...)? IMO this is a perfect example where proper abstraction could save engineers non-trivial amount of time - imagine a compiler stack which allows 3xTF32 as a normal dtype and subsequent analysis compatible with this special dtype :-)
- With LLVM and MLIR, is manual cuda optimizing still important?
- CUTLASS 3.0 is now available
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How to Optimize a CUDA Matmul Kernel for CuBLAS-Like Performance: A Worklog
This is a great post for people who are new to optimizing GPU code.
It is interesting to see that the author got this far without interchanging the innermost loop over k to the outermost loop, as is done in CUTLASS (https://github.com/NVIDIA/cutlass).
As you can see in this blog post the code ends up with a lot of compile-time constants (e.g. BLOCKSIZE, BM, BN, BK, TM, TN) one way to optimize this code further is to use an auto-tuner to find the optimal value for all of these parameters for your GPU and problem size, for example Kernel Tuner (https://github.com/KernelTuner/kernel_tuner)
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pytorch example to actually see anything near 83 TFLOP/s on a RTX 4090?
Some examples here have a benchmark: https://github.com/NVIDIA/cutlass/blob/master/examples/24_gemm_grouped/gemm_grouped.cu
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Create a bare CMake for Nvidia CUTLASS
I would like to make a minimum CMakeLists to use the CUDA CUTLASS library in another project. The build system is CMake, however I have little experience with CMake.
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[D] What are some good resources to learn CUDA programming?
If you already know some C++, the Nvidia devblog is a great resource. Going further, Cub and Cutlass provide examples of efficient implementations for key operations at all hardware levels. Finally, this is more anecdotal but I always start my lectures on Cuda programming with the pictures in this doc page, to provide some intuition on the different memory layers that you can leverage to speed up a program. In any case, good luck :-)
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?
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Eigen
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
GLM - OpenGL Mathematics (GLM)
GPU-Puzzles - Solve puzzles. Learn CUDA.
cblas - Netlib's C BLAS wrapper: http://www.netlib.org/blas/#_cblas
triton - Development repository for the Triton language and compiler
blaze
Chess_BinaryNeuralNetwork - Training and Code Emitting Library for Binary Neural Networks
Boost.Multiprecision - Boost.Multiprecision
Open3D - Open3D: A Modern Library for 3D Data Processing
ceres-solver - A large scale non-linear optimization library