FastKron is a fast library for computing Generalized Kronecker-Matrix Matrix Multiplication (GeKMM) on NVIDIA GPUs and X86 CPUs. FastKron contains a specialized algorithm and implementations of GeKMM rather than using existing linear algebra operations. FastKron avoids extra transposes and adds more optimizations including fusion of multiple kernels. Therefore, FastKron performs orders of magnitude better than baseline GPyTorch, NVIDIA cuTensor, and HPTT. FastKron obtains upto 90% of the maximum FLOPs of a NVIDIA Tesla A100 and same FLOPs as Intel MKL of an AMD EPYC 7742 64-Core with AVX256. FastKron supports float and double data type. Fastkron provides a C++ library and a Python library compatible with PyTorch and Numpy.
For more details look Fast Kronecker Matrix-Matrix Multiplication on GPUs.
We compare FastKron with state-of-the-art baselines of existing algorithms. GPyTorch implements the traditional shuffle algorithm that uses matrix multiplication and transpose. GPyTorch runs on NVIDIA GPUs and x86 CPUs. NVIDIA cuTensor and TCCG (https://github.com/HPAC/tccg) are tensor contraction engines for NVIDIA GPUs and x86 CPUs respectively. Graphs below shows the performance of FastKron against these baselines.
| NVIDIA A100 SXM 80GB | AMD 7742 64-Core with AVX2 |
|---|---|
 |
 |
The graphs above multiplies a matrix of shape [M, PN] with a Kronecker Product of N matrices of size [P, Q]. FastKron performs significantly better than existing baselines.
| Linux | WSL2 | |
|---|---|---|
| x86 | ✅ | ✅ |
| AVX256 | ✅ | ✅ |
| AVX512 | ✅ | ✅ |
| SM50+ CUDA cores | ✅ | ✅ |
| SM80+ Tensor cores | ❌ | ❌ |
x86 CPUs older than x86-64-v2, ARM CPUs, AMD GPUs, Windows, and Mac OS are not yet supported.
The directory example/ pinclude examples of using FastKron's CUDA and x86 backend using both C++ and Python.
Before using an example, follow below instructions to build FastKron.
Build the C++ library, libFastKron.so, to use with C++ programs or the Python library, PyFastKron, to use with PyTorch or Numpy programs.
On Ubuntu :
sudo apt update && sudo apt install gcc linux-headers-$(uname -r) make g++ git python3-dev wget unzip python3-pip build-essential devscripts debhelper fakeroot intel-mkl cmake
Install CUDA 11+ from https://developer.nvidia.com/cuda/ .
Clone repository with submodules using
git clone --recurse-submodules https://github.com/abhijangda/fastkron.git
If already cloned and want to only clone submodules, use
git submodule update --init --recursive
Build FastKron as C++ library using below commands:
cd build/
cmake ..
make -j
To install run
make install
By default both x86 and CUDA backends are built. use CMAKE option -DENABLE_CUDA=OFF to disable CUDA backend or -DENABLE_X86=OFF to disable x86 backend.
Run X86 CPU tests using
make run-x86-tests
Run CUDA tests using
make run-cuda-tests
Install PyFastKron using pip
pip install .
To disable a backend add --config-settings=cmake.define.ENABLE_<backend>=OFF as argument to above command.
Run tests using
pytest
FastKron C++ API: documents/api.md
FastKron Python API: documents/api.md
Kernel Tuning: documents/autotuning.md
Multi-GPU: documents/multigpu.md
@inproceedings{10.1145/3627535.3638489,
author = {Jangda, Abhinav and Yadav, Mohit},
title = {Fast Kronecker Matrix-Matrix Multiplication on GPUs},
year = {2024},
isbn = {9798400704352},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627535.3638489},
doi = {10.1145/3627535.3638489},
booktitle = {Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming},
pages = {390–403},
numpages = {14},
keywords = {graphics processing units, CUDA, kronecker product, linear algebra},
location = {Edinburgh, United Kingdom},
series = {PPoPP '24}
}