用于稀疏矩阵运算的GPU内核库。_C++_CMake_下载.zip

# Sputnik Sputnik is a library of sparse linear algebra kernels and utilities for deep learning. ## Build Sputnik uses the CMake build system. Sputnik depends on the CUDA toolkit (v10.1+) and supports SM70+. The only additional dependency for the library is [google/glog](https://github.com/google/glog). To build the library, enter the project directory and run the following commands: `mkdir build && cd build` `cmake .. -DCMAKE_BUILD_TYPE=Release` `make -j12` The test and benchmark suites additionally depend on [abseil/abseil-cpp](https://github.com/abseil/abseil-cpp), [google/googltest](https://github.com/google/googletest), and [google/benchmark](https://github.com/google/benchmark). These dependencies are includes as submodules in [third_party](https://github.com/google-research/sputnik/tree/os-build/third_party). To build the test suite and/or benchmark suite, set `-DBUILD_TEST=ON` and/or `-DBUILD_BENCHMARK=ON` in your `cmake` command. `cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_TEST=ON -DBUILD_BENCHMARK=ON -DCUDA_ARCHS="70;75"` ## Docker Sputnik provides a [Dockerfile](https://github.com/google-research/sputnik/blob/os-build/Dockerfile) that builds the proper environment with all dependencies. Note that [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) must be installed to run on GPU. To build the image, run the following command: `docker build . -t sputnik-dev` To launch the container with the sputnik source mounted under `/mount` (assuming you're working out of $HOME), run the following: `sudo docker run --runtime=nvidia -v ~/:/mount/ -it sputnik-dev:latest` ## Citation If you make use of this library, please cite: ``` @inproceedings{sgk_sc2020, author = {Trevor Gale and Matei Zaharia and Cliff Young and Erich Elsen}, title = {Sparse {GPU} Kernels for Deep Learning}, booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, {SC} 2020}, year = {2020}, } ``` The sparse models and dataset of sparse matrices from deep neural networks from the above paper can be found [here](https://github.com/google-research/google-research/tree/master/sgk). ## Disclaimer This is not an official Google product.