文件列表:

.travis.yml (3013, 2020-06-15)
CMakeLists.txt (4171, 2020-06-15)
LICENSE (1070, 2020-06-15)
MANIFEST.in (47, 2020-06-15)
pybind11 (0, 2020-06-15)
pyproject.toml (239, 2020-06-15)
setup.cfg (40, 2020-06-15)
setup.py (4151, 2020-06-15)
src (0, 2020-06-15)
src\cpu_main.cpp (2451, 2020-06-15)
src\cpu_main_py.cpp (5054, 2020-06-15)
src\cpu_solver.cpp (9277, 2020-06-15)
src\cpu_solver.h (924, 2020-06-15)
src\gpu_solver.cu (21945, 2020-06-15)
src\gpu_solver.cuh (1225, 2020-06-15)
src\main.cu (4857, 2020-06-15)
src\main_py.cu (8238, 2020-06-15)
src\utils.cpp (609, 2020-06-15)
src\utils.h (267, 2020-06-15)
tests (0, 2020-06-15)
tests\tests.py (2598, 2020-06-15)
tests\testsCPU.cpp (1147, 2020-06-15)
tests\testsGPU.cu (0, 2020-06-15)

# CUDA-quartic-solver  [](https://travis-ci.org/qureshizawar/CUDA-quartic-solver) [](https://pypi.org/project/QuarticSolver/) [](https://pepy.tech/project/quarticsolver) A general cubic equation solver and quartic equation minimisation solver written for CPU and Nvidia GPUs, for more details and results, see: https://arxiv.org/abs/1903.10041. The library is available for C++/CUDA as well as Python using Pybind11. ## Running the solver ### Requirements 1) CUDA 9.0+ capable GPU and nvcc required to run the GPU solvers 2) CMake 3.8+ 3) Python 3.6, numpy (if using Python) 4) Pybind11 v2.5.0+ ([installation instructions](https://stackoverflow.com/a/56552686)) ### CMake CMake can be used to build the project, for example: ``` git clone --recursive https://github.com/qureshizawar/CUDA-quartic-solver mkdir build && cd build cmake -D CMAKE_BUILD_TYPE=Release ../ -D CPP_exe=true -D GPU_build=false make ``` When the `CPP_exe` flag is set to `true`, CMake will build a c++ executable, then ```quartic_solver_main``` can be executed to run the example code. When the `GPU_build` flag is set to `true`, CMake will build the CUDA capable version of the library. ### Python The Python package can be installed via PyPI: ``` pip install QuarticSolver ``` Or package can be installed by building its `.whl` file, for example: ``` git clone --recursive https://github.com/qureshizawar/CUDA-quartic-solver python setup.py bdist_wheel --GPU_build True ``` When the `GPU_build` flag is set to `True`, the CUDA capable version of the library will be built. The built `.whl` can then be used to install the package, for example: ``` pip install ./dist/QuarticSolver-0.1.1-cp36-cp36m-linux_x86_***.whl ``` ### Usage Please see `src/main.cu`, `src/cpu_main.cpp`, and the examples in the `/tests` folder for detailed usage examples ##### C++ Given quartic functions of the form `Ax^4 + Bx^3 + Cx^2 + Dx + E` the minimiser for the quartic functions can calculated on the CPU by calling the following function from `cpu_solver.cpp`: ``` QuarticMinimumCPU(N, A, B, C, D, E, min); ``` Where: - `N` is the number of functions - `A` is an array containing the coefficient A for each of the quartic functions - `B` is an array containing the coefficient B for each of the quartic functions - `C` is an array containing the coefficient C for each of the quartic functions - `D` is an array containing the coefficient D for each of the quartic functions - `E` is an array containing the coefficient E for each of the quartic functions - `min` is an array which will contain the corresponding minimums for each of the quartic functions The calculations can be carried out on a GPU by using `QuarticMinimumGPU` or `QuarticMinimumGPUStreams`. Please note signifcant performance improvement is observed when `N>10000` for `QuarticMinimumGPU` vs `QuarticMinimumGPUStreams` ##### Python The Python version of the library can be used as follows: ``` import numpy as np import QuarticSolver minimum = QuarticSolver.QuarticMinimum(A,B,C,D,E,True) ``` Where `A`,`B`,`C`,`D`,`E` are numpy arrays containing the quartic function coefficients and the final arg is a boolean flag which if set to `True` will use the GPU if possible. `minimum` is the returned numpy array containing the corresponding minimisers. ## TODO 1) Add multi-gpu support

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：