Unifying Python/C++/CUDA memory: Python buffered array -> C++11 std::vector -> CUDA managed memory.
Why
Other libraries which expose functionality to convert/pass data formats between these different language spaces tend to be bloated, unnecessarily complex, and relatively unmaintainable. By comparison, cuvec uses the latest functionality of Python, C++, and CUDA to keep its code (and yours) as succinct as possible. “Native” containers are exposed so your code follows the conventions of your language. Want something which works like a numpy.ndarray? Not a problem. Want to convert it to a std::vector? Or perhaps a raw float * to use in a CUDA kernel? Trivial.
Non objectives
Anything to do with mathematical functionality. Even something as simple as setting element values is left to the user and/or pre-existing features - simply use numpy.ndarray.fill() (Python/Numpy), std::fill() (C++), or memset() (C/CUDA).
Install
Requirements:
Python 3.6 or greater (e.g. via Anaconda or Miniconda)
CUDA SDK/Toolkit (including drivers for an NVIDIA GPU)
pip install cuvecUsage
Creating
Python
import cuvec
vec = cuvec.vector((1337, 42), "float32")CPython API
#include "Python.h"
#include "pycuvec.cuh" // requires nvcc
PyObject *obj = (PyObject *)PyCuVec_zeros<float>({1337, 42});
// don't forget to Py_INCREF(obj) if returning it.
/// N.B.: convenience functions provided by "pycuvec.cuh":
// PyCuVec<T> *PyCuVec_zeros(std::vector<Py_ssize_t> shape);
// PyCuVec<T> *PyCuVec_zeros_like(PyCuVec<T> *other);
// PyCuVec<T> *PyCuVec_deepcopy(PyCuVec<T> *other);C++/CUDA
#include "cuvec.cuh" // requires nvcc
CuVec<float> vec(1337 * 42); // like std::vector<float>Converting
The following involve no memory copies.
CPython API to C++
/// input: `PyObject *obj` (obtained from e.g.: `PyArg_ParseTuple()`, etc)
/// output: `CuVec<type> vec`
CuVec<float> &vec = ((PyCuVec<float> *)obj)->vec; // like std::vector<float>
std::vector<Py_ssize_t> &shape = ((PyCuVec<float> *)obj)->shape;C++ to C/CUDA
/// input: `CuVec<type> vec`
/// output: `type *arr`
float *arr = vec->data(); // pointer to `cudaMallocManaged()` dataExternal CMake Projects
The raw C++/CUDA libraries may be included in external projects using cmake. Simply build the project and use find_package(AMYPADcuvec).
# print installation directory (after `pip install cuvec`)...
python -c "import cuvec; print(cuvec.cmake_prefix)"
# ... or build & install directly with cmake
mkdir build && cd build
cmake ../cuvec && cmake --build . && cmake --install . --prefix /my/install/dirAt this point any external project may include cuvec as follows (Once setting -DCMAKE_PREFIX_DIR=<installation prefix from above>):
cmake_minimum_required(VERSION 3.3 FATAL_ERROR)
project(myproj)
find_package(AMYPADcuvec COMPONENTS cuvec REQUIRED)
add_executable(myexe ...)
target_link_libraries(myexe PRIVATE AMYPAD::cuvec)Licence
Copyright 2021
Casper O. da Costa-Luis @ University College London/King’s College London
1 maintainer
Release files for cuvec 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cuvec-1.0.0.tar.gz | 22.0 kB | Details |
Release files / cuvec-1.0.0.tar.gz
| Download URL | cuvec-1.0.0.tar.gz |
|---|---|
| Size | 22.0 kB |
| Tags | Source |
|
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