cuvec 1.0.0

Unifying Python/C++/CUDA memory: Python buffered array -> C++11 `std::vector` -> CUDA managed memory

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 cuvec

Usage

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()` data

External 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/dir

At 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

• Contributors