cuda4py 1.3.6

CUDA cffi bindings and helper classes

cuda4py
=========

Python cffi CUDA bindings and helper classes.

Tested with Python 2.7, Python 3.4 and PyPy on Linux with CUDA 7.5.

To compile kernel code written in C++, nvcc should be in PATH and
exported functions should be marked as extern "C"
(for Windows, cl.exe should be in PATH also).
Functions in plain PTX can be used without nvcc.

To use CUBLAS, libcublas.so (cublas64_65.dll) should be present.
To use CUDNN, libcudnn.so (cudnn64_65.dll) should be present.
To use CUFFT, libcufft.so (cufft64_65.dll) should be present.

Not all CUDA api is currently covered.

To install the module run:
```bash
python setup.py install
```
or just copy src/cuda4py to any place where python
interpreter will be able to find it.

To run the tests, execute:

for Python 2.7:
```bash
PYTHONPATH=src nosetests -w tests
```

for Python 3.4:
```bash
PYTHONPATH=src nosetests3 -w tests
```

for PyPy:
```bash
PYTHONPATH=src pypy -m nose -w tests
```

Example usage:

```python
import cuda4py as cu
import logging
import numpy


if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)
ctx = cu.Devices().create_some_context()
module = cu.Module(
ctx, source=
"""
extern "C"
__global__ void test(const float *a, const float *b,
float *c, const float k) {
size_t i = blockDim.x * blockIdx.x + threadIdx.x;
c[i] = (a[i] + b[i]) * k;
}
""")
test = cu.Function(module, "test")
a = numpy.arange(1000000, dtype=numpy.float32)
b = numpy.arange(1000000, dtype=numpy.float32)
c = numpy.empty(1000000, dtype=numpy.float32)
k = numpy.array([0.5], dtype=numpy.float32)
a_buf = cu.MemAlloc(ctx, a)
b_buf = cu.MemAlloc(ctx, b)
c_buf = cu.MemAlloc(ctx, c)
test.set_args(a_buf, b_buf, c_buf, k)
test((a.size, 1, 1))
c_buf.to_host(c)
max_diff = numpy.fabs(c - (a + b) * k[0]).max()
logging.info("max_diff = %.6f", max_diff)
```

Released under Simplified BSD License.
Copyright (c) 2014, Samsung Electronics Co.,Ltd.