ArrayFire is a high performance library for parallel computing wih an easy-to-use API. This project provides Python bindings for the ArrayFire library. It enables the users to write scientific computing code that is portable across CUDA, OpenCL and CPU devices.
import arrayfire as af
# Display backend information
af.info()
# Generate a uniform random array with a size of 5 elements
a = af.randu(5, 1)
# Print a and its minimum value
af.print_array(a)
# Print min and max values of a
print("Minimum, Maximum: ", af.min(a), af.max(a))
On an AMD GPU:
Using opencl backend
ArrayFire v3.0.1 (OpenCL, 64-bit Linux, build 17db1c9)
[0] AMD : Spectre
-1- AMD : AMD A10-7850K Radeon R7, 12 Compute Cores 4C+8G
[5 1 1 1]
0.4107
0.8224
0.9518
0.1794
0.4198
Minimum, Maximum: 0.17936542630195618 0.9517996311187744
On an NVIDIA GPU:
Using cuda backend
ArrayFire v3.0.0 (CUDA, 64-bit Linux, build 86426db)
Platform: CUDA Toolkit 7, Driver: 346.46
[0] Tesla K40c, 12288 MB, CUDA Compute 3.5
-1- GeForce GTX 750, 1024 MB, CUDA Compute 5.0
Generate a random matrix a:
[5 1 1 1]
0.7402
0.9210
0.0390
0.9690
0.9251
Minimum, Maximum: 0.039020489901304245 0.9689629077911377
Fallback to CPU when CUDA and OpenCL are not availabe:
Using cpu backend
ArrayFire v3.0.0 (CPU, 64-bit Linux, build 86426db)
Generate a random matrix a:
[5 1 1 1]
0.0000
0.1315
0.7556
0.4587
0.5328
Minimum, Maximum: 7.825903594493866e-06 0.7556053400039673
The backend selection is automated currently. Choosing a particular backend will be made available in the future.
Currently, this project is tested only on Linux and OSX. You also need to have the ArrayFire C/C++ library installed on your machine. You can get it from the following sources.
Please check the following links for dependencies.
If you have not installed the ArrayFire library in your system paths, please make sure the following environment variables are exported.
On Linux
export LD_LIBRARY_PATH=/path/to/arrayfire/lib:$LD_LIBRARY_PATH
On OSX
export DYLD_LIBRARY_PATH=/path/to/arrayfire/lib:$DYLD_LIBRARY_PATH
On both systems, to run the example, you will need to add the python bindings to your PYTHONPATH
export PYTHONPATH=/path/to/arrayfire_python/:$PYTHONPATH
You are now good to go!
This is a work in progress and is not intended for production use.
The ArrayFire library is written by developers at ArrayFire LLC with contributions from several individuals.
The developers at ArrayFire LLC have received partial financial support from several grants and institutions. Those that wish to receive public acknowledgement are listed below:
This material is based upon work supported by the DARPA SBIR Program Office under Contract Numbers W31P4Q-14-C-0012 and W31P4Q-15-C-0008. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DARPA SBIR Program Office.