scikit-cuda is a Python package that provides interfaces to popular GPU-powered libraries like CUBLAS, CUFFT and CUSOLVER. It exposes both low-level wrappers of C functions using ctypes as well as high-level functions with an interface similar to SciPy to make GPU capabilities more accessible to Python programmers. The package originated from using CULA's GPU SVD routines to accelerate time series decoding algorithms in Python. Future goals include improving wrappers, adding support for MAGMA, and enabling library backend selection.

1. scikit-cuda
A Python Interface to GPU-Powered Libraries
Lev Givon
https://www.columbia.edu/~lev
Bionet Group
Department of Electrical Engineering
Columbia University
September 24, 2015
Lev Givon scikit-cuda

2. Motivation
NVIDIA CUDA contains a (growing) range of great GPU-based
libraries (CUBLAS, CUFFT, CURAND, CUSPARSE, CUSOLVER,
NPP) in addition to the core driver and runtime libraries.
Other libraries (both commercial and free) also available:
CULA - http://www.culatools.com/
MAGMA - http://icl.cs.utk.edu/magma/
PyCUDA (http://mathema.tician.de/software/pycuda/)
enables Python programmers to take advantage of CUDA, but
provides few interfaces to the above libraries.
Lev Givon scikit-cuda

3. Package Description
Exposes contents of various GPU-based libraries via both low-level
and high-level Python functions.
Project origin: using CULA’s GPU-based SVD routines to accelerate
time decoding algorithms implemented in Python. 1
Low-level functions
wrap C functions using ctypes
(https://docs.python.org/2/library/ctypes.html);
replicate C function signatures (with a few slight differences);
catch errors and map error codes to Python exceptions.
High-level functions
provide a similar interface to functions in scipy (http://scipy.org);
use PyCUDA’s GPUArray class to encapsulate matrices/vectors in
GPU memory.
1http://www.bionet.ee.columbia.edu/publications
Lev Givon scikit-cuda

4. Example
1 import pycuda.gpuarray as gpuarray
2 import pycuda.autoinit
3 import numpy as np
4 import skcuda.linalg as linalg
5
6 linalg.init()
7 a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
8 a = np.asarray(a, np.complex64)
9 a_gpu = gpuarray.to_gpu(a)
10 u_gpu, s_gpu, vh_gpu = linalg.svd(a_gpu, ’S’, ’S’)
11 np.allclose(a, np.dot(u_gpu.get(), np.dot(np.diag(s_gpu.get()),
12 vh_gpu.get())), 1e-4)
Lev Givon scikit-cuda

5. Available High-Level Functions
FFT/IFFT
Numerical integration (trapezoidal 1D and 2D)
Linear algebra (dot, eig, qr, svd, transpose, etc.)
Randomized linear algebra (rdmd, rsvd)
Special element-wise functions (expi, sici, etc.)
Numpy-like helper routines not provided by PyCUDA (cumsum,
zeros, etc.)
Lev Givon scikit-cuda

6. Areas for Future Improvement
Improve maintainability and coverage of exposed functions in
supported libraries by automatically generating ctypes wrappers by
parsing C header files.
Alternately, use cffi (https://bitbucket.org/cffi/cffi) to
generate wrappers.
Provide full support for functions in MAGMA (possibly combined
with a Python-based MAGMA library installer) to enable folks
without access to CULA to run GPU-based LPAPACK.
Enable selection of library backend for functions that are provided by
more than one library (e.g., CULA, MAGMA).
Less naive automated block/grid selection mechanism for functions
that require it.
Lev Givon scikit-cuda

7. Resources
Code: https://github.com/lebedov/scikit-cuda
Docs: https://scikit-cuda.rtfd.org
Thanks to our long (and growing) list of contributors!
http://scikit-cuda.readthedocs.org/en/latest/authors.html
Lev Givon scikit-cuda