Python wrapper for CULAtools
Project description
PyCULA provides an efficient and simple CUDA GPU environment for python. PyCULA accomplishes this feat by combining the power of driver based PyCUDA with nVidia’s runtime libraries and, most importantly, CULA GPU-LAPACK functionality in a single environment. We aim to hide complications without limiting the enduser.
- PyCULA provides:
Complete python ctypes bindings for CULAtools Premium
Complete suite of sophisticated numpy based wrappers for CULA host_interface routines
Complete suite of sophisticated PyCUDA.gpuarray based wrappers for CULA device_interface routines
- Functions to seamlessly combine the above in a single enviornment with:
PyCUDA and PyCUDA custom kernels
- nVidia Runtime Libraries
cuBLAS (scikits.cuda)
cuFFT (scikits.cuda)
cuSPARSE (coming soon!)
- and without:
Headaches
Memory Leaks
Mountains of C code
- Other reasons to use Python and PyCULA for your GPGPU and scientific computing include:
Develop and Debug Interactively in the Command-Line-Interpreter
Open Source Code
Integrate with scientific libraries including database and web libraries
Prototype algorithms efficiently with SciPy
Integrated Help
GPU device arrays with Numpy like interface (via PyCUDA)
To see what PyCULA looks like, or to see more details, check out our documentation!