[far future] CUDA implementation of AMICA #4
Description
AMICA is better than the Infomax algorithm used in CUDAICA; the former is noise-resistant & quasi-nonstationary (can use multiple mixture models with adaptive likelihood across timeframes)... AMICA is currently Intel CPU-only & much slower than CUDAICA
Compile AMICA's fortran source with CUDA's Fortran compiler... sounds simple but it's not
Required Fortran edits:
- Switch all calls to Intel MKL libraries/functions to their CUDA-optimized alternatives (function names are different)
- Add logic for ascertaining available RAM & VRAM, edit existing data chunking logic to account for both (e.g. differentiate GPU-constrained machines)
- Convert all data to single-precision (Jason: 'ok for theaters multiplication') except during precision-critical operations (follow CUDAICA as guide for implementing this)
Alternate Matlab-CUDA implementation:
- See Jason's e-mail with matlab implementation of AMICA
- Add logic for data chunking using gpuArray calls
- Add logic for normalization & integration of weights across data chunks
- Keep as Matlab code or use Matlab CUDA compiler?
Lots of work but keep in back burner