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Matrix multiplication is the only thing GPUs are useful for... Basically, parallel mathematical operations. It's interesting, but the applications to this are
by powertower 16y ago
Matrix multiplication is the only thing GPUs are useful for... Basically, parallel mathematical operations.
It's interesting, but the applications to this are seriously limited and very specific.
Graphic apps already use the GPU. The OS mostly keeps data structures and calls functions.
- michael_h 16y agoThat may be a bit harsh. If your input size is sufficently large, I would bet that encryption would get a pretty big boost. I know convolution operations are a lot faster (not sure where this would happen in a kernel though). I think people often forget to factor in data transfer time. Matrix multiplication is 98x faster on my gpu than on my cpu, but I don't actually break even on real world time until the dimensions are up around 2000 or so.
- woodson 16y agoTrue. A talk I recently attended quoted results of a Gammatone filterbank implementation for audio processing using CUDA (which was properly parallelized) and compared it with IPP (Intel Integrated Performance Primitives) on the CPU, which turned out to be a lot faster due to the cost of transfering the data to the GPU in the CUDA implementation.
- adrianN 16y agoThat's not true. You can do all kinds of things related to polynomials on GPUs with nearly optimal speedup. https://domino.mpi-sb.mpg.de/intranet/ag1/ag1publ.nsf/0/ca00677497561c7ec125763c0044a41a/$FILE/gpgpu_mul.pdf https://domino.mpi-sb.mpg.de/intranet/ag1/ag1publ.nsf/0/ca00...
- powertower 16y ago...which the kernel and OS have no need for. That's the entire point, that there is no usefulness to this outside of very specific applications.