This might be a stupid question but I couldn't find a solution anywhere.
When I use gpu to run non-negative decompositions for a random tensor, it is much slower than using a cpu (for various sizes). For reference it takes 0.4 seconds on cpu while it takes more than 10 seconds on gpu to run a single decomposition (size 3x2x2, but the same holds for 100 x 100 x 1000). I have pytorch and cuda 11.1 as well as cudnn on my computer and my gpu is rtx 3070 so it should theoretically beat my cpu?
This might be a stupid question but I couldn't find a solution anywhere.
When I use gpu to run non-negative decompositions for a random tensor, it is much slower than using a cpu (for various sizes). For reference it takes 0.4 seconds on cpu while it takes more than 10 seconds on gpu to run a single decomposition (size 3x2x2, but the same holds for 100 x 100 x 1000). I have pytorch and cuda 11.1 as well as cudnn on my computer and my gpu is rtx 3070 so it should theoretically beat my cpu?