3 ms·

You can probably figure out why 3x3 or smaller convolutions would be faster without much thought: hint FP performance is improving much faster than either inter

by varelse 12y ago

You can probably figure out why 3x3 or smaller convolutions would be faster without much thought: hint FP performance is improving much faster than either internal bandwidth or CUDA kernel launch latency. And IMO this opens up the window for future GPUs to do even better. That said, when 9 TFLOPs is ~$1,100 (2 x GTX 980), I'm not too worried about memory usage in the long term.

jimduk 12y ago
Neophyte question - is FP necessary for a CNN as opposed to using 32 bits on some fixed encoding. If I used a 32 bit value on the order of +- 4.27 or +- 8.23, wouldn't that have enough accuracy. I'm assuming the weights and parameters don't go much above say 8 or 16 after all the Relu stuff.
- benanne 12y ago
  Probably not. The noise in the training data itself, noise from dropout and various other sources are going to trump any quantization noise anyway, so you can use fairly inaccurate representations. This is why everyone's using gamer GPUs for this stuff, and not Tesla cards: single precision is enough, and much cheaper to come by. Although, I guess the cutoff might have to be a bit higher than 8.23. Maybe the neuron activations would never exceed that range, but some intermediate computations could. Supposedly the new Maxwell GPUs have some new instructions for working with half-precision floats ( https://developer.nvidia.com/sites/default/files/akamai/opengl/specs/GL_NV_shader_atomic_fp16_vector.txt https://developer.nvidia.com/sites/default/files/akamai/open... ). I wonder how complete this implementation is, because half-precision might be sufficient to train a convnet, and this would result in a significant speedup.
  - smhx 12y ago
    In these deep relu networks which are not renormalized in between, (like overfeat which has no normalization), some of the activations become pretty big in size! (in the order of 1e3). You also cant clip them and get away with it, you have to either renormalize the layers to do half-precision (and live with the extra cost) or stick to full-precision. I was doing fun stuff early this year that did fixed-precision nets (8-bit/16-bit). Things get very interesting :)
    - varelse 12y ago
      16-bit float has dynamic range from + or - 6.5e04 to 6.1e-05. http://en.wikipedia.org/wiki/Half-precision_floating-point_format http://en.wikipedia.org/wiki/Half-precision_floating-point_f... That's plenty IMO for most inputs and weights. Where it gets tricky is in accumulation. You could constrain the weights for each unit I guess, but this is the sort of work best done under the hood rather than by the data scientist IMO. I'd personally choose 32-bit accumulation just because it would drastically simplify code development. I've also worked with fixed precision elsewhere. It's awesome if you understand the dynamic range of your application. It's a migraine headache if you don't.
fiatmoney 12y ago
Be careful; most of NVidia's consumer cards only support single-precision FP at anything close to "full" speed - the 980 I believe runs at 1/32 speed.
- varelse 12y ago
  Say what? You can get insane single-precision efficiency out of GTX 980. Double-precision OTOH is a disaster (1/150 yes 1/150 as opposed to GK104's 1/30 or so). But for most machine learning, double-precision is overkill. IMO What remains to be seen is what one could do with FP16 data mixed with FP32 accumulation. My signal processing friends think a pure FP16 network is just asking for trouble and are of the opinion that a minimum of 20 bits is needed for stable accumulation and 24 is more than enough. That's not far off from Teradeep's 8-bit data plus 16-bit accumulators for image data.

by varelse 12y ago

jimduk 12y ago

Neophyte question - is FP necessary for a CNN as opposed to using 32 bits on some fixed encoding. If I used a 32 bit value on the order of +- 4.27 or +- 8.23, wouldn't that have enough accuracy. I'm assuming the weights and parameters don't go much above say 8 or 16 after all the Relu stuff.

benanne 12y ago
Probably not. The noise in the training data itself, noise from dropout and various other sources are going to trump any quantization noise anyway, so you can use fairly inaccurate representations. This is why everyone's using gamer GPUs for this stuff, and not Tesla cards: single precision is enough, and much cheaper to come by. Although, I guess the cutoff might have to be a bit higher than 8.23. Maybe the neuron activations would never exceed that range, but some intermediate computations could. Supposedly the new Maxwell GPUs have some new instructions for working with half-precision floats ( https://developer.nvidia.com/sites/default/files/akamai/opengl/specs/GL_NV_shader_atomic_fp16_vector.txt https://developer.nvidia.com/sites/default/files/akamai/open... ). I wonder how complete this implementation is, because half-precision might be sufficient to train a convnet, and this would result in a significant speedup.
- smhx 12y ago
  In these deep relu networks which are not renormalized in between, (like overfeat which has no normalization), some of the activations become pretty big in size! (in the order of 1e3). You also cant clip them and get away with it, you have to either renormalize the layers to do half-precision (and live with the extra cost) or stick to full-precision. I was doing fun stuff early this year that did fixed-precision nets (8-bit/16-bit). Things get very interesting :)
  - varelse 12y ago
    16-bit float has dynamic range from + or - 6.5e04 to 6.1e-05. http://en.wikipedia.org/wiki/Half-precision_floating-point_format http://en.wikipedia.org/wiki/Half-precision_floating-point_f... That's plenty IMO for most inputs and weights. Where it gets tricky is in accumulation. You could constrain the weights for each unit I guess, but this is the sort of work best done under the hood rather than by the data scientist IMO. I'd personally choose 32-bit accumulation just because it would drastically simplify code development. I've also worked with fixed precision elsewhere. It's awesome if you understand the dynamic range of your application. It's a migraine headache if you don't.

fiatmoney 12y ago

Be careful; most of NVidia's consumer cards only support single-precision FP at anything close to "full" speed - the 980 I believe runs at 1/32 speed.

varelse 12y ago
Say what? You can get insane single-precision efficiency out of GTX 980. Double-precision OTOH is a disaster (1/150 yes 1/150 as opposed to GK104's 1/30 or so). But for most machine learning, double-precision is overkill. IMO What remains to be seen is what one could do with FP16 data mixed with FP32 accumulation. My signal processing friends think a pure FP16 network is just asking for trouble and are of the opinion that a minimum of 20 bits is needed for stable accumulation and 24 is more than enough. That's not far off from Teradeep's 8-bit data plus 16-bit accumulators for image data.