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We choose GNU Octave, since it supports single precision which the GK104 we’ll be running performs best on. Is single-precision computations useful in the cont

We choose GNU Octave, since it supports single precision which the GK104 we’ll be running performs best on. Is single-precision computations useful in the context where GNU Octave is generally used?

valarauca1 12y ago
Single vs Double precisions is fine. Most of Matlab calculations are homework or simulations. Generally speaking the real world will add more errors then double floating points will reduce. Honestly GNU Octave has been a surprisingly fast developing product. Matlab only got GPU acceleration 2 years ago (according to my buddy who uses Matlab) (which is very fast for a GNU project). A lot of academics are picking up on it, even professors recommending it. I think the most glowing recommendation I heard was, "Well its free, and free goes along way when you're living on a research stipend."
- wtallis 12y ago
  Double-precision is a good default for a tool like Octave, but I agree that single-precision can be plenty accurate for a lot of uses. It's often just not worth the added time and code complexity to determine where single-precision should be used.
- maaku 12y ago
  Depends very much on the algorithm. Some are sensitive to rounding precision, others are not.
mjcohen 12y ago
Note that GNU Octave, the real thing, is available for Android along with a number of its toolboxes: https://play.google.com/store/search?q=octave&c=apps https://play.google.com/store/search?q=octave&c=apps
- bane 12y ago
  For even more math fun, Maxima is also on Android. https://play.google.com/store/apps/details?id=jp.yhonda&hl=en https://play.google.com/store/apps/details?id=jp.yhonda&hl=e...
foxhill 12y ago
it could very easily run double precision, but then they'd have to use their tesla cards to run it, and at several thousand dollars a piece, wont be quite as attractive to the user..
- gh02t 12y ago
  Uh most recent Nvidia GPUs support double precision, albeit at a speed penalty. And they are using a Tesla card in the article. It says they are using a K10.
  - foxhill 12y ago
    of course they all can do double, but at 1/24th of the speed of single. (1/32th in maxwell) they use a GK104 based tesla, i.e a "dual GPU" GTX680, so it has no real double precision. unless you consider 2x 95GFlops noteworthy.
- wtallis 12y ago
  Consumer GPUs can do double-precision. At the high end, consumer parts usually (though not always) get crippled performance relative to their pro counterparts, but for the low and mid-range chips even the workstation cards have lackluster double-precision performance.
  - foxhill 12y ago
    well, yes, technically correct, but a CPU has much better double precision performance than that. 1/24th (or 1/32th in maxwell) of single precision performance doesn't really sell the usefulness of CUDA.
lightcatcher 12y ago
Single precision is about 2.5x faster than double precision on current GPUs (for matrix multiplication, which is compute dominated). It really depends on the application, but in my experience more often than not the only reason you are using GPUs is because you want to squeeze out every last ounce of performance. In these cases, single precision makes a lot of sense (assuming you're algorithm doesn't depend heavily on 64 bits of precision). Within the neural nets community, single precision is almost always used (at least on GPUs).

valarauca1 12y ago

Single vs Double precisions is fine. Most of Matlab calculations are homework or simulations. Generally speaking the real world will add more errors then double floating points will reduce. Honestly GNU Octave has been a surprisingly fast developing product. Matlab only got GPU acceleration 2 years ago (according to my buddy who uses Matlab) (which is very fast for a GNU project). A lot of academics are picking up on it, even professors recommending it. I think the most glowing recommendation I heard was, "Well its free, and free goes along way when you're living on a research stipend."

wtallis 12y ago
Double-precision is a good default for a tool like Octave, but I agree that single-precision can be plenty accurate for a lot of uses. It's often just not worth the added time and code complexity to determine where single-precision should be used.
maaku 12y ago
Depends very much on the algorithm. Some are sensitive to rounding precision, others are not.

mjcohen 12y ago

Note that GNU Octave, the real thing, is available for Android along with a number of its toolboxes: https://play.google.com/store/search?q=octave&c=apps https://play.google.com/store/search?q=octave&c=apps

bane 12y ago
For even more math fun, Maxima is also on Android. https://play.google.com/store/apps/details?id=jp.yhonda&hl=en https://play.google.com/store/apps/details?id=jp.yhonda&hl=e...

foxhill 12y ago

it could very easily run double precision, but then they'd have to use their tesla cards to run it, and at several thousand dollars a piece, wont be quite as attractive to the user..

gh02t 12y ago
Uh most recent Nvidia GPUs support double precision, albeit at a speed penalty. And they are using a Tesla card in the article. It says they are using a K10.
- foxhill 12y ago
  of course they all can do double, but at 1/24th of the speed of single. (1/32th in maxwell) they use a GK104 based tesla, i.e a "dual GPU" GTX680, so it has no real double precision. unless you consider 2x 95GFlops noteworthy.
wtallis 12y ago
Consumer GPUs can do double-precision. At the high end, consumer parts usually (though not always) get crippled performance relative to their pro counterparts, but for the low and mid-range chips even the workstation cards have lackluster double-precision performance.
- foxhill 12y ago
  well, yes, technically correct, but a CPU has much better double precision performance than that. 1/24th (or 1/32th in maxwell) of single precision performance doesn't really sell the usefulness of CUDA.

lightcatcher 12y ago

Single precision is about 2.5x faster than double precision on current GPUs (for matrix multiplication, which is compute dominated). It really depends on the application, but in my experience more often than not the only reason you are using GPUs is because you want to squeeze out every last ounce of performance. In these cases, single precision makes a lot of sense (assuming you're algorithm doesn't depend heavily on 64 bits of precision). Within the neural nets community, single precision is almost always used (at least on GPUs).