4 ms·
By single precision, do you mean 32-bit floating-point computation? Probably not but, if so, isn't that what both computer gaming and deep learning need the mo
by rhaps0dy 9y ago
By single precision, do you mean 32-bit floating-point computation?
Probably not but, if so, isn't that what both computer gaming and deep learning need the most?
- majewsky 9y agoYes, at least for gaming. (Don't know about DNN.) Single-precision is the only kind that GPUs supported until CUDA happened. Around 2011, I got my feet wet in CUDA and tried to calculate quantum waveforms (using a method that is mostly matrix multiplications and FFTs). I eventually went back to doing stuff on the CPU because GPU memory was too small in the systems that I had access to (256 MB), which restricted me to one job at a time, whereas the CPU (a contemporary i7) had enough cores and memory to do 4 jobs in parallel. And I needed double precision, which the GPU could only execute at a tenth the speed of a single-precision job. Also, with the GPU, I was restricted to running jobs during the night since those systems were desktops that were also used for classes. Whenever one of my calculations ran, it would occupy the GPU completely, this rendering the graphical login unusable. I reckon that the situation would look much more favorably for the GPU today, esp. because of the larger memory sizes and because double-precision speed has caught up. But yeah, the most common uses need only single-precision.
- imbusy111 9y agoGeForce 5xx series came out in 2010 (https://en.wikipedia.org/wiki/GeForce_500_series https://en.wikipedia.org/wiki/GeForce_500_series) and NONE of them had less than 1GB of memory. Idk what GPU you used, but it was old technology at that point.
- majewsky 9y agoProbably. Whoever bought those machines probably didn't realize that GPU performance was quickly becoming a relevant metric for scientific computation.