i wish the vray would run at gpu stream processor and will be a lot faster than on cpu
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We are, of course, thinking in that direction. However the GPU technology is quite unstable yet, with completely new hardware coming out each year, which makes any long-term plas quite difficult to make. Hopefully the GPU technology will settle on a standard sometime soon, so that development can be more predictable.
Best regards,
VladoI only act like I know everything, Rogers.
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Originally posted by cubiclegangsterSo if you coded vray to work on an 8800, would it not work for many other new cards after that?
Best regards,
VladoI only act like I know everything, Rogers.
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Excellent technology, potentially a revolution, but thanks to one little oversight in creating a standard it becomes completley and utterly useless.
More curiosity - would you have to re-write the way that vray does its calculations or is it just updating/optimising all the code to use the right parts of the hardware?
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It's a MESS
I was reading some page (watch it, it's a pdf...) on mathematica accelerators: dedicated, parallel processing units that come on a pci-E card, from http://www.clearspeed.com .
The principle is VERY close to that touted by NVidia (the article on CUDA had a link to the "similar" clearspeed boards).
FIGURE 6: STANDARD C
Code:#include <stdio.h> #include <math.h> #define PI 3.1415926535897932384f #define SAMPLES 96 int main() { double sine, angle; int i; for (i = 0; i < SAMPLES; i++) { // convert to an angle in range 0 to Pi angle = i * PI / SAMPLES; // calculate sine of angle sine = sin(angle); } }
FIGURE 7: PARALLEL C
Code:#include <lib_ext.h> #define PI 3.1415926535897932384f #define SAMPLES 96 int main() { poly double sine, angle; poly int i; // get PE number: 0...n-1 i = get_penum(); // convert to an angle in range 0 to Pi angle = i * PI / SAMPLES; // calculate all sine values simultaneously sine = sinp(angle); }
I don't know, lol.
Lele
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