Data aware jit/blit - drawing 1.25 to 1.45 times faster.
Drawing different types of pixels can be quicker if you know about the image you are drawing, and if you know that drawing parts of the image with specialised blitters is quicker.
A good example is if your image is 25% areas of large blocks of either white or black. Using a specialised fill routine to just draw those big blocks of color is lots quicker. This is because there is usually an optimised, and hardware accelerated fill routine.
See all this whitespace? (all the white parts on your screen) These can be drawn really quickly with ASIC fill hardware rather than a slow GPU running a general purpose image blitter.
Another example is like this Alien image. The edges of the image are transparent, but the middle has no transparency. Since drawing transparent images is slow, using a different drawing routine for the middle part than the edges turns out to be faster.
Here is a proof of concept which draws an image used by pygame zero in 80% of the time it normally takes. That is about 1.25 times quicker.
https://github.com/illume/dataaware
The results vary dramatically depending on the image itself. But the 1.25 times faster is fairly representative of transparent images where the middle part isn't. If it finds sections where the image is a plain colour, that can be 1.42 times faster. Or more. Larger images give you different results as does different hardware. Obviously a platform with a fast path hardware accelerated image fills, or 16 bit image rendering but slow 32bit alpha transparency is going to get a lot bigger speedups with this technique.
Further work is to develop a range of image classifiers for common situations like this, which return custom blitters depending on the image data, and the hardware which it is running on.
(this is one of several techniques I'm working on for drawing things more quickly on slow computers)
A good example is if your image is 25% areas of large blocks of either white or black. Using a specialised fill routine to just draw those big blocks of color is lots quicker. This is because there is usually an optimised, and hardware accelerated fill routine.
See all this whitespace? (all the white parts on your screen) These can be drawn really quickly with ASIC fill hardware rather than a slow GPU running a general purpose image blitter.
Another example is like this Alien image. The edges of the image are transparent, but the middle has no transparency. Since drawing transparent images is slow, using a different drawing routine for the middle part than the edges turns out to be faster.
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| Alien graphic used in Pygame Zero teaching framework documentation. |
Here is a proof of concept which draws an image used by pygame zero in 80% of the time it normally takes. That is about 1.25 times quicker.
https://github.com/illume/dataaware
 |
| Alien sectioned up, drawn with 5 different blitters, each perfect for the section. |
The results vary dramatically depending on the image itself. But the 1.25 times faster is fairly representative of transparent images where the middle part isn't. If it finds sections where the image is a plain colour, that can be 1.42 times faster. Or more. Larger images give you different results as does different hardware. Obviously a platform with a fast path hardware accelerated image fills, or 16 bit image rendering but slow 32bit alpha transparency is going to get a lot bigger speedups with this technique.
Further work is to develop a range of image classifiers for common situations like this, which return custom blitters depending on the image data, and the hardware which it is running on.
(this is one of several techniques I'm working on for drawing things more quickly on slow computers)
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