Everyone likes examples. So here are three examples using TDSI. The archive includes a ready-to-go FDT project with post-compile ANT tasks configured.
Example01
This is the old code of the already optimized attractor using the Memory API instead of a Vector.<uint>.
Example02
In this case there exists no Particle class at all and no linked list. The particle information is stored inside the memory as well. Particles are extended to a fourth value so indexing a particle can be done with a simple bitshift which is very fast.
Example03
The last example uses float instead of double values for the particles. The framerate stays the same which is really cool because the memory usage drops. Before a particle consisted of four doubles which is a total of 4 * 8b = 32b. In this example each particle takes up only 16b. There the memory difference is 0x4B0000b which is about 4.7mb in total.
And also the first version needs about 20mb on my machine which means about 12mb of RAM are not wasted. Pretty cool when thinking about devices with less memory.
By the way I just stumpled across a bug when using [Embed]. Hopefully it will be easy to fix.
Very interesting work. Wanna digg into alchemy, memory management etc, and this is a really good source to start with, tnx.
The qualified name space for the Memory and __bytecode are specific to TDSI, so if I have my own Memory class it shouldn’t care about it?
Correct.
Cheers Joa.
Keep up the good work ;-)
Hi Joa,
very nice project!
I have a question:
Do you have an idea how much speed improvement one can expect optimizing SHA256 of the mx.util package in flex? This one is terrible slow (2 secs per mb computing a digest …). Can we expect a factor 5 or will it more like a few percent?
thanx,
Arnoud
thanks for the example Joa, great work :).
A short tutorial on how to build a project with TDSI would be very helpful for people like me,
who are not so used to work with ant for example.
Thanks,
Michael.