Project based on LiquidFun about liquidfun HOT 3 OPEN

A good starting point for this would be to profile an application (e.g the
Testbed) to determine which areas are most expensive given a particular
simulation. This would enable you to determine where to focus optimization
efforts.

If you're running on Linux boxes consider using
https://perf.wiki.kernel.org/index.php/Main_Page
If you're using OS X you could profile using XCode Instruments
On Windows there is a huge selection VTune, AMD CodeX, Visual Studio
Profiling Tools etc.

Jason (see the git log) - after profiling - spent a lot of time rewriting
the particle simulation in ARM SIMD which yielded significant gains. An
x86_64 version using intrinsics may be reasonable and maintainable. You
could also try plumbing MathFu https://github.com/google/mathfu in to get
SIMD optimization across the library to see whether that yields a gain.

Cheers,
Stewart

On Tue, Oct 6, 2015 at 12:17 PM, Patrick Drumm [email protected]
wrote:

Hello all,

Similarly to Jim Hye (who posted an issue about a year and a half ago), my
team and I found LiquidFun very intriguing and have selected it as the
subject of our final project. Rather than parallelizing the code however,
we are looking for ways to make LiquidFun run faster or more efficiently
relating to data stuctures. Basically we are looking for anywhere in the
program that might slow up a little bit - where we might be able to
implement an alternative, more efficient sorting algorithm, searching
algorithm, or data structure to store things.

We would appreciate any advice as to where in the program these
optimizations might be most useful!

Thanks,
Patrick Drumm

—
Reply to this email directly or view it on GitHub
#57.

from liquidfun.

Valgrind is also a pretty good tool for this (Unix-ish systems):
http://c.learncodethehardway.org/book/ex41.html . Keep in mind though, the
base of Liquid fun is Box2D. So any performance tuning in the base code
could be contributed back to that project. Maybe you're just talking about
the "Liquid" features though.

On Tue, Oct 6, 2015 at 1:13 PM, Stewart Miles [email protected]
wrote:

A good starting point for this would be to profile an application (e.g the
Testbed) to determine which areas are most expensive given a particular
simulation. This would enable you to determine where to focus optimization
efforts.

If you're running on Linux boxes consider using
https://perf.wiki.kernel.org/index.php/Main_Page
If you're using OS X you could profile using XCode Instruments
On Windows there is a huge selection VTune, AMD CodeX, Visual Studio
Profiling Tools etc.

Jason (see the git log) - after profiling - spent a lot of time rewriting
the particle simulation in ARM SIMD which yielded significant gains. An
x86_64 version using intrinsics may be reasonable and maintainable. You
could also try plumbing MathFu https://github.com/google/mathfu in to get
SIMD optimization across the library to see whether that yields a gain.

Cheers,
Stewart

On Tue, Oct 6, 2015 at 12:17 PM, Patrick Drumm [email protected]
wrote:

Hello all,

Similarly to Jim Hye (who posted an issue about a year and a half ago),
my
team and I found LiquidFun very intriguing and have selected it as the
subject of our final project. Rather than parallelizing the code however,
we are looking for ways to make LiquidFun run faster or more efficiently
relating to data stuctures. Basically we are looking for anywhere in the
program that might slow up a little bit - where we might be able to
implement an alternative, more efficient sorting algorithm, searching
algorithm, or data structure to store things.

We would appreciate any advice as to where in the program these
optimizations might be most useful!

Thanks,
Patrick Drumm

—
Reply to this email directly or view it on GitHub
#57.

—
Reply to this email directly or view it on GitHub
#57 (comment).

from liquidfun.

Obviously, profile what you are about to optimize, but if you're after learning how to optimize, might I (not so) humbly suggest my GDC talk about optimization strategies?
https://www.youtube.com/watch?v=Nsf2_Au6KxU

from liquidfun.